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t 5.4pt; vertical-align:top">Edwin Alfredo Riofrio N=C3= =BA=C3=B1ez</td><td style=3D"width:10.5pt; padding:0pt 5.4pt; ve= rtical-align:top"><img src=3D"data:image/png;base64,= iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFl= zAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0= ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnu= U1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUIT= u/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1P3nOCN= IFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihyhxbT+K= 2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW0rxDF6rUUvddaJfdwM9HwqYFSa0GO1Srpf= ucUVUXEZCQcqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx65fWTOy7l0exjKgGrGcjTo2/oKS9EUZ2aP= cf/Oo1iTI9UGqFJrzDF5+Sv+Wvg+y+IEVXaGibsbOO5hGBSkbUUQdRT9zaQuBhE6Ct2MT7/D9OI= IVnw6Ct3csfk52oNtTMwcBbE0YteeC7we4LinSI9Z03dBNUVxTdu1eI7FcBIjHvPVU1Tjv7nn5s= 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ign:top"> </spa= n></td><td colspan=3D"4" style=3D"width:370.7pt; padding:0pt 5.4pt; ver= tical-align:top">Un= iversidad Bolivariana del Ecuador (UBE), Dur=C3=A1n, Ecuador.Maestr=C3=ADa en Gesti= =C3=B3n y An=C3=A1lisis de Datos con menci=C3=B3n en Inteligencia de Negoci= os <a href=3D"mailto:eariofrion= @ube.edu.ec" style=3D"text-decoration:none">eario= frion@ube.edu.ec</a> </td></tr><tr><td style=3D"width:3.25pt; paddin= g:0pt 5.4pt; vertical-align:top">2</td><td style=3D"width:171.95pt; padding:0pt 5.4= pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Iv=C3=A1n Patricio Montaleza Qu= izhpe</td><td style=3D"width:10.5pt; padding:0pt 5.4pt; vertical= -align:top"><img src=3D"data:image/png;base64,iVBORw= 0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOx= AAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGg= A1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f= 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padding:0pt 5.4pt; vertical-ali= gn:top">3</p= ></td><td style=3D"width:171.95pt; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-left:7.1pt; margin-bottom:0pt; text-indent:-7.1pt; text-a= lign:justify; line-height:115%; font-size:10pt">Glen Freddy Robayo Cabrera</td><td style=3D"w= idth:10.5pt; padding:0pt 5.4pt; vertical-align:top">= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNi= R0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUU= hr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ou= iGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO= 5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x= 8yXB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXN= kShKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW= 0rxDF6rUUvddaJfdwM9HwqYFSa0GO1SrpfucUVUXEZCQcqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx= 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style=3D"text-decoration:none">https://orcid.org/0009-0006-9195-7423</a></td><td sty= le=3D"padding:0pt; vertical-align:top"></td></tr><tr><td style=3D"width:3.2= 5pt; padding:0pt 5.4pt; vertical-align:top"> </td><td colspan=3D"4" style=3D"wi= dth:370.7pt; padding:0pt 5.4pt; vertical-align:top">Universidad Bolivariana del Ecuador (UBE= ), Dur=C3=A1n, Ecuador.gfrobayoc@ube.edu= .ec </td></tr><tr style=3D"height:0pt"><td style=3D"= width:14.05pt"></td><td style=3D"width:182.75pt"></td><td style=3D"width:21= .3pt"></td><td style=3D"width:177.15pt"></td><td style=3D"width:0.3pt"></td= ></tr></table>&= #xa0;<table style=3D"width:436.35pt; margin-left:0.25pt; margin-= bottom:0pt; padding:0pt; border-collapse:collapse"><tr><td colspan=3D"3" st= yle=3D"width:156.4pt; padding:0pt 5.4pt; vertical-align:top"><a id= =3D"_Hlk92186133"> </a>= </td><td col= span=3D"2" style=3D"width:258.35pt; border-top:0.75pt solid #000000; paddin= g:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de Investigaci=C3=B3n Cient=C3= =ADfica y Tecnol=C3=B3gicaEnviado: <span= style=3D"font-family:'Times New Roman'; background-color:#ffff00">Revisado= : Aceptado: Publicado:= DOI: </td></tr><tr style= =3D"height:6.55pt"><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.= 4pt; vertical-align:top"> </= p> </td><td colspan=3D"2= " style=3D"width:258.35pt; border-bottom:0.75pt solid #000000; padding:0pt = 5.4pt; vertical-align:top"> </td></tr><tr><td style=3D"width:37= .8pt; padding:0pt 5.4pt; vertical-align:top"> <spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">C=C3=ADtese:</s= pan> </td><td styl= e=3D"width:1.55pt; padding:0pt 5.4pt; vertical-align:top"> <= /td><td colspan=3D"2" style=3D"width:358.55pt; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top"> DATOS REVISTADATOS REVISTADATOS REVISTA</td><td style= =3D"border-top:0.75pt solid #000000; padding:0pt; vertical-align:top"></td>= </tr><tr style=3D"height:0pt"><td style=3D"width:48.6pt"></td><td style=3D"= 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x48ePDgwYOH/yo8wY0HDx48ePDg4b8KT3DjwYMHDx48ePiv4v8DkrKy3KIORuIAAAAASUVORK5C= YII=3D" width=3D"567" height=3D"140" alt=3D"" /> <table style=3D"width:440.5pt; margin-le= ft:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr st= yle=3D"height:67.9pt"><td style=3D"width:75.55pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">Palabras claves: Avicultura de precisi=C3= =B3n, factor de conversi=C3=B3n alimenticia, regresi=C3=B3n lineal m=C3=BAl= tiple, eficiencia alimenticia, IoT, pollos de engorde, condiciones tropicales, granjas automatizadas.</= span></td><td colspan=3D"2" style=3D"width:4.4pt; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical= -align:top"> </td><td style= =3D"width:328.1pt; border-top:0.75pt solid #000000; border-bottom:0.75pt so= lid #000000; padding:0pt 5.4pt; vertical-align:top">Resumen Intr= oducci=C3=B3n: la indu= stria av=C3=ADcola opera en un entorno altamente competitivo, donde el Fact= or de Conversi=C3=B3n Alimenticia (FCR) representa entre el 60 y el 70% de = los costos operativos. Aunque las granjas automatizadas generan grandes vol= =C3=BAmenes de datos ambientales mediante sistemas IoT, su potencial suele = aprovecharse de manera limitada en la toma de decisiones. Objetivos:<spa= n style=3D"font-family:'Times New Roman'"> desarrollar un modelo predictivo= basado en regresi=C3=B3n lineal m=C3=BAltiple para estimar el FCR semanal = en granjas av=C3=ADcolas automatizadas bajo condiciones tropicales h=C3=BAm= edas del Ecuador. Metodolog=C3=ADa: estudio observacional longitudinal retrospectivo con 936 registro= s semanales (2022-2025) de siete granjas automatizadas. Se aplic=C3=B3 regr= esi=C3=B3n lineal m=C3=BAltiple con validaci=C3=B3n hold-out 80/20 estratif= icada, verificaci=C3=B3n de supuestos estad=C3=ADsticos y evaluaci=C3=B3n m= ediante R=C2=B2, MAE y RMSE. Resultados: el modelo demostr=C3=B3 capacidad predictiva robusta con R= =C2=B2 =3D 0,774 (entrenamiento) y R=C2=B2 =3D 0,739 (validaci=C3=B3n exter= na), acompa=C3=B1ado de MAE =3D 0,088 y RMSE =3D 0,109. Variables significa= tivas (p < 0,001) incluyeron temperatura ambiental (coeficiente =3D -0,0= 445 FCR/=C2=B0C), humedad relativa (+0,00858 FCR/%), fase fisiol=C3=B3gica = (+0,198 FCR para semanas 5-7) y l=C3=ADnea gen=C3=A9tica (+0,072 FCR para R= OSS 308). CO=E2=82=82 y NH=E2=82=83 no alcanzaron significancia estad=C3=AD= stica. Conclusi=C3=B3n: el= modelo evidencia capacidad predictiva operativa para gesti=C3=B3n preventi= va del FCR en condiciones tropicales h=C3=BAmedos, ofreciendo una herramien= ta interpretable de implementaci=C3=B3n inmediata que permite proyecciones = semanales confiables con implicaciones directas en rentabilidad mediante op= timizaci=C3=B3n de eficiencia alimenticia. =C3=81rea de estudio general: Ingenier=C3=ADa Agropecuaria. =C3=81rea de estudio = espec=C3=ADfica: Avicu= ltura de precisi=C3=B3n. Tipo de estudio: Art=C3=ADculo original.</td></tr><tr style=3D"he= ight:168.2pt"><td colspan=3D"2" style=3D"width:78.85pt; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:top">Keywords: Precision poultry, Feed Conversion Ratio, multiple linear regression, </p= >feed efficiency, IoT, </= span>broiler chickens= , tropical conditions, automated farms.</td><td style=3D"width:1= .15pt; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">= </td><td style=3D"width:328.1pt; border-top:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Abstract Introduct= ion: the poultry indus= try operates in a highly competitive environment, where feed costs represen= t between 60% and 70% of operating costs. Although automated farms generate= large volumes of environmental data through IoT systems, their potential i= s often underutilized in decision-making. Objectives: to develop a predictive model based on multiple= linear regression to estimate the weekly FCR in automated poultry farms un= der humid tropical conditions of Ecuador. Methodology: a retrospective longitudinal observational stu= dy was conducted with 936 weekly records (2022-2025) from seven automated f= arms. Multiple linear regression was applied with stratified hold-out valid= ation (80/20), assessment of statistical assumptions, and evaluation using = R=C2=B2, MAE, and RMSE. Results: the model demonstrated robust predictive capacity with R=C2=B2 =3D 0= .774 (training) and R=C2=B2 =3D 0.739 (external validation), accompanied by= MAE =3D 0.088 and RMSE =3D 0.109. Significant variables (p < 0.001) inc= luded ambient temperature (=CE=B2 =3D -0.0445 FCR units/=C2=B0C), relative = humidity (=CE=B2 =3D +0.00858 FCR units/%), physiological phase (=CE=B2 =3D= +0.198 FCR units for weeks 5-7), and genetic line (=CE=B2 =3D +0.072 FCR u= nits for ROSS 308). CO=E2=82=82 and NH=E2=82=83 did not reach statistical s= ignificance. Conclusion: t= he model demonstrates predictive capacity for preventive FCR management und= er humid tropical conditions, offering an interpretable tool for immediate = implementation that enables reliable weekly projections with direct implica= tions for profitability through optimization of feed efficiency. <sp= an style=3D"font-family:'Times New Roman'; font-weight:bold">General study = area: Agricultural Eng= ineering. Specific study area: Precision Poultry Farming. Type of study: Original article.</td></tr><tr style=3D"hei= ght:0pt"><td style=3D"width:86.35pt"></td><td style=3D"width:3.3pt"></td><t= d style=3D"width:11.95pt"></td><td style=3D"width:338.9pt"></td></tr></tabl= e><span= style=3D"font-weight:bold; color:#a5a5a5"> <div style=3D"c= lear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYA= AACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTE= BAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" heig= ht=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; positi= on:absolute" />&#x= a0; = Nombre = 1</p= > </div></div> <div class=3D"Section_2"><div style= =3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBD= AAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExc= iJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh= 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la competencia global y la necesidad de mejorar continuamente la eficien= cia productiva en un entorno de m=C3=A1rgenes estrechos (Oke<spa= n style=3D"font-family:'Times New Roman'"> et al., 2024). La maximizaci=C3= =B3n de la eficiencia biol=C3=B3gica y econ=C3=B3mica de cada lote de pollo= s de engorde se convierte en un imperativo estrat=C3=A9gico para la supervi= vencia empresarial.<a id=3D"_Hlk213271731">El Factor de Conversi=C3=B3n Alimenticia (FCR<s= pan style=3D"font-family:'Times New Roman'">) es uno de los indicadores m= =C3=A1s importantes para evaluar el rendimiento productivo en sistemas inte= nsivos. Entendido como la proporci=C3=B3n entre el alimento ingerido (kg) y= el peso vivo ganado (kg), el FCR es un indicador que resume muchos factore= s de la producci=C3=B3n. Su significancia econ=C3=B3mica va m=C3=A1s all=C3= =A1 de la eficiencia, ya que el alimento balanceado siempre oscila entre el= 60% y 70% de los costos operativos totales (Muyulema et al., 2020), siendo el principal rubr= o que conforma la estructura de costos en granjas av=C3=ADcolas. </a= ><spa= n style=3D"font-family:'Times New Roman'">El FCR cobra mayor importancia ya= que peque=C3=B1os cambios en =C3=A9ste se reflejan en grandes cambios econ= =C3=B3micos a nivel comercial. Una variaci=C3=B3n de 0.1 puntos en el FCR e= n una granja que procesa 1 mill=C3=B3n de aves al a=C3=B1o puede significar= miles de d=C3=B3lares en costos de alimento, impactando directamente en la= rentabilidad (Quintana-Ospina et al., 2023). Esta sensibilidad econ=C3=B3mica hace relevante= el desarrollo de instrumentos capaces de anticipar y controlar este indica= dor antes de que las desviaciones se consoliden y generen efectos irreversi= bles. Al mismo tiempo la av= icultura mundial se transform=C3=B3 tecnol=C3=B3gicamente en la =C3=BAltima= d=C3=A9cada, con automatizaci=C3=B3n completa de procesos, control ambient= al computarizado y uso generalizado de sistemas de sensorizaci=C3=B3n en ti= empo real. </spa= n><sp= an style=3D"font-family:'Times New Roman'">Esta revoluci=C3=B3n tecnol=C3= =B3gica, enmarcada en la Industria 4.0 y la agricultura de precisi=C3=B3n, = posibilito la captura continua de grandes cantidades de datos operativos, c= omo variables ambientales (temperatura, humedad relativa, CO=E2=82=82, NH= =E2=82=83), datos de consumo de alimento, mortalidad y diversos indicadores= de desempe=C3=B1o (Jackman et al., 2020; Pereira et al., 2020; Ahmed et al., 2024). Estas infraestructuras IoT generan grandes vol=C3=BAmenes de datos que = potencialmente pueden predecir desviaciones productivas antes de que se ref= lejen en indicadores zoot=C3=A9cnicos finales, como el peso corporal o la m= ortalidad acumulada (Liu et al., 2024; Elwakeel, 2025). La habilidad de identificar tempranamente tendencias ne= gativas permitir=C3=ADa realizar medidas correctivas tempranas, minimizando= p=C3=A9rdidas econ=C3=B3micas y mejorando el bienestar animal con medidas = preventivas. Pero todav=C3= =ADa existe una gran paradoja: en muchas granjas comerciales estos datos se= quedan en registros operativos est=C3=A1ticos y bases de datos infrautiliz= adas, sin convertirse en herramientas anal=C3=ADticas vivas que gu=C3=ADen = las decisiones diarias de gesti=C3=B3n (Astill et al., 2020). Esta diferencia entre lo que t= =C3=A9cnicamente se puede capturar y lo que realmente se aprovecha es una g= ran oportunidad perdida de optimizaci=C3=B3n productiva, m=C3=A1s a=C3=BAn = cuando est=C3=A1 demostrado el efecto de variables ambientales sobre indica= dores como el FCR. En parti= cular, las variables ambientales controladas en galpones automatizados infl= uyen directamente en par=C3=A1metros fisiol=C3=B3gicos que impactan en el d= esempe=C3=B1o zoot=C3=A9cnico. Las temperaturas ambientales por encima de l= a zona de termoneutralidad propia de cada fase productiva inducen estr=C3= =A9s t=C3=A9rmico que disminuye el consumo voluntario de alimento y empeora= el =C3=ADndice de conversi=C3=B3n por mecanismos fisiol=C3=B3gicos conocid= os (Oke et al., 20= 24; Tahamtani et a= l., 2020). </spa= n><sp= an style=3D"font-family:'Times New Roman'">Las humedades relativas altas, u= n problema en climas tropicales h=C3=BAmedos, favorecen el crecimiento bact= eriano y deterioran las caracter=C3=ADsticas f=C3=ADsicas de la cama, gener= ando las condiciones para problemas podales y dermatitis que impactan en el= bienestar y la productividad (Hidalgo et al., 2024). Las altas concentraciones de Amon=C3=ADaco (NH=E2=82=83</span= >) impactan negativamente en = la salud respiratoria y son precursoras de inflamaci=C3=B3n sist=C3=A9mica,= que compromete la eficiencia de crecimiento (Qaid et al., 2023). Las altas concentraciones d= e Di=C3=B3xido de Carbono (CO=E2=82=82) se relacionan con hipoxia subcl=C3=ADnica y letargo = productivo, y la ventilaci=C3=B3n insuficiente puede causar estr=C3=A9s por= fr=C3=ADo y aumentar los requerimientos energ=C3=A9ticos de mantenimiento,= desviando los recursos metab=C3=B3licos para el crecimiento (Shynkaruk= et al., 2023; = Gho= lami et al., 2020). </= span>= Estas interacciones ambiental= es siguen patrones fisiol=C3=B3gicamente no lineales, pero que pueden ser a= proximadas mediante funciones multivariables estacionarias cuando las condi= ciones de manejo y la gen=C3=A9tica animal no var=C3=ADan en el tiempo, tal= como ocurre en los sistemas intensivos actuales con l=C3=ADneas comerciale= s estandarizadas y protocolos uniformizados. En la =C3=BAltima d=C3=A9cada se presenci=C3=B3 un auge e= n el uso de t=C3=A9cnicas de anal=C3=ADtica avanzada y machine learning par= a la estimaci=C3=B3n en tiempo real de variables productivas en sistemas pe= cuarios (Ojo et al= ., 2022). Estas t=C3=A9cnicas abarcan desde la evaluaci=C3=B3n automatizada= del bienestar animal usando visi=C3=B3n por computador (Nasiri<= span style=3D"font-family:'Times New Roman'"> et al., 2022; Fang= et al., 2020), la estimaci= =C3=B3n no invasiva del peso corporal con an=C3=A1lisis de imagen avanzado = (Nyalala et al., 2= 021; Natho et al.,= 2025) hasta la predicci=C3=B3n del rendimiento alimenticio a partir de la = integraci=C3=B3n de datos heterog=C3=A9neos (Gonz=C3=A1lez et al., 2021; Batista-Mendoz= a et al., 2023; </span= >Li= et al., 2024). </span= ><spa= n style=3D"font-family:'Times New Roman'">Los mejores enfoques, especialmen= te los basados en redes neuronales profundas y m=C3=A9todos de conjunto, pu= eden lograr una precisi=C3=B3n predictiva superior a la de los m=C3=A9todos= estad=C3=ADsticos convencionales (Adli et al., 2025; Vilema et al., 2022). Un modelo predictivo reciente basad= o en SVM obtuvo una precisi=C3=B3n del 98,95% en clasificaci=C3=B3n binaria= para mantenimiento predictivo industrial (Vilema et al., 2022), lo que ilustra el potencial = de estos algoritmos cuando se afinan correctamente. Pero estos avances tecnol=C3=B3gicos, estas metodo= log=C3=ADas sofisticadas, tienen serias restricciones para su implementaci= =C3=B3n pr=C3=A1ctica generalizada en fincas comerciales latinoamericanas, = especialmente en lugares como Ecuador con escasos recursos t=C3=A9cnicos e = infraestructura computacional. Primero, req= uieren infraestructura computacional dedicada y personal capacitado para ca= libraci=C3=B3n, implementaci=C3=B3n y mantenimiento continuo. Segundo, y qu= iz=C3=A1s lo m=C3=A1s relevante para el usuario final, a menudo son modelos= 'caja negra' cuya l=C3=B3gica interna es dif=C3=ADcil de comprender por el= operador de producci=C3=B3n que tiene que defender cambios en las recetas = ante la gerencia operativa (You et al., 2021). En contraposici=C3=B3n, la regresi=C3=B3n lineal m=C3=BAltiple sigue sien= do una herramienta predictiva vigente y con utilidad pr=C3=A1ctica en la pr= oducci=C3=B3n animal intensiva (Granda et al., 2023; Freitas et al., 2025). Esta t=C3=A9cnica estad=C3=ADstica = comprobada puede modelar el efecto combinado de muchos factores ambientales= sobre un resultado zoot=C3=A9cnico importante como el FCR, pero al mismo t= iempo mantener trazabilidad estad=C3=ADstica completa, transparencia audita= ble y explicabilidad directa. Un estudio re= ciente de rendimiento de mano de obra en construcci=C3=B3n usando regresi= =C3=B3n lineal logr=C3=B3 una confianza del 96% que demuestra que la simpli= cidad metodol=C3=B3gica no sacrifica la capacidad predictiva cuando se usa = adecuadamente (Granda et al., 2023). Cada coeficiente del modelo es un efecto marginal interp= retable y comprensible para el personal t=C3=A9cnico, lo que facilita enorm= emente la comunicaci=C3=B3n de resultados y la justificaci=C3=B3n de las de= cisiones operativas. Un mod= elo predictivo de este tipo, una vez validado, puede incorporarse como un m= =C3=B3dulo de alerta temprana en el flujo de trabajo diario de una granja c= omercial. Cuando el FCR previsto para la semana siguiente supera ciertos um= brales internos, el equipo t=C3=A9cnico puede actuar de forma preventiva mo= dificando par=C3=A1metros clave como ventilaci=C3=B3n m=C3=ADnima, caudal d= e l=C3=ADneas de bebida, densidad efectiva o estrategia de alimentaci=C3=B3= n antes de que la eficiencia se deteriore y los sobrecostes sean irreversib= les. Esta capacidad predictiva es un cambio radical con respecto a la forma= tradicional reactiva de gestionar y convertirse en una gesti=C3=B3n preven= tiva impulsada por datos. P= ero a pesar de estos avances conceptuales y tecnol=C3=B3gicos, a=C3=BAn exi= ste una gran brecha en la literatura cient=C3=ADfica enfocada en Latinoam= =C3=A9rica y, m=C3=A1s espec=C3=ADficamente, en el contexto ecuatoriano. Ha= y poca evidencia emp=C3=ADrica disponible de modelos predictivos estad=C3= =ADsticamente verificados para estimar el FCR en granjas av=C3=ADcolas come= rciales automatizadas utilizando datos reales de sensores en ambientes trop= icales h=C3=BAmedos. Esta ausencia es especialmente relevante porque el cli= ma de lugares como Santo Domingo de los Ts=C3=A1chilas, con alta temperatur= a media anual, humedad permanente y presi=C3=B3n sanitaria, es muy diferent= e al de los climas templados en los que se realiz=C3=B3 la mayor=C3=ADa de = estudios experimentales que se encuentran en la literatura internacional. <= /span><a id=3D"_Hlk213272489">La i= ndustria av=C3=ADcola en Ecuador es un pilar para la seguridad alimentaria = y el desarrollo econ=C3=B3mico del pa=C3=ADs. Seg=C3=BAn cifras de la </spa= n>= Corporaci=C3=B3n Nacional de Avicultores del Ecuador (CONAVE, 2024) en los =C3=BAltimos 30 a=C3=B1os el sector crec= i=C3=B3 de manera permanente, al pasar de producir 50 millones de pollos de= engorde en 1990 a m=C3=A1s de 300 millones en 2024. Pero la producci=C3=B3= n de pollos broilers en zonas tropicales se enfrenta a grandes desaf=C3=ADo= s por los cambios clim=C3=A1ticos que ocurren durante todo el a=C3=B1o, afe= ctando el rendimiento productivo y el bienestar de las aves (Hidalgo</s= pan> et al., 2024).<sp= an style=3D"font-family:'Times New Roman'"> </a>La extrapolaci=C3=B3n directa de resultados de= climas templados a climas tropicales h=C3=BAmedos es metodol=C3=B3gicament= e inapropiada, ya que existen diferencias significativas en la din=C3=A1mic= a ambiental, presencia de pat=C3=B3genos, desaf=C3=ADos de manejo y respues= ta fisiol=C3=B3gica de las aves expuestas a estr=C3=A9s t=C3=A9rmico perman= ente. Por eso, es necesario desarrollar y validar modelos ajustados a condi= ciones tropicales, teniendo en cuenta sus peculiaridades ambientales, gen= =C3=A9ticas y de manejo. La falta de dichas herramientas limita la optimiza= ci=C3=B3n de la eficiencia alimenticia en un sector clave para la seguridad= alimentaria regional y nacional. En este c= ontexto problem=C3=A1tico, la pregunta cient=C3=ADfica que gu=C3=ADa la pre= sente investigaci=C3=B3n es: =C2=BFen qu=C3=A9 medida se puede estimar el f= actor de conversi=C3=B3n alimenticia semanal en pollos de engorde criados e= n galpones automatizados, a partir de datos ambientales capturados por sist= emas IoT, mediante un modelo de regresi=C3=B3n lineal m=C3=BAltiple validad= o estad=C3=ADsticamente y listo para ser utilizado operativamente por el pe= rsonal de producci=C3=B3n? = El objetivo general de la investigaci=C3=B3n consiste en desarrollar un mod= elo predictivo basado en regresi=C3=B3n lineal m=C3=BAltiple que estime el = FCR en granjas av=C3=ADcolas automatizadas a partir de variables ambientale= s cr=C3=ADticas medidas en tiempo real. De manera espec=C3=ADfica, se busca= : (i) caracterizar el comportamiento conjunto de temperatura, humedad relat= iva, Di=C3=B3xido de Carbono (CO=E2=82=82)<span= style=3D"font-family:'Times New Roman'"> y Amon=C3=ADaco (NH=E2=82=83= ) bajo condiciones com= erciales ecuatorianas; (ii) identificar cu=C3=A1les de estas variables expl= ican con mayor peso estad=C3=ADstico la variabilidad del FCR acumulado por = semana; (iii) construir la ecuaci=C3=B3n predictiva y evaluar su ajuste, es= tabilidad y ausencia de colinealidad; y (iv) proponer su uso como herramien= ta de apoyo a la toma de decisiones para optimizar la eficiencia alimentici= a y, por extensi=C3=B3n, la rentabilidad del sistema productivo.= 2.<sp= an style=3D"width:9pt; font:7pt 'Times New Roman'; display:inline-block">&#= xa0; Metodolog=C3=ADaSe realiz=C3=B3 un estudio de enfoque cuantitativo, observaci= onal y longitudinal retrospectivo, utilizando datos hist=C3=B3ricos generad= os por granjas automatizadas de pollos de engorde en el periodo de enero de= 2022 a marzo de 2025 (39 meses). El estudio se llev=C3=B3 a cabo en la pro= vincia de Santo Domingo de los Ts=C3=A1chilas, Ecuador, zona tropical h=C3= =BAmeda con temperatura media anual de 25-27=C2=B0C, humedad relativa >8= 0% y r=C3=A9gimen de lluvia estacional (Hidalgo et al., 2024; Quintana-Ospina<span= style=3D"font-family:'Times New Roman'"> et al., 2023). Las granjas de est= udio utilizan galpones modernos con ventilaci=C3=B3n mec=C3=A1nica forzada = tipo t=C3=BAnel, cortinas y sistemas automatizados de comederos y bebederos= (Pereira et al., = 2020; Liu et al., = 2023). Este ambiente tecnol=C3=B3gico permite el monitoreo en tiempo real d= e las condiciones ambientales y productivas de las aves. 2.1. Recolecci=C3=B3n y registro semanal de datosLas condiciones ambientales se controlaron co= n sensores IoT colocados a nivel de las aves y distribuidos uniformemente e= n los galpones. Estos sensores tomaron medidas continuas de temperatura, hu= medad relativa, Di=C3=B3xido de Carbono (CO=E2=82=82) y Amon=C3=ADaco (NH=E2=82=83)</sp= an> ambiental (Ahmed</spa= n> et al., 2024; <span= style=3D"font-family:'Times New Roman'; background-color:#ffff00">Elwakeel= , 2025). Los datos de = producci=C3=B3n se tomaron de los sistemas internos de gesti=C3=B3n de cada= granja, tales como consumo de alimento, peso promedio de las aves y mortal= idad diaria (Astill et al., 2020). Todos estos datos se recopilaron y revisaron semanalmente = utilizando protocolos estandarizados de captura de datos en sistemas intens= ivos (Liu et al., = 2024). <span= style=3D"font-family:'Times New Roman'; font-style:italic">2.2.</spa= n>&#= xa0; Poblaci=C3=B3n y muestraLa poblaci=C3=B3n estuvo compuesta por lotes comerciales complet= os de las l=C3=ADneas gen=C3=A9ticas COBB 500 y ROSS 308, seleccionadas por= su predominio en sistemas intensivos ecuatorianos (Gholami et al</span= >., 2020; Quintana-Ospin= a et al., 2023). La un= idad de an=C3=A1lisis fue el registro semanal consolidado por granja, integ= rando promedios ambientales semanales con el FCR acumulado. El proceso de selecci=C3=B3n inici=C3=B3= con m=C3=A1s de 8000 registros brutos, estructurado en m=C3=BAltiples hoja= s, pertenecientes a 18 granjas numeradas. Se aplicaron criterios de inclusi= =C3=B3n: galpones completamente automatizados; capacidad mayor o igual a 30= ,000 aves por lote; sensorizaci=C3=B3n ambiental con registro horario conti= nuo; historial digitalizado mayor o igual a 20 ciclos productivos consecuti= vos; y densidad de alojamiento entre 30-40 kg/m=C2=B2 (Gallard= et= al., 2022; <sp= an style=3D"font-family:'Times New Roman'; background-color:#ffff00">Shynka= ruk et al., 2023). Los= criterios de exclusi=C3=B3n incluyeron: semanas con fallas de sensorizaci= =C3=B3n >20% del tiempo; brotes sanitarios; cambios significativos en fo= rmulaci=C3=B3n alimenticia o l=C3=ADnea gen=C3=A9tica; y registros con valo= res ausentes. Tras la depuraci=C3=B3n, la muestra final consolid=C3=B3 936 = observaciones semanales de siete granjas automatizadas, codificadas como Gr= anja A, B, C, D, E, F y G, para confidencialidad industrial.2.3. <s= pan style=3D"font-family:'Times New Roman'; font-style:italic">Procesamient= o de datosEl procesamiento = se llev=C3=B3 a cabo utilizando una cadena ETL (Extract, Transform, Load</span= >) estandarizada y documentad= a (Li et al., 2024= ; Gonz=C3=A1lez et= al., 2021). En la etapa de extracci=C3=B3n, se unificaron las fuentes oper= ativas heterog=C3=A9neas de las siete granjas. La transformaci=C3=B3n impli= c=C3=B3: estandarizaci=C3=B3n de variables y formatos, creaci=C3=B3n de ide= ntificadores =C3=BAnicos lote-semana y el c=C3=A1lculo de variables derivad= as, tales como: FCR acumulado semanal, mortalidad acumulada (%), consumo y = ganancia semanal por ave. Para garantizar la calidad de los datos, se verif= ic=C3=B3 que todos estuvieran completos, que la informaci=C3=B3n registrada= fuera consistente en el tiempo, que los valores estuvieran dentro de rango= s normales. Adem=C3=A1s, se utiliz=C3=B3 un m=C3=A9todo de dispersi=C3=B3n = de valores para detectar outliers de forma precisa. Finalmente, la etapa de carga unific=C3=B3 la in= formaci=C3=B3n en una =C3=BAnica base de datos anal=C3=ADtica, consolidando= las variables descritas en la Tabla 1. Tabla 1Variables de la base de datos<table style=3D"width:100%; margi= n-right:auto; margin-left:auto; margin-bottom:0pt; padding:0pt; border-coll= apse:collapse"><tr style=3D"height:26.05pt"><td style=3D"width:39.3%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">Variable</= span></td><td style=3D"width:23.26%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:10pt; line-height:115%; font-size:10pt">Tipo (unidad)</td><td style= =3D"width:37.44%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n breve</td></tr><tr><td style=3D"wi= dth:39.3%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">fecha_sema= na</td><td style=3D"width:23.26%; border-top:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">Fecha (AAAA-MM-DD)</td><td style=3D"widt= h:37.44%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-alig= n:top">Fecha de co= rte semanal (ISO).</td></tr><tr><td style=3D"width:39.3%; paddin= g:0pt 5.4pt; vertical-align:top">A=C3=B1o</td><td style=3D"width:23.26%; padding:0= pt 5.4pt; vertical-align:top">Entero</td><td style=3D"width:37.44%; padding:0pt 5.= 4pt; vertical-align:top">A=C3=B1o calendario (2022=E2=80=932025).</td></tr><tr><td= style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Mes</td><td style=3D= "width:23.26%; padding:0pt 5.4pt; vertical-align:top">Entero (1=E2=80=9312)</td><t= d style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top">= Mes calendario.</t= d></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top"= >Ciclo<= /td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top">Entero</td><= td style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top">ID interno de ciclo/lote.<= /span></td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertic= al-align:top">Sema= na</td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-al= ign:top">Entero (1= =E2=80=937)</td><td style=3D"width:37.44%; padding:0pt 5.4pt; ve= rtical-align:top">= Semana del ciclo productivo.</td></tr><tr><td style=3D"width:39.= 3%; padding:0pt 5.4pt; vertical-align:top">granja_id</td><td style=3D"width:23.26%= ; padding:0pt 5.4pt; vertical-align:top">Categ=C3=B3rica (A=E2=80=93G)</td><td sty= le=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Identificador an=C3=B3nimo de gran= ja.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; ve= rtical-align:top">= linea_genetica</td><td style=3D"width:23.26%; padding:0pt 5.4pt;= vertical-align:top">Categ=C3=B3rica</td><td style=3D"width:37.44%; padding:0pt 5.= 4pt; vertical-align:top">COBB 500 / ROSS 308.</td></tr><tr><td style=3D"width:39.3= %; padding:0pt 5.4pt; vertical-align:top">ave_madura</td><td style=3D"width:23.26%= ; padding:0pt 5.4pt; vertical-align:top">Binaria (0/1)</td><td style=3D"width:37.4= 4%; padding:0pt 5.4pt; vertical-align:top">0 =3D sem. 1=E2=80=934; 1 =3D sem. 5=E2=80=937.</s= pan></td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical= -align:top">fcr_ac= umulado (fcr_acumulativo)</td><td style=3D"width:23.26%; padding= :0pt 5.4pt; vertical-align:top">Continua (kg/kg)</td><td style=3D"width:37.44%; pa= dding:0pt 5.4pt; vertical-align:top">Alimento acum. / peso vivo acum. a semana t.<= /td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:to= p">mortalidad_acum= ulada_pct0</td><td style=3D"width:23.26%; padding:0pt 5.4pt; ver= tical-align:top">C= ontinua (%)</td><td style=3D"width:37.44%; padding:0pt 5.4pt; ve= rtical-align:top">= Fallecidas acum. / poblaci=C3=B3n inicial =C3=97100.</td></tr><t= r><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top">consumo_semanal_g_ave</spa= n></td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top= ">Continua (g/ave)= </td><td style=3D"width:37.44%; padding:0pt 5.4pt; vertical-alig= n:top">Consumo sem= anal / aves vivas promedio.</td></tr><tr><td style=3D"width:39.3= %; padding:0pt 5.4pt; vertical-align:top">ganancia_semanal_g</td><td style=3D"widt= h:23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (g/ave)</td><td style=3D= "width:37.44%; padding:0pt 5.4pt; vertical-align:top"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABgAAAASCAYAA= ABB7B6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAACdJREFUOI1jYBgF= o2Awgf9DzmCaGk4Vg7EZMrhdjM1QmkbgKBgmAABh8gj4zupwOAAAAABJRU5ErkJggg=3D=3D" w= idth=3D"24" height=3D"18" alt=3D"" style=3D"margin-top:14.82pt; margin-left= :1.55pt; position:absolute" />Incremento de peso semanal por ave.</td></tr><tr><td sty= le=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top">peso_promedio_g</td><td = style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Continua (g)</td><td= style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><= span style=3D"font-family:'Times New Roman'">Peso promedio de muestra seman= al (=E2=89=A5100 aves).</td></tr><tr><td style=3D"width:39.3%; p= adding:0pt 5.4pt; vertical-align:top">temperatura_promedio</td><td style=3D"width:= 23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (=C2=B0C)</td><td style=3D= "width:37.44%; padding:0pt 5.4pt; vertical-align:top">Promedio semanal de temperatura interio= r.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; ver= tical-align:top">h= umedad_promedio</td><td style=3D"width:23.26%; padding:0pt 5.4pt= ; vertical-align:top">Continua (%)</td><td style=3D"width:37.44%; padding:0pt 5.4p= t; vertical-align:top">Promedio semanal de HR interior.</td></tr><tr><td style=3D"= width:39.3%; padding:0pt 5.4pt; vertical-align:top">co2_promedio_ppm</td><td styl= e=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (ppm)</td><td s= tyle=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><sp= an style=3D"font-family:'Times New Roman'">Promedio semanal de CO=E2=82=82 = interior.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4= pt; vertical-align:top">nh3_promedio_ppm</td><td style=3D"width:23.26%; padding:0p= t 5.4pt; vertical-align:top">Continua (ppm)</td><td style=3D"width:37.44%; padding= :0pt 5.4pt; vertical-align:top">Promedio semanal de NH=E2=82=83 interior.</td></tr= ><tr><td style=3D"width:39.3%; border-bottom:0.75pt solid #000000; padding:= 0pt 5.4pt; vertical-align:top">densidad_aves_m2</td><td style=3D"width:23.26%; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Continua (aves/m=C2= =B2)</td><td style=3D"width:37.44%; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Aves / =C3=A1rea =C3=BAtil del galp=C3=B3n.</s= pan></td></tr></table> <= span style=3D"font-family:'Times New Roman'; font-style:italic">2.4.<= /span> An=C3=A1lisis estad=C3=ADstico El an=C3=A1lisis se inici=C3=B3 con 18 variables candidatas= de la base de datos. La elecci=C3=B3n de predictores se realiz=C3=B3 en tr= es pasos para asegurar robustez metodol=C3=B3gica (Freitas et al., 2025; Li<span= style=3D"font-family:'Times New Roman'"> et al., 2024). En la primera etap= a se usaron criterios te=C3=B3rico-pr=C3=A1cticos de literatura av=C3=ADcol= a tropical y disponibilidad operativa. En la segunda etapa se realiz=C3=B3 = un an=C3=A1lisis exploratorio cuantitativo usando una matriz de correlacion= es y modelos univariados iniciales (p < 0.25), seg=C3=BAn metodolog=C3= =ADas informadas en estudios previos de producci=C3=B3n av=C3=ADcola (</spa= n>Q= uintana-Ospina et al.,= 2023; Gonz=C3=A1lez et al., 2021). La tercera etapa estableci=C3=B3 las variables predictora= s finales combinando significancia estad=C3=ADstica (p < 0.05) con justi= ficaci=C3=B3n te=C3=B3rica encontrada en literatura cient=C3=ADfica sobre f= isiolog=C3=ADa aviar en ambientes tropicales (Oke et al., 2024; Hidalgo et al., 2024). Se estim=C3=B3 un modelo de regresi=C3=B3n lineal m= =C3=BAltiple (RLM) por M=C3=ADnimos Cuadrados Ordinarios (MCO), tomando com= o variable dependiente el FCR acumulado semanal. Candidatas a variables pre= dictoras: temperatura promedio semanal (=C2=B0C), humedad relativa promedio= semanal (%), CO=E2=82=82 promedio semanal (ppm), NH=E2=82=83 promedio sema= nal (ppm), ave madura (0=3Dsemanas 1-4; 1=3Dsemanas 5-7) y l=C3=ADnea gen= =C3=A9tica (0=3DCOBB 500; 1=3DROSS 308). La elecci=C3=B3n de RLM sobre mode= los m=C3=A1s complejos se alinea con trazabilidad operativa y facilidad de = interpretaci=C3=B3n en ambientes de producci=C3=B3n av=C3=ADcola comercial = (Freitas et al., 2= 025; Granda et al.= , 2023; Vilema et = al., 2022). La Ecuaci=C3=B3n 1 inicial del modelo de RLM que considera las variables antes mencionas:= <img src=3D"data:i= mage/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhAAAAApCAYAAABk6hDFAAAABHNCSVQICAg= IfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeJzsnXd8lFXa978zmZlkJmXSQ0moAQ= LSqxQBUemIiKCwKiAoygIKLiorWFdW7IgKqKhYFkVBQAVREEUglAAJJRASICSkJzOZ3u/z/sE75= yEGFBWfdfeZ3+fDH2Rm7vuU61z9XJdKCCEIIYQQQgghhBBCuEyoVCqV+t89iBBCCCGEEEII4T8P= IQUihBBCCCGEEEL41dD8uwfwR0EIgc/nw+/3ExYWhlarRa3+9+tLgUAAr9eLEAKdTodG81+7BSF= cQfxZ6fnPhkAggNvtRgiBXq8nLCzsNz/L6/Xi9XpRq9VERET8YesdCATw+XwoioJWq0Wj0aBSqf= 6Qd/03QQiB1+vF7/ej0WjQ6XShdfsNuJD+fk4mBXmQRqORZ0ET/MBisWCxWFAUpc6PwsPDiY+PJ= yIiQr7M4XDgcDjw+/2oVCr0ej1GoxGNRkMgEKC6uhqXy4UQApVKhVarxWAwYDQa/1eYnt/vx2w2= s2fPHjIzM4mNjeXGG28kPT0djUaDEAKHw4Hb7UZRFDQaDdHR0Wi12j90XDabjcLCQrZs2UJ1dTW= 9e/dm6NChhIeHy+8oiiLXNjY2NnQgQsDv92Mymfjxxx/Zu3cvDRo04KabbqJp06YA2O12vF6v/G= 5ERAQxMTG/S3ja7XZMJhMAycnJREREEAgEMJlMOBwOIiMjiY+P/13v+CNgs9n461//islk4s033= 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MtERUW1CTBpTbxYUlLCF198QWRkJGPHjlXnVRqNhqFDh6rlb06DZDQa1QyrN3O75a7w8PA2aZue= eOIJ9f8mk4mEhAT175vbk2XZ71zrRiRJIikpiYaGBlwuF4qi8NxzzxESEoLX62X48OHtTsda4zs= 6Mk5ptVq0Wm27I/iBAwcYNWoUzc3NrFmzhokTJzJ8+PB2A8cURenQ/hBATZzysNMpqkaWZSZNms= Qrr7zS6bnO/Dy6SJKELMtYrVYqKytZvHgxISEhOBwOzpw5Q3p6ervTMZfLxZQpU9rdkwGQlpbGG= 2+80a5SOXPmDHa7nRkzZmAymVi+fHmHOmtzczOzZ8/mzJkzdy0nSRLz589n6dKl7dbZ3XSKAoiJ= ibnnKDs/3x5KSkoQQjBw4EA0Gg3Xrl1DkqQ2ufzvhE6nY/v27R1KRhocHNyu+dwajHbo0CEAJk+= erB4g2h5Go5HMzMwORWN2ZV7A+6FTFMCjeo68nwePoihcvHiRxx57TM04nJeXhyzL6PV6ioqK6N= u37x07rkaj6dTt5pcuXcLn85GQkKAeDpKdnc3o0aNpaGi4a3pxWZYfmI9ACNEtCXb9PdfPA8Xpd= JKbm0vfvn3V6aHL5aKmpobVq1dz/PjxLl16O3LkCMOHD6dHjx7qGQMrVqxgyZIlHdpo1Zn4fD7q= 6+s5ceIExcXF/Otf/6KwsBC73d5lyqDDqwB+/HwT7HY7e/fuZeDAgerW6tYz/vr160dCQkKXLgN= +8cUXeL1eJk+ejBCC2tpaDhw4wIQJE+54JsKDoqamhu3bt7c5rRdg9OjRXRKvIkmS5FcAfvx8S5= EkSfp/OpFbrJ1kX5oAAAAASUVORK5CYII=3D" width=3D"256" height=3D"39" alt=3D"" = style=3D"vertical-align:middle" />= Donde n representa el tama=C3=B1o muestral y k el n=C3=BAmero de predictore= s. Se estableci=C3=B3 el reporte de error est=C3=A1ndar de estimaci=C3=B3n,= significancia global mediante prueba F (ANOVA), y significancia individual= de coeficientes mediante pruebas t bilaterales (=CE=B1 =3D 0.05). Se calcu= laron intervalos de confianza del 95% para todos los coeficientes de regres= i=C3=B3n.Se implement=C3=B3= validaci=C3=B3n tipo hold-out 80/20 con partici=C3=B3n estratificada por c= iclo productivo para garantizar representatividad temporal (Freitas</sp= an> et al., 2025; <spa= n style=3D"font-family:'Times New Roman'; background-color:#ffff00">You</sp= an> et al., 2021). En el conj= unto de validaci=C3=B3n independiente (20% de observaciones no utilizadas e= n el ajuste) se estimaron R=C2=B2 de validaci=C3=B3n, MAE (Mean Absolute Error) y RMSE (Root Mean Square Error) como m=C3=A9tricas de capacidad predictiva en datos n= o vistos. Los supuestos fundamentales de la RLM se verificaron mediante: gr= =C3=A1ficos de dispersi=C3=B3n y residuos parciales para evaluar linealidad= ; prueba de Durbin-Watson para verificar independencia de residuos (ausenci= a de autocorrelaci=C3=B3n); prueba de Breusch-Pagan para evaluar homocedast= icidad (varianza constante de errores); prueba de Shapiro-Wilk para verific= ar normalidad de residuos; y Factores de Inflaci=C3=B3n de la Varianza (VIF<spa= n style=3D"font-family:'Times New Roman'">) para detectar multicolinealidad= entre predictores, con umbral cr=C3=ADtico VIF < 5 (Freitas= et al., 2025; Li<= span style=3D"font-family:'Times New Roman'"> et al., 2024). Ante evidencia= de heterocedasticidad residual, la inferencia estad=C3=ADstica se compleme= nt=C3=B3 con errores est=C3=A1ndar robustos agrupados por ciclo productivo = (cl= uster-robust standard errors).El an=C3=A1lisis es= tad=C3=ADstico se realiz=C3=B3 en RStudio (versi=C3=B3n 2023.12.0) utilizan= do los paquetes stats, car, lmtest y sandwich, con verificaci=C3=B3n cruzad= a en IBM SPSS Statistics = (versi=C3=B3n 27) para garantizar reproducibilidad de resultados.</p= >2.5. <span= style=3D"font-family:'Times New Roman'; font-style:italic">Consideraciones= =C3=A9ticasEl estudio empl= e=C3=B3 =C3=BAnicamente datos secundarios anonimizados, autorizados por la = gerencia t=C3=A9cnica bajo acuerdos de confidencialidad industrial. Todas l= as granjas av=C3=ADcolas fueron codificadas (A-G) y no se realizaron manipu= laciones experimentales ni cambios en los protocolos productivos. La inform= aci=C3=B3n confidencial se manej=C3=B3 con protocolos estrictos de confiden= cialidad para no poner en riesgo la posici=C3=B3n competitiva de las unidad= es participantes. = ; 3. = ResultadosEl presente estudio cumpli=C3=B3 satisfactoriamente su objetivo de desa= rrollar un modelo predictivo basado en regresi=C3=B3n lineal m=C3=BAltiple = para estimar el FCR semanal en granjas av=C3=ADcolas automatizadas bajo con= diciones tropicales h=C3=BAmedas del Ecuador. 3.1.&= #xa0;Estad=C3=ADsticos descriptivos de las variables del estudioLa Tabla 2 presenta los estad=C3=ADsticos descriptivos de las 936 = observaciones semanales analizadas. El FCR acumulado mostr=C3=B3 media de 1= .23 (DE =3D 0.22) con rango entre 0.81 y 1.67, reflejando la heterogeneidad= natural entre fases productivas tempranas (semanas 1-4) y tard=C3=ADas (se= manas 5-7).Tabla 2E= stad=C3=ADsticos descriptivos de las variables del estudio (n =3D 936)</spa= n><table style=3D"width:100%; margin-bottom:0pt; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:= 1.5pt"><thead><tr><td style=3D"width:22.68%; border-bottom:0.75pt solid #00= 0000; padding:0.75pt; vertical-align:top">Variable</td><td style=3D"width:0.86%; border-bottom:0.75pt so= lid #000000; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%;= border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">Media</td><td style=3D"width:= 12.54%; border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-align:= top">DE</td><td style=3D"widt= h:14.86%; border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-alig= n:top"><spa= n style=3D"font-family:'Times New Roman'">M=C3=ADnimo</td><td st= yle=3D"width:15.76%; border-bottom:0.75pt solid #000000; padding:0.75pt; ve= rtical-align:top">M=C3=A1ximo= </td><td style=3D"width:11.82%; border-bottom:0.75pt solid #000000; padding= :0.75pt; vertical-align:top">CV (%)</spa= n></td></tr></thead><tbody><tr><td style=3D"width:22.68%; padding:0.75p= t; vertical-align:top">FCR acumulativo (= kg/kg)</td><td style=3D"width:0.86%; padding:0.75pt; vertical-al= ign:top"><s= pan style=3D"font-family:'Times New Roman'"> </td><td style= =3D"width:15.76%; padding:0.75pt; vertical-align:top">1,23</td><td style=3D"width:12.54%; padding:0.75pt= ; vertical-align:top">0,22</t= d><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top">0,81</td><td style=3D"width:15.76%; p= adding:0.75pt; vertical-align:top">1,67<= /span></td><td style=3D"width:11.82%; padding:0.75pt; vertical-align:to= p">17,9</td></tr><tr><td styl= e=3D"width:22.68%; padding:0.75pt; vertical-align:top">Temperatura interna (=C2=B0C)</td><td style=3D"wi= dth:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.75pt; ver= tical-align:top">27,42</td><t= d style=3D"width:12.54%; padding:0.75pt; vertical-align:top">2,10</td><td style=3D"width:14.86%; padding= :0.75pt; vertical-align:top">22,90</span= ></td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">32,00</td><td style=3D"width:= 11.82%; padding:0.75pt; vertical-align:top">7,7</td></tr><tr><td style=3D"width:22.68%; padding:0.75pt; = vertical-align:top">Humedad relativa (%)= </td><td style=3D"width:0.86%; padding:0.75pt; vertical-align:to= p"> </td><td style=3D"wi= dth:15.76%; padding:0.75pt; vertical-align:top">82,58</td><td style=3D"width:12.54%; padding:0.75pt; ver= tical-align:top">4,18</td><td= style=3D"width:14.86%; padding:0.75pt; vertical-align:top">68,50</td><td style=3D"width:15.76%; padding= :0.75pt; vertical-align:top">89,80</span= ></td><td style=3D"width:11.82%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">5,1</td></tr><tr><td style=3D= "width:22.68%; padding:0.75pt; vertical-align:top">CO=E2=82=82 (ppm)</td><td style=3D"width:0.86%; paddi= ng:0.75pt; vertical-align:top"> </s= pan></td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:top"= >589,25</td><td style=3D"widt= h:12.54%; padding:0.75pt; vertical-align:top">36,28</td><td style=3D"width:14.86%; padding:0.75pt; verti= cal-align:top">476,30</td><td= style=3D"width:15.76%; padding:0.75pt; vertical-align:top">700,00</td><td style=3D"width:11.82%; paddin= g:0.75pt; vertical-align:top">6,2= </td></tr><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align= :top"><span= style=3D"font-family:'Times New Roman'">NH=E2=82=83 (ppm)</td><= td style=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; paddi= ng:0.75pt; vertical-align:top">18,46</sp= an></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">= 2,02</td><td style=3D"width:1= 4.86%; padding:0.75pt; vertical-align:top">12,60</td><td style=3D"width:15.76%; padding:0.75pt; vertical= -align:top">24,70</td><td sty= le=3D"width:11.82%; padding:0.75pt; vertical-align:top">10,9</td></tr><tr><td style=3D"width:22.68%; pad= ding:0.75pt; vertical-align:top">Peso pr= omedio (g)</td><td style=3D"width:0.86%; padding:0.75pt; vertica= l-align:top"> </td><td s= tyle=3D"width:15.76%; padding:0.75pt; vertical-align:top">1.476,91</td><td style=3D"width:12.54%; paddin= g:0.75pt; vertical-align:top">957,24</sp= an></td><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top">= 166,20</td><td style=3D"width= :15.76%; padding:0.75pt; vertical-align:top">3.389,30</td><td style=3D"width:11.82%; padding:0.75pt; ver= tical-align:top">64,8</td></t= r><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align:top">Mortalidad acumulada (%)</td><td st= yle=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.= 75pt; vertical-align:top">1,70</p= ></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">1,00</td><td style=3D"width:14.86%;= padding:0.75pt; vertical-align:top">0,1= 0</td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:= top">3,58</td><td style=3D"wi= dth:11.82%; padding:0.75pt; vertical-align:top">58,8</td></tr><tr><td style=3D"width:22.68%; padding:0.7= 5pt; vertical-align:top">Consumo semanal= (g/ave)</td><td style=3D"width:0.86%; padding:0.75pt; vertical-= align:top">= </td><td sty= le=3D"width:15.76%; padding:0.75pt; vertical-align:top">1.666,60</td><td style=3D"width:12.54%; padding:= 0.75pt; vertical-align:top">866,36</span= ></td><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">426,50</td><td style=3D"width= :15.76%; padding:0.75pt; vertical-align:top">3.483,80</td><td style=3D"width:11.82%; padding:0.75pt; ver= tical-align:top">52,0</td></t= r><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align:top">Densidad (aves/m=C2=B2)</td><td sty= le=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.7= 5pt; vertical-align:top">16,38</p= ></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">0,17</td><td style=3D"width:14.86%;= padding:0.75pt; vertical-align:top">16,= 10</td><td style=3D"width:15.76%; padding:0.75pt; vertical-align= :top"><span= style=3D"font-family:'Times New Roman'">16,60</td><td style=3D"= width:11.82%; padding:0.75pt; vertical-align:top">1,0</td></tr></tbody></table> Las condi= ciones ambientales reflejaron caracter=C3=ADsticas t=C3=ADpicas de clima tr= opical h=C3=BAmedo: temperatura promedio de 27.42=C2=B0C (DE =3D 2.10) con = rango entre 22.90 y 32.00=C2=B0C, y humedad relativa de 82.58% (DE =3D 4.18= ), valor que excede las gu=C3=ADas t=C3=A9cnicas =C3=B3ptimas establecidas = (60-75%). Los gases ambient= ales se mantuvieron en concentraciones moderadas (CO=E2=82=82: 589.25 =C2= =B1 36.28 ppm; NH=E2=82=83: 18.46 =C2=B1 2.02 ppm). La variabilidad observa= da en peso corporal (1,476.91 =C2=B1 957.24 g) y FCR (1.23 =C2=B1 0.22) ref= leja la progresi=C3=B3n natural del ciclo productivo, proporcionando adecua= da dispersi=C3=B3n para la modelaci=C3=B3n predictiva.3.2. Selecci=C3=B3n de = variables predictores mediante an=C3=A1lisis de correlacionesEl an=C3=A1lisis de correlaciones bivari= adas revel=C3=B3 patrones definidos entre el FCR acumulado y las variables = ambientales evaluadas. Como se observa en la Tabla 3, cuatro variables mostraron correlaciones estad= =C3=ADsticamente significativas (p < 0.001) con el FCR acumulado, mientr= as que otras tres variables no alcanzaron el umbral de significancia establ= ecido.Tabl= a 3Correl= aci=C3=B3n de variables candidatas con FCR acumulado<table style= =3D"width:100%; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><= tr style=3D"height:22.2pt"><td style=3D"width:44.84%; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical= -align:top">Variable</td><td = style=3D"width:27.58%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 3.5pt; vertical-align:top">Correlaci=C3=B3n FCR</td><td style=3D"width:27.5= 8%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 3.5pt; vertical-align:top">Sig= nificancia</td></tr><tr style=3D"height:30.6pt"><td style=3D"wid= th:44.84%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">Temperatura ambiental (=C2=B0C)= </td><td style=3D"width:27.58%; border-top:0.75pt solid #000000;= padding:0pt 3.5pt; vertical-align:top">-0.682***</td><td style=3D"width:27.58%; border-top:0.75pt soli= d #000000; padding:0pt 3.5pt; vertical-align:top">p < 0.001</td></tr><tr style=3D"height:30.6pt"><td= style=3D"width:44.84%; padding:0pt 3.5pt; vertical-align:top">Humedad relativa (%)</td><td style=3D"wi= dth:27.58%; padding:0pt 3.5pt; vertical-align:top">0.512***</td><td style=3D"width:27.58%; padding:0pt = 3.5pt; vertical-align:top">p < 0.001= </td></tr><tr style=3D"height:30.6pt"><td style=3D"width:44.84%;= padding:0pt 3.5pt; vertical-align:top">Ave_madura (semanas 5-7)</td><td style=3D"width:27.58%; padding= :0pt 3.5pt; vertical-align:top">0.745**= *</td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">p < 0.001</td></t= r><tr style=3D"height:30.6pt"><td style=3D"width:44.84%; padding:0pt 3.5pt;= vertical-align:top">L=C3=ADnea gen=C3= =A9tica (ROSS 308)</td><td style=3D"width:27.58%; padding:0pt 3.= 5pt; vertical-align:top">0.321***</span= ></td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top"= >p < 0.001</td></tr><tr s= tyle=3D"height:20.4pt"><td style=3D"width:44.84%; padding:0pt 3.5pt; vertic= al-align:top">CO=E2=82=82 promedio (ppm= )</td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">0.045</td><td style= =3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top">p =3D 0.324</td></tr><tr style=3D"height:20.4p= t"><td style=3D"width:44.84%; padding:0pt 3.5pt; vertical-align:top">NH=E2=82=83 promedio (ppm)</td><td= style=3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top">0.038</td><td style=3D"width:27.58%; pad= ding:0pt 3.5pt; vertical-align:top">p = =3D 0.287</td></tr><tr style=3D"height:20.4pt"><td style=3D"widt= h:44.84%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-a= lign:top">= Densidad_aves_m=C2=B2<= /p></td><td style=3D"width:27.58%; border-bottom:0.75pt solid #000000; padd= ing:0pt 3.5pt; vertical-align:top">0.06= 2</td><td style=3D"width:27.58%; border-bottom:0.75pt solid #000= 000; padding:0pt 3.5pt; vertical-align:top">p =3D 0.156</td></tr></table> La temperatura= ambiental exhibi=C3=B3 correlaci=C3=B3n negativa fuerte (r =3D -0.682, p &= lt; 0.001), evidenciando su papel en la din=C3=A1mica del FCR. Ave_madura p= resent=C3=B3 la correlaci=C3=B3n positiva m=C3=A1s alta (r =3D 0.745, p <= ; 0.001), reflejando el incremento progresivo del FCR en semanas finales de= l ciclo productivo. La humedad relativa mostr=C3=B3 correlaci=C3=B3n positi= va moderada (r =3D 0.512, p < 0.001), mientras que la l=C3=ADnea gen=C3= =A9tica evidenci=C3=B3 correlaci=C3=B3n positiva significativa (r =3D 0.321= , p < 0.001), reflejando diferencias en eficiencia alimenticia entre COB= B 500 y ROSS 308 bajo las condiciones evaluadas.Las variables CO=E2=82=82 (r =3D 0.045, p =3D 0.324) y= NH=E2=82=83 (r =3D 0.038, p =3D 0.287) no alcanzaron significancia estad= =C3=ADstica. La densidad de aves (r =3D 0.062, p =3D 0.156) mostr=C3=B3 baj= a variabilidad operativa entre granjas (16.38 =C2=B1 0.17 aves/m=C2=B2), li= mitando su capacidad predictiva diferencial.3.3. Verificaci=C3=B3n de supuestos estad= =C3=ADsticosEl diagn=C3=B3s= tico exhaustivo de supuestos confirm=C3=B3 la adecuaci=C3=B3n del modelo de= RLM. La prueba de Shapiro-Wilk (W =3D 0.994, p =3D 0.089) valid=C3=B3 la n= ormalidad de la distribuci=C3=B3n de residuos, cumpliendo con el supuesto d= e errores normalmente distribuidos. La prueba de Durbin-Watson arroj=C3=B3 = un valor de 1.98, evidenciando independencia residual y ausencia de autocor= relaci=C3=B3n significativa (valor cercano a 2 indica independencia =C3=B3p= tima). La prueba de Breusch-Pagan (=CF=87=C2=B2 =3D 4.32, p =3D 0.115) conf= irm=C3=B3 homocedasticidad, descartando problemas de varianza heterog=C3=A9= nea de residuos. Los Factores de Inflaci=C3=B3n de la Varianza (VIF < 2.= 5 para todos los predictores) descartaron problemas sustanciales de multico= linealidad entre variables independientes.La Figura 1= complementa estos resultados mostrando la distribuci=C3=B3n de residuales = en funci=C3=B3n de los valores predichos de FCR. 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RhIpbBMA8/zCTyXUsUlkXSoVl0sy0KoED9QJBMOSgjcSgklTHKZJB/duEHvwDA6Pv/zz/8UPdHN= 7/7m11BBSLEwy3//0z/nwkuXSeqCVy69jPGUL5keFOJgEtNiFOVSGTuRQCjFw4cTGHaKnu5ONCH= w3CoTDx6Q7+hC03Ry2fR+NzimRcTBJCYmpiXE48uYmJiW8P8B4tG4sYELJFUAAAAASUVORK5CYI= I=3D" width=3D"275" height=3D"214" alt=3D"" /> Los punto= s se dispersan aleatoriamente alrededor de la l=C3=ADnea de residual cero (= l=C3=ADnea s=C3=B3lida negra), sin evidenciar patrones sistem=C3=A1ticos de= curvatura o heterocedasticidad. La l=C3=ADnea suavizada (morada) permanece= pr=C3=B3xima a cero en todo el rango de predicci=C3=B3n, y el =C3=A1rea so= mbreada (intervalo de confianza) mantiene amplitud constante, confirmando l= a estabilidad de la varianza residual. La diferenciaci=C3=B3n por fase prod= uctiva (semanas 1-4 en azul, semanas 5-7 en naranja) muestra distribuci=C3= =B3n equilibrada en ambos per=C3=ADodos, con la mayor=C3=ADa de los residua= les contenidos dentro del rango de =C2=B10.1 unidades de FCR, validando vis= ualmente la ausencia de sesgo y el cumplimiento de los supuestos de regresi= =C3=B3n lineal. = 3.4. Modelo de RLM finalEl modelo final explic=C3=B3 77.4% de la varianza total del FCR acumulad= o (R=C2=B2 =3D 0.774; R=C2=B2 ajustado =3D 0.773), evidenciando excelente c= apacidad explicativa. El an=C3=A1lisis de varianza confirm=C3=B3 significan= cia global altamente robusta (F(6,929) =3D 158.3, p < 0.001), validando = la utilidad conjunta de los predictores incluidos. El error est=C3=A1ndar r= esidual fue de 0.107 puntos de FCR.La Tabla 4 muestra= que cuatro predictores fueron altamente significativos (p < 0.001). La = temperatura exhibi=C3=B3 efecto negativo sustancial (=CE=B2 =3D -0.0445, p = < 0.001), indicando que cada incremento de 1=C2=B0C reduce el FCR en 0.0= 445 puntos. La fase fisiol=C3=B3gica mostr=C3=B3 el mayor impacto absoluto = (=CE=B2 =3D 0.198, p < 0.001), reflejando el deterioro natural de la efi= ciencia de conversi=C3=B3n al avanzar hacia semanas finales. La humedad pre= sent=C3=B3 efecto positivo moderado (=CE=B2 =3D 0.00858, p < 0.001). La = l=C3=ADnea gen=C3=A9tica mostr=C3=B3 diferencia significativa (=CE=B2 =3D 0= .072, p < 0.001), con ROSS 308 exhibiendo FCR superior a COBB 500 bajo l= as condiciones evaluadas. CO=E2=82=82 (=CE=B2 =3D 0.000017, p =3D 0.879) y = NH=E2=82=83 (=CE=B2 =3D 0.000655, p =3D 0.742) no alcanzaron significancia = estad=C3=ADstica. Tabla 4Coeficiente del modelo de regresi=C3=B3n para FCR</s= pan><table style=3D"width:100%; margin-bottom:0pt; padding:0pt; border-= collapse:collapse"><tr><td style=3D"width:30.12%; border-top:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Predictor</td><td style=3D"width:24.2= 8%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"= >=CE=92</= td><td style=3D"width:22.18%; border-top:0.75pt solid #000000; padding:0pt = 5.4pt; vertical-align:top">p-valor</td><td style=3D"width:23.42%; border-top:0.75pt = solid #000000; padding:0pt 5.4pt; vertical-align:top">IC 95%</td></tr><tr><td style= =3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Intercepto</td><td style=3D"width:24.28%; padd= ing:0pt 5.4pt; vertical-align:top">1,63= 1</td><td style=3D"width:22.18%; padding:0pt 5.4pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'"><0,001</td><td st= yle=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[1,400; 1,862]</td></tr><tr><td style=3D"wi= dth:30.12%; padding:0pt 5.4pt; vertical-align:top">Temperatura (=C2=B0C)</td><td style=3D"width:24.28%;= padding:0pt 5.4pt; vertical-align:top">-0,0445</td><td style=3D"width:22.18%; padding:0pt 5.4pt; verti= cal-align:top"><0,001</td= ><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[-0,049; -0,040]</td></tr><tr><td st= yle=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Humedad relativa (%)</td><td style=3D"width= :24.28%; padding:0pt 5.4pt; vertical-align:top">0,00858</td><td style=3D"width:22.18%; padding:0pt 5.4p= t; vertical-align:top"><0,001= </td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">= [0,0067; 0,0105]</td></tr><t= r><td style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Ave madura</td><td style=3D"width:2= 4.28%; padding:0pt 5.4pt; vertical-align:top">0,198</td><td style=3D"width:22.18%; padding:0pt 5.4pt; v= ertical-align:top"><0,001= </td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[0,176; 0,220]</td></tr><tr><td = style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">L=C3=ADnea gen=C3=A9tica</td><td style=3D= "width:24.28%; padding:0pt 5.4pt; vertical-align:top">0,072</td><td style=3D"width:22.18%; padding:0pt = 5.4pt; vertical-align:top"><0,001</s= pan></td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:t= op">[0,058; 0,086]</td></tr>= <tr><td style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">CO=E2=82=82 (ppm)</td><td style= =3D"width:24.28%; padding:0pt 5.4pt; vertical-align:top">0,000017</td><td style=3D"width:22.18%; paddin= g:0pt 5.4pt; vertical-align:top">0,879<= /span></td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align= :top"><spa= n style=3D"font-family:'Times New Roman'">[-0,0002; 0,0002]</td>= </tr><tr><td style=3D"width:30.12%; border-bottom:0.75pt solid #000000; pad= ding:0pt 5.4pt; vertical-align:top">NH= =E2=82=83 (ppm)</td><td style=3D"width:24.28%; border-bottom:0.7= 5pt solid #000000; padding:0pt 5.4pt; vertical-align:top">0,000655</td><td style=3D"width:22.18%; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">0,742</td><td style=3D"width:23.4= 2%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">[-0,0033; 0,0046]</td></= tr></table> La Ecuaci=C3=B3n 3 predictiva final del modelo se define de la siguiente= manera:= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACo= AAAAgCAYAAABkWOo9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAoFJRE= FUWIXtlz1LI1EYRk+WBRFtgwQErf0gBLHUOCqiYGFh41+wswoINoJYKCJoYSOiQtIIYisWIgbUQ= o0oQghCIvEDEUQ0+MU8WxkY4y4zs7O7uMyBae77vMzhDvedmYAk8QX49q8F7OKLeo0v6jW+qNf8= 36IXFxfEYjGur6+RRCKRoL29ndbWVhKJRDE3NjZGMpn0xlQOOT09VSQS0crKikzT1MLCggDLtbu= 7K0nKZDIyDEPxeNzpbUpwJHp3d6eWlhZNT09Lkm5ubrS8vKxCoaBcLqfe3l4Bmp+fL/bs7+8L0M= 7Ozt8TnZmZEaDz83NJ0tvbm6U+Pj4uQNvb25b1/v5+dXd3l+T/iOjLy4vq6+sVDodlmqalVigUt= L6+rpqaGk1MTJT0Tk1NCdDe3p5rUduH6ezsjJOTExoaGggEApba7OwsXV1dZLNZ4vE4h4eHlnp1= dTUAqVTK9VmyLXp7ewtAZWVlSW1wcJCNjQ2i0SgHBwcMDQ1Z6uXl5QBcXl66Fv1uN/i+ix93E6C= iooKOjg7q6uqIRqNsbm7y/PxMWVkZAKZpAqDf+PS1LRoMBgF4fHz8aSYUCtHT00NjY2NREuDp6Q= mAqqoqt572H31tbS2RSITj4+Pi2uLiIoZhsLa2xsPDA7lcjlQqxcjIiKU3n88DEA6HXYs6Gk9zc= 3MClM1mJUnJZFJ9fX0KhUJqbm7W8PCw0ul0Sd/AwIA6Ozv1+vrq+tQ7Er2/v1dbW5smJydt9xwd= HX06W53i+BWaTqfV1NSk1dXVknn6WdYwDC0tLbkWfMexqCTl83nFYjFdXV39Mjc6OqqtrS1XYh8= JSP7vsqf4ol7ji3qNL+o1X0b0B7r3N/AN7Y34AAAAAElFTkSuQmCC" width=3D"42" height= =3D"32" alt=3D"" style=3D"margin-top:2.03pt; margin-left:400.66pt; position= :absolute" />FCR =3D 1,631=E2=88=92 0,0445 =E2=8B=85 Temperatura + 0,00858 <span= style=3D"font-family:'Cambria Math'; font-style:italic">=E2=8B=85<s= pan style=3D"font-family:'Times New Roman'; font-style:italic"> Humedad + 0= ,198 = =E2=8B=85 Ave_madura + 0,072 =E2=8B=85 L=C3=ADnea_gen=C3=A9ticaDonde temperatura se expresa en =C2=B0C, humedad en= %, Ave_madura es una variable binaria (0 =3D semanas 1-4; 1 =3D semanas 5-= 7), y L=C3=ADnea_gen=C3=A9tica es una variable binaria (0 =3D COBB 500; 1 = =3D ROSS 308).La <sp= an style=3D"font-family:'Times New Roman'; font-weight:bold">Figura 2</span= > muestra los coeficientes de= l modelo de regresi=C3=B3n, cuantificando el impacto de cada variable sobre= el FCR. Las barras rojas indican factores que aumentan el FCR (menor efici= encia), mientras que las verdes lo reducen (mayor eficiencia).<p= style=3D"text-align:justify; line-height:115%; font-size:12pt">La fase productiva presenta el mayor efe= cto, con un incremento de +0.198 unidades al pasar de las semanas 1-4 a las= semanas 5-7, reflejando la disminuci=C3=B3n natural de la eficiencia de co= nversi=C3=B3n con la edad. La l=C3=ADnea gen=C3=A9tica ROSS 308 registra un= FCR superior en +0.072 unidades respecto a COBB 500, se=C3=B1alando difere= ncias en el potencial gen=C3=A9tico de conversi=C3=B3n bajo condiciones tro= picales.La temperatura ambi= ental constituye la variable de control m=C3=A1s influyente, con un coefici= ente de -0.044, indicando que cada incremento de 1=C2=B0C mejora la eficien= cia alimenticia, posiblemente al reducir el gasto energ=C3=A9tico de termor= regulaci=C3=B3n dentro de la zona de confort t=C3=A9rmico. La humedad relat= iva muestra un efecto m=C3=ADnimo (+0.009 por cada 1%), con impacto limitad= o en comparaci=C3=B3n con las dem=C3=A1s variables.Estos resultados destacan que, si bien los factores= biol=C3=B3gicos (fase y gen=C3=A9tica) ejercen la mayor influencia sobre e= l FCR, las variables ambientales monitorizadas mediante IoT ofrecen oportun= idades tangibles de optimizaci=C3=B3n productiva mediante el control microc= lim=C3=A1tico preciso. 3.5. Validaci=C3=B3n y capacidad predictivaLa validaci=C3=B3n externa= con 186 observaciones independientes (20% del conjunto de datos) confirm= =C3=B3 robustez predictiva del modelo, alcanzando R=C2=B2 =3D 0.739 en dato= s no vistos durante el proceso de ajuste. Esta p=C3=A9rdida predictiva de 4= .5 puntos porcentuales (de 77.4% a 73.9%) evidencia excelente capacidad de = generalizaci=C3=B3n y estabilidad del modelo ante nuevos datos.<= p class=3D"NoSpacing" style=3D"text-align:justify; line-height:115%; font-s= ize:12pt"> <p= class=3D"NoSpacing" style=3D"text-align:justify; line-height:115%; font-si= ze:12pt">Las m=C3=A9tricas de= error absoluto mostraron alta precisi=C3=B3n operativa: MAE =3D 0.088 y RM= SE =3D 0.109 puntos de FCR. Estos valores representan aproximadamente 7% y = 9% respectivamente de la desviaci=C3=B3n est=C3=A1ndar observada en FCR (DE= =3D 0.22), indicando utilidad pr=C3=A1ctica para aplicaciones de monitoreo= en granjas automatizadas.La Figura 2 presenta la relaci=C3=B3n entre valores predichos y observados de FCR,= mostrando un ajuste satisfactorio del modelo de regresi=C3=B3n desarrollad= o. Los puntos se distribuyen pr=C3=B3ximos a la l=C3=ADnea de predicci=C3= =B3n perfecta (y =3D x, l=C3=ADnea gris), mientras que la l=C3=ADnea roja y= su intervalo de confianza del 95% evidencian la capacidad del modelo para = estimar el =C3=ADndice de conversi=C3=B3n alimenticia a partir de variables= ambientales. <= /p>Figura 2</p= >Relaci=C3=B3n entre= valores predichos y observados de FCR<img src=3D"data:image/png;base64,iV= BORw0KGgoAAAANSUhEUgAAARMAAAD0CAYAAAC4n8I2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzA= AAOxAAADsQBlSsOGwAAIABJREFUeJzsvWeQXdeZrvesHU/qjM6N0AiNDIIAwQTmJJKSKJIapdEd= zdXIdzQ1vjX3lstlV025ylUul8v+Yf/wretbnqnxndFMaTQaBVIiKTGKJEgCBJFjowF0o3OOJ+2= 01vKPfdAkSBAkwCZFCvv5QzR79z777D777bW+8H5Ca61J+IzRzExNYqZrqMm6lz1CKYnnBWQy6S= V5xSgMiLRByrGu+RyeV6ZU8qitq8UQ4rLHKBkRKoFrG0R+GcNOYxiXP/ZKKBVRKPlU57LXfL0Jn= y0iEZPfD0ophBCID3koQaM1V/j+1RH/mgWf5HQXPyofdU3xK136r2t4tSV9/wmfPomYJCQkLAnG= 7/sCEhIS/jBIxCQhIWFJuPZo3HtIdkoJCQmfSEy01kgpUUot1fUkJCR8QfnEYlIul/F9P4m6E2d= 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Edbzf77734XM5GfHKa8EDSV6cC1pISfy0uS3EJCQk5JPntpiADQkJ+eQJxSQkJOS6EIpJSEjIdS= EUk5CQkOtCKCYhISHXhVBMQkJCQkJCQm4e/n97KXscX4rwyAAAAABJRU5ErkJggg=3D=3D" wid= th=3D"275" height=3D"244" alt=3D"" /> El an=C3=A1lisis por l=C3=ADnea gen=C3=A9tica (COBB 5= 00 y ROSS 308) no revela diferencias sustanciales en el patr=C3=B3n predict= ivo, validando la aplicabilidad del modelo para ambas estirpes. La estratif= icaci=C3=B3n temporal muestra que las semanas 1-4 (azul) presentan menor di= spersi=C3=B3n que las semanas 5-7 (naranja), sugiriendo mayor sensibilidad = del modelo en fases tempranas cuando las variables ambientales ejercen mayo= r impacto sobre el desempe=C3=B1o productivo. La distribuci=C3=B3n homog=C3= =A9nea de residuales confirma la ausencia de sesgo sistem=C3=A1tico y respa= lda la validez del enfoque de regresi=C3=B3n lineal m=C3=BAltiple para cond= iciones de producci=C3=B3n av=C3=ADcola en clima tropical h=C3=BAmedo.</spa= n> 4. Discusi=C3=B3n</span= ><spa= n style=3D"font-family:'Times New Roman'">Los resultados obtenidos demuestr= an consistentemente que el modelo de regresi=C3=B3n lineal m=C3=BAltiple de= sarrollado exhibe capacidad predictiva robusta y operativamente =C3=BAtil p= ara estimar el factor de conversi=C3=B3n alimenticia semanal en granjas av= =C3=ADcolas automatizadas bajo condiciones tropicales h=C3=BAmedas. El R=C2= =B2 de 0,774 en entrenamiento y 0,739 en validaci=C3=B3n externa, acompa=C3= =B1ado de errores de predicci=C3=B3n bajos (MAE =3D 0,088; RMSE =3D 0,109),= supera los valores t=C3=ADpicamente reportados en la literatura para model= os similares en contextos comerciales reales. Investigaciones previas en gr= anjas brasile=C3=B1as reportaron R=C2=B2 entre 0,65-0,72 para modelos de pr= edicci=C3=B3n de FCR utilizando variables ambientales, mientras que estudio= s en M=C3=A9xico alcanzaron R=C2=B2 de 0,69-0,75 (Freitas et al., 2025). La capacidad predict= iva de nuestro modelo se mantuvo estable a trav=C3=A9s de diferentes ciclos= productivos, fases fisiol=C3=B3gicas y condiciones estacionales, evidencia= ndo robustez para implementaci=C3=B3n operativa inmediata.El efecto negativo sustancial de la temperat= ura ambiental sobre el FCR (-0,0445 puntos por =C2=B0C) constituye un halla= zgo que requiere interpretaci=C3=B3n cuidadosa dentro del contexto tropical= espec=C3=ADfico. Dentro del rango de temperaturas observado (22,9-32,0=C2= =B0C), incrementos t=C3=A9rmicos moderados parecen favorecer la eficiencia = alimenticia, posiblemente debido a reducciones en los requerimientos energ= =C3=A9ticos de mantenimiento para termorregulaci=C3=B3n. Este hallazgo cont= rasta con estudios realizados en climas templados donde temperaturas superi= ores a 28=C2=B0C generalmente deterioran el FCR mediante mecanismos de estr= =C3=A9s t=C3=A9rmico bien documentados (Oke et al., 2024). La aparente discrepancia sugiere t= res explicaciones plausibles no mutuamente excluyentes.Primero, las aves criadas bajo condiciones tro= picales h=C3=BAmedas podr=C3=ADan desarrollar aclimataci=C3=B3n fisiol=C3= =B3gica que modifica sus rangos =C3=B3ptimos de termoneutralidad. La exposi= ci=C3=B3n cr=C3=B3nica a temperaturas elevadas puede inducir adaptaciones m= etab=C3=B3licas y conductuales que mejoran la tolerancia t=C3=A9rmica. Segu= ndo, el rango de temperaturas observado (22,9-32,0=C2=B0C) podr=C3=ADa ubic= arse mayoritariamente dentro o ligeramente por encima de la zona de termone= utralidad para pollos en fase de crecimiento acelerado (t=C3=ADpicamente 18= -26=C2=B0C), donde incrementos moderados efectivamente reducen gastos energ= =C3=A9ticos de mantenimiento sin alcanzar umbrales de estr=C3=A9s t=C3=A9rm= ico severo. Tercero, podr=C3=ADa existir confusi=C3=B3n parcial con variabl= es estacionales no capturadas: per=C3=ADodos de mayor temperatura (=C3=A9po= ca seca) podr=C3=ADan coincidir con mejor calidad de cama, menor carga micr= obiana y condiciones generales m=C3=A1s favorables para el desempe=C3=B1o a= v=C3=ADcola.La magnitud del= coeficiente indica que variaciones de 2-3=C2=B0C en la temperatura promedi= o semanal comunes entre =C3=A9pocas secas y lluviosas en Santo Domingo de l= os Ts=C3=A1chilas pueden traducirse en diferencias de 0,09-0,13 puntos en e= l FCR, representando impactos econ=C3=B3micos sustanciales considerando que= el alimento constituye 60-70% de los costos operativos (Muyulema</span= > et al., 2020). Esta sensibi= lidad econ=C3=B3mica subraya la importancia cr=C3=ADtica del control ambien= tal automatizado en condiciones tropicales.El efecto positivo de la humedad relativa sobre el FCR (0,0= 0858 puntos por %) confirma los desaf=C3=ADos particulares de la avicultura= en ambientes tropicales h=C3=BAmedos, consistente con hallazgos previos en= condiciones similares (Hidalgo et al., 2024). El coeficiente estimado indica que, por cada a= umento de 10 puntos porcentuales en la humedad relativa dentro del rango ob= servado, el FCR se incrementa en aproximadamente 0,086 puntos lo que implic= a menor eficiencia alimenticia. Este efecto probablemente opera a trav=C3= =A9s de m=C3=BAltiples mecanismos fisiol=C3=B3gicos y ambientales interrela= cionados: estr=C3=A9s t=C3=A9rmico por reducci=C3=B3n de la capacidad de p= =C3=A9rdida de calor evaporativo, deterioro de la calidad f=C3=ADsica de la= cama con consecuentes problemas podales y dermatitis de contacto, y potenc= ial aumento de la carga microbiana ambiental que compromete la salud intest= inal y respiratoria.El hech= o de que la humedad relativa emergiera como predictor significativo a pesar= de la relativa homogeneidad de los valores observados (CV =3D 5,1%, rango = 68,5-89,8%) resalta su importancia cr=C3=ADtica en estos sistemas. Los valo= res promedio de 82,58% exceden consistentemente las gu=C3=ADas t=C3=A9cnica= s =C3=B3ptimas internacionales (60-75%), reflejando la limitaci=C3=B3n f=C3= =ADsica fundamental de controlar humedad en climas tropicales mediante sist= emas de ventilaci=C3=B3n convencionales sin deshumidificaci=C3=B3n activa, = tecnolog=C3=ADa a=C3=BAn no econ=C3=B3micamente viable para producci=C3=B3n= comercial en la regi=C3=B3n.La fase fisiol=C3=B3gica present=C3=B3 el mayor efecto individual sobre e= l FCR, al pasar de las semanas 1=E2=80=934 a 5=E2=80=937, el FCR aumenta en= 0,198 puntos, en l=C3=ADnea con la menor eficiencia alimenticia propia del= cierre del ciclo productivo. Este resultado es consistente con el conocimi= ento fisiol=C3=B3gico establecido: a medida que las aves maduran, la compos= ici=C3=B3n de la ganancia de peso cambia progresivamente hacia mayor deposi= ci=C3=B3n de grasa (energ=C3=A9ticamente costosa) en detrimento de prote=C3= =ADna muscular, los requerimientos energ=C3=A9ticos de mantenimiento aument= an proporcionalmente m=C3=A1s que el potencial de ganancia, y pueden manife= starse reducciones en la capacidad digestiva y de absorci=C3=B3n intestinal= (Quintana-Ospina = et al., 2023).La magnitud d= e este efecto (0,198 puntos) resalta la importancia estrat=C3=A9gica de imp= lementar pr=C3=A1cticas de manejo diferenciadas seg=C3=BAn la fase producti= va, particularmente concerniente a densidad de alojamiento efectiva, estrat= egias de ventilaci=C3=B3n, y formulaci=C3=B3n alimenticia adaptada a los ca= mbios en requerimientos nutricionales. La capacidad del modelo de mantener = precisi=C3=B3n predictiva equivalente en ambas fases (demostrada en = Figura 1</s= pan>) confirma su utilidad op= erativa a lo largo del ciclo productivo completo.Las diferencias gen=C3=A9ticas entre l=C3=ADneas tamb= i=C3=A9n emergieron como predictores significativos, con la l=C3=ADnea ROSS= 308 mostrando un FCR 0,072 puntos superior al COBB 500 bajo condiciones co= mparables. Esta diferencia, aunque moderada en magnitud, es consistentement= e reportada en la literatura t=C3=A9cnica comercial y refleja distintos equ= ilibrios establecidos por los programas de selecci=C3=B3n gen=C3=A9tica: RO= SS 308 t=C3=ADpicamente exhibe velocidad de crecimiento ligeramente superio= r pero eficiencia alimenticia marginalmente inferior comparada con COBB 500= (Gholami et al., = 2020). Desde una perspectiva operativa, este hallazgo justifica el establec= imiento de metas de FCR diferenciadas por l=C3=ADnea gen=C3=A9tica en siste= mas de monitoreo y la consideraci=C3=B3n expl=C3=ADcita de estas diferencia= s en an=C3=A1lisis econ=C3=B3micos de selecci=C3=B3n de gen=C3=A9tica.</spa= n><sp= an style=3D"font-family:'Times New Roman'">La ausencia de significancia est= ad=C3=ADstica de CO=E2=82=82 y NH=E2=82=83 como predictores dentro de los r= angos operativos observados constituye un hallazgo relevante desde la persp= ectiva del manejo pr=C3=A1ctico. Las concentraciones promedio de CO=E2=82= =82 (589,25 ppm) y NH=E2=82=83 (18,46 ppm) se mantuvieron consistentemente = por debajo de los umbrales cr=C3=ADticos establecidos en la literatura para= afectaci=C3=B3n del desempe=C3=B1o productivo (>3,000 ppm y >25 ppm,= respectivamente). Esto sugiere que, bajo las condiciones de ventilaci=C3= =B3n mec=C3=A1nica y manejo de cama prevalentes en las granjas estudiadas c= aracterizadas por sistemas de t=C3=BAnel con alta capacidad de renovaci=C3= =B3n de aire estas variables no constituyeron limitantes principales para l= a eficiencia alimenticia durante el per=C3=ADodo de estudio.Sin embargo, es importante destacar enf= =C3=A1ticamente que este resultado no minimiza la importancia de mantener a= decuados controles de calidad de aire, particularmente considerando los efe= ctos documentados de estos gases sobre la salud respiratoria, el bienestar = animal y la incidencia de enfermedades respiratorias a m=C3=A1s largo plazo= . La ausencia de efecto detectado probablemente refleja el =C3=A9xito de la= s pr=C3=A1cticas de manejo implementadas m=C3=A1s que la irrelevancia intr= =C3=ADnseca de estos par=C3=A1metros.La capacidad predictiva del modelo compara favorablemente con est= udios utilizando t=C3=A9cnicas de machine learning m=C3=A1s complejas. A di= ferencia de algoritmos que alcanzan exactitudes superiores al 98% mediante = optimizaci=C3=B3n exhaustiva de hiperpar=C3=A1metros (Vilema<spa= n style=3D"font-family:'Times New Roman'"> et al., 2022), pero carecen de i= nterpretabilidad directa, nuestro modelo mantiene un equilibrio =C3=B3ptimo= entre capacidad predictiva (R=C2=B2 =3D 0,774) y transparencia operativa. = Esta caracter=C3=ADstica es particularmente valiosa en contextos donde el p= ersonal t=C3=A9cnico debe justificar intervenciones de manejo espec=C3=ADfi= cas ante gerencia operativa, y donde la comprensi=C3=B3n de la direcci=C3= =B3n y magnitud de los efectos facilita la toma de decisiones fundamentada = (Granda et al., 20= 23).Desde la perspectiva de= implementaci=C3=B3n pr=C3=A1ctica, el modelo ofrece ventajas significativa= s. La Ecuaci=C3=B3n 3 resu= ltante FCR =3D 1,631 - 0,0445=C3=97Temperatura + 0,00858=C3=97Humedad + 0,1= 98=C3=97Fase + 0,072=C3=97ROSS puede programarse f=C3=A1cilmente en hojas d= e c=C3=A1lculo o dashboards operativos, requiriendo =C3=BAnicamente insumos= rutinariamente disponibles en granjas automatizadas. La simplicidad comput= acional lo hace accesible para empresas medianas y peque=C3=B1as sin infrae= structura especializada para algoritmos complejos. La naturaleza lineal de = las relaciones facilita sustancialmente la comunicaci=C3=B3n de hallazgos y= la justificaci=C3=B3n de intervenciones basadas en pron=C3=B3sticos genera= dos.Las implicaciones econ= =C3=B3micas son sustanciales. Considerando que variaciones de 0,1 puntos en= FCR pueden traducirse en diferencias de 3-5% en rentabilidad en operacione= s medianas, la capacidad de predecir y gestionar proactivamente este indica= dor tiene impacto directo en sostenibilidad financiera. La implementaci=C3= =B3n del modelo como sistema de alerta temprana permitir=C3=ADa intervencio= nes correctivas ajustes en ventilaci=C3=B3n, densidad, estrategia alimentic= ia antes de que las ineficiencias se consoliden en sobrecostos irreversible= s.4.1.</= span> Limitaciones del estudioEs importante contextualizar las limitaciones= . El modelo fue desarrollado y validado espec=C3=ADficamente para condicion= es tropicales h=C3=BAmedas de Ecuador y podr=C3=ADa requerir recalibraci=C3= =B3n para otros contextos clim=C3=A1ticos. El rango de valores observado pa= ra gases ambientales fue relativamente estrecho debido a las buenas pr=C3= =A1cticas de ventilaci=C3=B3n, limitando potencialmente la detecci=C3=B3n d= e efectos que podr=C3=ADan manifestarse en condiciones m=C3=A1s extremas. E= l modelo no incorpora variables relacionadas con salud aviar espec=C3=ADfic= a, calidad nutricional del alimento, o factores de estr=C3=A9s agudo, que p= odr=C3=ADan aportar varianza explicativa adicional. La generalizaci=C3=B3n = a sistemas con diferente nivel de automatizaci=C3=B3n o capacidad de contro= l ambiental requiere evaluaci=C3=B3n espec=C3=ADfica.Los hallazgos abren varias l=C3=ADneas de investi= gaci=C3=B3n. Ser=C3=ADa valioso explorar interacciones no lineales mediante= splines o transformaciones polin=C3=B3micas, particularmente para temperat= ura en rangos extremos. La incorporaci=C3=B3n de variables adicionales como= velocidad de viento, calidad de cama medida objetivamente, e indicadores d= e salud del lote podr=C3=ADa mejorar la capacidad predictiva. El desarrollo= de sistemas de integraci=C3=B3n en tiempo real con plataformas IoT existen= tes facilitar=C3=ADa la implementaci=C3=B3n operativa. Estudios longitudina= les evaluando el impacto econ=C3=B3mico de la implementaci=C3=B3n del model= o en condiciones comerciales reales permitir=C3=ADan cuantificar m=C3=A1s p= recisamente su valor agregado y retorno de inversi=C3=B3n. 5. = 0; ConclusionesEl m= odelo de regresi=C3=B3n lineal m=C3=BAltiple desarrollado demostr=C3=B3 cap= acidad predictiva robusta y operativamente =C3=BAtil para estimar el FCR se= manal en granjas av=C3=ADcolas automatizadas bajo condiciones tropicales h= =C3=BAmedas. Los indicadores de desempe=C3=B1o alcanzados (R=C2=B2 =3D 0,77= 4 en entrenamiento; R=C2=B2 =3D 0,739 en validaci=C3=B3n externa; MAE =3D 0= ,088; RMSE =3D 0,109) superan los umbrales t=C3=ADpicamente considerados ac= eptables para aplicaciones comerciales y se mantienen estables a trav=C3=A9= s de diferentes ciclos productivos y condiciones estacionales, evidenciando= robustez para implementaci=C3=B3n operativa inmediata.Las variables ambientales monitorizadas contin= uamente fueron predictores significativos del FCR (p < 0,001). En el ran= go observado (22,9=E2=80=9332,0 =C2=B0C), la temperatura se asoci=C3=B3 inv= ersamente con el FCR (=E2=88=920,0445 puntos/=C2=B0C), coherente con una me= nor demanda de termorregulaci=C3=B3n. La humedad relativa mostr=C3=B3 asoci= aci=C3=B3n positiva (+0,00858 puntos/%), reflejando los retos del ambiente = tropical h=C3=BAmedo. En conjunto, los resultados obtenidos en granjas auto= matizadas en operaci=C3=B3n resaltan la importancia del control ambiental a= utomatizado para optimizar la eficiencia alimenticia.Las caracter=C3=ADsticas del lote incidieron sign= ificativamente en el FCR. La fase final (semanas 5=E2=80=937) increment=C3= =B3 el FCR en 0,198 puntos respecto de la fase inicial (semanas 1=E2=80=934= ), en concordancia con el mayor costo energ=C3=A9tico de mantenimiento y lo= s cambios en la composici=C3=B3n del crecimiento al cierre del ciclo. La l= =C3=ADnea gen=C3=A9tica ROSS 308 present=C3=B3 un FCR 0,072 puntos superior= a COBB 500 bajo condiciones comparables, lo que sugiere diferencias sistem= =C3=A1ticas entre perfiles de crecimiento y eficiencia. Estos resultados re= spaldan metas de FCR diferenciadas por fase productiva y gen=C3=A9tica.</sp= an><s= pan style=3D"font-family:'Times New Roman'">El modelo representa una herram= ienta operativamente viable para gesti=C3=B3n preventiva del FCR en granjas= comerciales, ofreciendo un balance =C3=B3ptimo entre capacidad predictiva,= simplicidad computacional y transparencia interpretativa. A diferencia de = algoritmos de machine learning tipo "caja negra", la ecuaci=C3=B3n de regre= si=C3=B3n lineal desarrollada puede implementarse f=C3=A1cilmente en hojas = de c=C3=A1lculo o dashboards operativos existentes, requiriendo =C3=BAnicam= ente insumos rutinariamente disponibles en granjas automatizadas. Esta acce= sibilidad facilita su adopci=C3=B3n por empresas medianas y peque=C3=B1as s= in infraestructura (galpones autom=C3=A1ticos) , democratizando el acceso a= herramientas anal=C3=ADticas avanzadas.La implementaci=C3=B3n del modelo como sistema de alerta tempr= ana permitir=C3=ADa intervenciones correctivas proactivas (ajustes en venti= laci=C3=B3n, densidad, estrategia alimenticia) antes de que las ineficienci= as se consoliden en sobrecostos irreversibles. Considerando que el alimento= representa 60-70% de costos operativos y que variaciones de 0,1 puntos en = FCR impactan 3-5% la rentabilidad, la capacidad de predecir y gestionar est= e indicador tiene consecuencias directas en sostenibilidad financiera. Se r= ecomienda la implementaci=C3=B3n piloto en granjas seleccionadas para valid= ar utilidad operativa y la integraci=C3=B3n futura con dashboards IoT en ti= empo real para maximizar su valor pr=C3=A1ctico en la toma de decisiones di= arias. </= p>6. Conflicto de in= teresesLos autores declaran= que no existe conflicto de intereses en relaci=C3=B3n con el art=C3=ADculo= presentado. Ninguna de las instituciones o granjas involucradas en la gene= raci=C3=B3n de los datos condiciono el an=C3=A1lisis, la interpretaci=C3=B3= n de resultados ni la redacci=C3=B3n del manuscrito.7. Declaraci=C3=B3n de contribuci= =C3=B3n de los autoresTodos= los autores contribuyeron significativamente en la elaboraci=C3=B3n del ar= t=C3=ADculo.8. Costos de financiamientoLa presente investigaci=C3=B3n fue financiada en su totalidad con recursos= propios de los autores. No se recibieron fondos externos de entidades p=C3= =BAblicas ni privadas. Las granjas participantes proporcionaron acceso a da= tos operativos bajo acuerdos de confidencialidad, pero no financiaron el an= =C3=A1lisis estad=C3=ADstico ni la elaboraci=C3=B3n del manuscrito.<= /p>9. <span= style=3D"font-family:'Times New Roman'; font-weight:bold">Referencias Bibl= iogr=C3=A1ficasAdli, D. 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Visionario Digital</s= pan>, 4(1), 43=E2=80=9366. </= span><a href=3D"https://doi.org/10.33262/visionariodigital.v4i1.1089" style= =3D"text-decoration:none">https://doi.org/10.33262/visionariodigital.v4i1.1089</span= ></a> <div style= =3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAA= BCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSI= ntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740"= height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; p= osition:absolute" /> &= #xa0; Nombre 1</spa= n> </div></div> <div class=3D"Section_3"><div st= yle=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2= wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8f= ExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4= eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQ= 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=3D"text-decoration:none">https://orcid.org/0009-0= 000-3612-816X</a></td><td style=3D"padding:0pt; vertical-align:t= op"></td></tr><tr><td style=3D"width:3.25pt; padding:0pt 5.4pt; vertical-al= ign:top"> </spa= n></td><td colspan=3D"4" style=3D"width:370.7pt; padding:0pt 5.4pt; ver= tical-align:top">Un= iversidad Bolivariana del Ecuador (UBE), Dur=C3=A1n, Ecuador.Maestr=C3=ADa en Gesti= =C3=B3n y An=C3=A1lisis de Datos con menci=C3=B3n en Inteligencia de Negoci= os <a href=3D"mailto:eariofrion= @ube.edu.ec" style=3D"text-decoration:none">eario= frion@ube.edu.ec</a> </td></tr><tr><td style=3D"width:3.25pt; paddin= g:0pt 5.4pt; vertical-align:top">2</td><td style=3D"width:171.95pt; padding:0pt 5.4= pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Iv=C3=A1n Patricio Montaleza Qu= izhpe</td><td style=3D"width:10.5pt; padding:0pt 5.4pt; vertical= -align:top"><img src=3D"data:image/png;base64,iVBORw= 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Inteligencia de Negocios<a href=3D"mailto:ipmontalezaq@ube= .edu.ec" style=3D"text-decoration:none">ipmontale= zaq@ube.edu.ec</a></td><td style=3D"padding:0pt; vertical-align:to= p"></td></tr><tr><td style=3D"width:3.25pt; padding:0pt 5.4pt; vertical-ali= gn:top">3</p= ></td><td style=3D"width:171.95pt; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-left:7.1pt; margin-bottom:0pt; text-indent:-7.1pt; text-a= lign:justify; line-height:115%; font-size:10pt">Glen Freddy Robayo Cabrera</td><td style=3D"w= idth:10.5pt; padding:0pt 5.4pt; vertical-align:top">= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNi= R0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUU= hr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ou= iGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO= 5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x= 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t=3D"20" alt=3D"" style=3D"margin-top:1.05pt; margin-left:-0.75pt; position= :absolute" /> </s= pan></td><td style=3D"width:166.35pt; padding:0pt 5.4pt; vertical-align= :top"><a href=3D"https://o= rcid.org/0009-0006-9195-7423" style=3D"text-decoration:none">https://orcid.org/0009-0006-9195-7423</a></td><td sty= le=3D"padding:0pt; vertical-align:top"></td></tr><tr><td style=3D"width:3.2= 5pt; padding:0pt 5.4pt; vertical-align:top"> </td><td colspan=3D"4" style=3D"wi= dth:370.7pt; padding:0pt 5.4pt; vertical-align:top">Universidad Bolivariana del Ecuador (UBE= ), Dur=C3=A1n, Ecuador.gfrobayoc@ube.edu= .ec </td></tr><tr style=3D"height:0pt"><td style=3D"= width:14.05pt"></td><td style=3D"width:182.75pt"></td><td style=3D"width:21= .3pt"></td><td style=3D"width:177.15pt"></td><td style=3D"width:0.3pt"></td= ></tr></table>&= #xa0;<table style=3D"width:436.35pt; margin-left:0.25pt; margin-= bottom:0pt; padding:0pt; border-collapse:collapse"><tr><td colspan=3D"3" st= yle=3D"width:156.4pt; padding:0pt 5.4pt; vertical-align:top"><a id= =3D"_Hlk92186133"> </a>= </td><td col= span=3D"2" style=3D"width:258.35pt; border-top:0.75pt solid #000000; paddin= g:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de Investigaci=C3=B3n Cient=C3= =ADfica y Tecnol=C3=B3gicaEnviado: <span= style=3D"font-family:'Times New Roman'; background-color:#ffff00">Revisado= : Aceptado: Publicado:= DOI: </td></tr><tr style= =3D"height:6.55pt"><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.= 4pt; vertical-align:top"> </= p> </td><td colspan=3D"2= " style=3D"width:258.35pt; border-bottom:0.75pt solid #000000; padding:0pt = 5.4pt; vertical-align:top"> </td></tr><tr><td style=3D"width:37= .8pt; padding:0pt 5.4pt; vertical-align:top"> <spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">C=C3=ADtese:</s= pan> </td><td styl= e=3D"width:1.55pt; padding:0pt 5.4pt; vertical-align:top"> <= /td><td colspan=3D"2" style=3D"width:358.55pt; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; 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ePDgwYOH28NtCW6cTifl5eVYLJZbD/QXBje3i79ijjKZjICAAHQ6nbuA04MHDx48ePDw13Fbghs= PHjx48ODBg4f/FDxLCR48ePDgwYOH/yo8wY0HDx48ePDg4b+KP7UVPDs72+3C7cGDBw8ePHjw8L= +BRqOhRYsWtzz+p4Kb77777qYqvB48ePDgwYMHD38X4eHhTJky5ZbHPQXFHjx48ODBg4f/Kjw1N= x48ePDgwYOH/yo8wY0HDx48ePDg4b8KT3DjwYMHDx48ePiv4v8DkrKy3KIORuIAAAAASUVORK5C= YII=3D" width=3D"567" height=3D"140" alt=3D"" /> <table style=3D"width:440.5pt; margin-le= ft:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr st= yle=3D"height:67.9pt"><td style=3D"width:75.55pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">Palabras claves: Avicultura de precisi=C3= =B3n, factor de conversi=C3=B3n alimenticia, regresi=C3=B3n lineal m=C3=BAl= tiple, eficiencia alimenticia, IoT, pollos de engorde, condiciones tropicales, granjas automatizadas.</= span></td><td colspan=3D"2" style=3D"width:4.4pt; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical= -align:top"> </td><td style= =3D"width:328.1pt; border-top:0.75pt solid #000000; border-bottom:0.75pt so= lid #000000; padding:0pt 5.4pt; vertical-align:top">Resumen Intr= oducci=C3=B3n: la indu= stria av=C3=ADcola opera en un entorno altamente competitivo, donde el Fact= or de Conversi=C3=B3n Alimenticia (FCR) representa entre el 60 y el 70% de = los costos operativos. Aunque las granjas automatizadas generan grandes vol= =C3=BAmenes de datos ambientales mediante sistemas IoT, su potencial suele = aprovecharse de manera limitada en la toma de decisiones. Objetivos:<spa= n style=3D"font-family:'Times New Roman'"> desarrollar un modelo predictivo= basado en regresi=C3=B3n lineal m=C3=BAltiple para estimar el FCR semanal = en granjas av=C3=ADcolas automatizadas bajo condiciones tropicales h=C3=BAm= edas del Ecuador. Metodolog=C3=ADa: estudio observacional longitudinal retrospectivo con 936 registro= s semanales (2022-2025) de siete granjas automatizadas. Se aplic=C3=B3 regr= esi=C3=B3n lineal m=C3=BAltiple con validaci=C3=B3n hold-out 80/20 estratif= icada, verificaci=C3=B3n de supuestos estad=C3=ADsticos y evaluaci=C3=B3n m= ediante R=C2=B2, MAE y RMSE. Resultados: el modelo demostr=C3=B3 capacidad predictiva robusta con R= =C2=B2 =3D 0,774 (entrenamiento) y R=C2=B2 =3D 0,739 (validaci=C3=B3n exter= na), acompa=C3=B1ado de MAE =3D 0,088 y RMSE =3D 0,109. Variables significa= tivas (p < 0,001) incluyeron temperatura ambiental (coeficiente =3D -0,0= 445 FCR/=C2=B0C), humedad relativa (+0,00858 FCR/%), fase fisiol=C3=B3gica = (+0,198 FCR para semanas 5-7) y l=C3=ADnea gen=C3=A9tica (+0,072 FCR para R= OSS 308). CO=E2=82=82 y NH=E2=82=83 no alcanzaron significancia estad=C3=AD= stica. Conclusi=C3=B3n: el= modelo evidencia capacidad predictiva operativa para gesti=C3=B3n preventi= va del FCR en condiciones tropicales h=C3=BAmedos, ofreciendo una herramien= ta interpretable de implementaci=C3=B3n inmediata que permite proyecciones = semanales confiables con implicaciones directas en rentabilidad mediante op= timizaci=C3=B3n de eficiencia alimenticia. =C3=81rea de estudio general: Ingenier=C3=ADa Agropecuaria. =C3=81rea de estudio = espec=C3=ADfica: Avicu= ltura de precisi=C3=B3n. Tipo de estudio: Art=C3=ADculo original.</td></tr><tr style=3D"he= ight:168.2pt"><td colspan=3D"2" style=3D"width:78.85pt; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:top">Keywords: Precision poultry, Feed Conversion Ratio, multiple linear regression, </p= >feed efficiency, IoT, </= span>broiler chickens= , tropical conditions, automated farms.</td><td style=3D"width:1= .15pt; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">= </td><td style=3D"width:328.1pt; border-top:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Abstract Introduct= ion: the poultry indus= try operates in a highly competitive environment, where feed costs represen= t between 60% and 70% of operating costs. Although automated farms generate= large volumes of environmental data through IoT systems, their potential i= s often underutilized in decision-making. Objectives: to develop a predictive model based on multiple= linear regression to estimate the weekly FCR in automated poultry farms un= der humid tropical conditions of Ecuador. Methodology: a retrospective longitudinal observational stu= dy was conducted with 936 weekly records (2022-2025) from seven automated f= arms. Multiple linear regression was applied with stratified hold-out valid= ation (80/20), assessment of statistical assumptions, and evaluation using = R=C2=B2, MAE, and RMSE. Results: the model demonstrated robust predictive capacity with R=C2=B2 =3D 0= .774 (training) and R=C2=B2 =3D 0.739 (external validation), accompanied by= MAE =3D 0.088 and RMSE =3D 0.109. Significant variables (p < 0.001) inc= luded ambient temperature (=CE=B2 =3D -0.0445 FCR units/=C2=B0C), relative = humidity (=CE=B2 =3D +0.00858 FCR units/%), physiological phase (=CE=B2 =3D= +0.198 FCR units for weeks 5-7), and genetic line (=CE=B2 =3D +0.072 FCR u= nits for ROSS 308). CO=E2=82=82 and NH=E2=82=83 did not reach statistical s= ignificance. Conclusion: t= he model demonstrates predictive capacity for preventive FCR management und= er humid tropical conditions, offering an interpretable tool for immediate = implementation that enables reliable weekly projections with direct implica= tions for profitability through optimization of feed efficiency. <sp= an style=3D"font-family:'Times New Roman'; font-weight:bold">General study = area: Agricultural Eng= ineering. Specific study area: Precision Poultry Farming. Type of study: Original article.</td></tr><tr style=3D"hei= ght:0pt"><td style=3D"width:86.35pt"></td><td style=3D"width:3.3pt"></td><t= d style=3D"width:11.95pt"></td><td style=3D"width:338.9pt"></td></tr></tabl= e><span= style=3D"font-weight:bold; color:#a5a5a5"> <div style=3D"c= lear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYA= AACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTE= BAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" heig= ht=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; positi= on:absolute" />&#x= a0; = Nombre = 1</p= > </div></div> <div class=3D"Section_2"><div style= =3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBD= AAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExc= iJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh= 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la competencia global y la necesidad de mejorar continuamente la eficien= cia productiva en un entorno de m=C3=A1rgenes estrechos (Oke<spa= n style=3D"font-family:'Times New Roman'"> et al., 2024). La maximizaci=C3= =B3n de la eficiencia biol=C3=B3gica y econ=C3=B3mica de cada lote de pollo= s de engorde se convierte en un imperativo estrat=C3=A9gico para la supervi= vencia empresarial.<a id=3D"_Hlk213271731">El Factor de Conversi=C3=B3n Alimenticia (FCR<s= pan style=3D"font-family:'Times New Roman'">) es uno de los indicadores m= =C3=A1s importantes para evaluar el rendimiento productivo en sistemas inte= nsivos. Entendido como la proporci=C3=B3n entre el alimento ingerido (kg) y= el peso vivo ganado (kg), el FCR es un indicador que resume muchos factore= s de la producci=C3=B3n. Su significancia econ=C3=B3mica va m=C3=A1s all=C3= =A1 de la eficiencia, ya que el alimento balanceado siempre oscila entre el= 60% y 70% de los costos operativos totales (Muyulema et al., 2020), siendo el principal rubr= o que conforma la estructura de costos en granjas av=C3=ADcolas. </a= ><spa= n style=3D"font-family:'Times New Roman'">El FCR cobra mayor importancia ya= que peque=C3=B1os cambios en =C3=A9ste se reflejan en grandes cambios econ= =C3=B3micos a nivel comercial. Una variaci=C3=B3n de 0.1 puntos en el FCR e= n una granja que procesa 1 mill=C3=B3n de aves al a=C3=B1o puede significar= miles de d=C3=B3lares en costos de alimento, impactando directamente en la= rentabilidad (Quintana-Ospina et al., 2023). Esta sensibilidad econ=C3=B3mica hace relevante= el desarrollo de instrumentos capaces de anticipar y controlar este indica= dor antes de que las desviaciones se consoliden y generen efectos irreversi= bles. Al mismo tiempo la av= icultura mundial se transform=C3=B3 tecnol=C3=B3gicamente en la =C3=BAltima= d=C3=A9cada, con automatizaci=C3=B3n completa de procesos, control ambient= al computarizado y uso generalizado de sistemas de sensorizaci=C3=B3n en ti= empo real. </spa= n><sp= an style=3D"font-family:'Times New Roman'">Esta revoluci=C3=B3n tecnol=C3= =B3gica, enmarcada en la Industria 4.0 y la agricultura de precisi=C3=B3n, = posibilito la captura continua de grandes cantidades de datos operativos, c= omo variables ambientales (temperatura, humedad relativa, CO=E2=82=82, NH= =E2=82=83), datos de consumo de alimento, mortalidad y diversos indicadores= de desempe=C3=B1o (Jackman et al., 2020; Pereira et al., 2020; Ahmed et al., 2024). Estas infraestructuras IoT generan grandes vol=C3=BAmenes de datos que = potencialmente pueden predecir desviaciones productivas antes de que se ref= lejen en indicadores zoot=C3=A9cnicos finales, como el peso corporal o la m= ortalidad acumulada (Liu et al., 2024; Elwakeel, 2025). La habilidad de identificar tempranamente tendencias ne= gativas permitir=C3=ADa realizar medidas correctivas tempranas, minimizando= p=C3=A9rdidas econ=C3=B3micas y mejorando el bienestar animal con medidas = preventivas. Pero todav=C3= =ADa existe una gran paradoja: en muchas granjas comerciales estos datos se= quedan en registros operativos est=C3=A1ticos y bases de datos infrautiliz= adas, sin convertirse en herramientas anal=C3=ADticas vivas que gu=C3=ADen = las decisiones diarias de gesti=C3=B3n (Astill et al., 2020). Esta diferencia entre lo que t= =C3=A9cnicamente se puede capturar y lo que realmente se aprovecha es una g= ran oportunidad perdida de optimizaci=C3=B3n productiva, m=C3=A1s a=C3=BAn = cuando est=C3=A1 demostrado el efecto de variables ambientales sobre indica= dores como el FCR. En parti= cular, las variables ambientales controladas en galpones automatizados infl= uyen directamente en par=C3=A1metros fisiol=C3=B3gicos que impactan en el d= esempe=C3=B1o zoot=C3=A9cnico. Las temperaturas ambientales por encima de l= a zona de termoneutralidad propia de cada fase productiva inducen estr=C3= =A9s t=C3=A9rmico que disminuye el consumo voluntario de alimento y empeora= el =C3=ADndice de conversi=C3=B3n por mecanismos fisiol=C3=B3gicos conocid= os (Oke et al., 20= 24; Tahamtani et a= l., 2020). </spa= n><sp= an style=3D"font-family:'Times New Roman'">Las humedades relativas altas, u= n problema en climas tropicales h=C3=BAmedos, favorecen el crecimiento bact= eriano y deterioran las caracter=C3=ADsticas f=C3=ADsicas de la cama, gener= ando las condiciones para problemas podales y dermatitis que impactan en el= bienestar y la productividad (Hidalgo et al., 2024). Las altas concentraciones de Amon=C3=ADaco (NH=E2=82=83</span= >) impactan negativamente en = la salud respiratoria y son precursoras de inflamaci=C3=B3n sist=C3=A9mica,= que compromete la eficiencia de crecimiento (Qaid et al., 2023). Las altas concentraciones d= e Di=C3=B3xido de Carbono (CO=E2=82=82) se relacionan con hipoxia subcl=C3=ADnica y letargo = productivo, y la ventilaci=C3=B3n insuficiente puede causar estr=C3=A9s por= fr=C3=ADo y aumentar los requerimientos energ=C3=A9ticos de mantenimiento,= desviando los recursos metab=C3=B3licos para el crecimiento (Shynkaruk= et al., 2023; = Gho= lami et al., 2020). </= span>= Estas interacciones ambiental= es siguen patrones fisiol=C3=B3gicamente no lineales, pero que pueden ser a= proximadas mediante funciones multivariables estacionarias cuando las condi= ciones de manejo y la gen=C3=A9tica animal no var=C3=ADan en el tiempo, tal= como ocurre en los sistemas intensivos actuales con l=C3=ADneas comerciale= s estandarizadas y protocolos uniformizados. En la =C3=BAltima d=C3=A9cada se presenci=C3=B3 un auge e= n el uso de t=C3=A9cnicas de anal=C3=ADtica avanzada y machine learning par= a la estimaci=C3=B3n en tiempo real de variables productivas en sistemas pe= cuarios (Ojo et al= ., 2022). Estas t=C3=A9cnicas abarcan desde la evaluaci=C3=B3n automatizada= del bienestar animal usando visi=C3=B3n por computador (Nasiri<= span style=3D"font-family:'Times New Roman'"> et al., 2022; Fang= et al., 2020), la estimaci= =C3=B3n no invasiva del peso corporal con an=C3=A1lisis de imagen avanzado = (Nyalala et al., 2= 021; Natho et al.,= 2025) hasta la predicci=C3=B3n del rendimiento alimenticio a partir de la = integraci=C3=B3n de datos heterog=C3=A9neos (Gonz=C3=A1lez et al., 2021; Batista-Mendoz= a et al., 2023; </span= >Li= et al., 2024). </span= ><spa= n style=3D"font-family:'Times New Roman'">Los mejores enfoques, especialmen= te los basados en redes neuronales profundas y m=C3=A9todos de conjunto, pu= eden lograr una precisi=C3=B3n predictiva superior a la de los m=C3=A9todos= estad=C3=ADsticos convencionales (Adli et al., 2025; Vilema et al., 2022). Un modelo predictivo reciente basad= o en SVM obtuvo una precisi=C3=B3n del 98,95% en clasificaci=C3=B3n binaria= para mantenimiento predictivo industrial (Vilema et al., 2022), lo que ilustra el potencial = de estos algoritmos cuando se afinan correctamente. Pero estos avances tecnol=C3=B3gicos, estas metodo= log=C3=ADas sofisticadas, tienen serias restricciones para su implementaci= =C3=B3n pr=C3=A1ctica generalizada en fincas comerciales latinoamericanas, = especialmente en lugares como Ecuador con escasos recursos t=C3=A9cnicos e = infraestructura computacional. Primero, req= uieren infraestructura computacional dedicada y personal capacitado para ca= libraci=C3=B3n, implementaci=C3=B3n y mantenimiento continuo. Segundo, y qu= iz=C3=A1s lo m=C3=A1s relevante para el usuario final, a menudo son modelos= 'caja negra' cuya l=C3=B3gica interna es dif=C3=ADcil de comprender por el= operador de producci=C3=B3n que tiene que defender cambios en las recetas = ante la gerencia operativa (You et al., 2021). En contraposici=C3=B3n, la regresi=C3=B3n lineal m=C3=BAltiple sigue sien= do una herramienta predictiva vigente y con utilidad pr=C3=A1ctica en la pr= oducci=C3=B3n animal intensiva (Granda et al., 2023; Freitas et al., 2025). Esta t=C3=A9cnica estad=C3=ADstica = comprobada puede modelar el efecto combinado de muchos factores ambientales= sobre un resultado zoot=C3=A9cnico importante como el FCR, pero al mismo t= iempo mantener trazabilidad estad=C3=ADstica completa, transparencia audita= ble y explicabilidad directa. Un estudio re= ciente de rendimiento de mano de obra en construcci=C3=B3n usando regresi= =C3=B3n lineal logr=C3=B3 una confianza del 96% que demuestra que la simpli= cidad metodol=C3=B3gica no sacrifica la capacidad predictiva cuando se usa = adecuadamente (Granda et al., 2023). Cada coeficiente del modelo es un efecto marginal interp= retable y comprensible para el personal t=C3=A9cnico, lo que facilita enorm= emente la comunicaci=C3=B3n de resultados y la justificaci=C3=B3n de las de= cisiones operativas. Un mod= elo predictivo de este tipo, una vez validado, puede incorporarse como un m= =C3=B3dulo de alerta temprana en el flujo de trabajo diario de una granja c= omercial. Cuando el FCR previsto para la semana siguiente supera ciertos um= brales internos, el equipo t=C3=A9cnico puede actuar de forma preventiva mo= dificando par=C3=A1metros clave como ventilaci=C3=B3n m=C3=ADnima, caudal d= e l=C3=ADneas de bebida, densidad efectiva o estrategia de alimentaci=C3=B3= n antes de que la eficiencia se deteriore y los sobrecostes sean irreversib= les. Esta capacidad predictiva es un cambio radical con respecto a la forma= tradicional reactiva de gestionar y convertirse en una gesti=C3=B3n preven= tiva impulsada por datos. P= ero a pesar de estos avances conceptuales y tecnol=C3=B3gicos, a=C3=BAn exi= ste una gran brecha en la literatura cient=C3=ADfica enfocada en Latinoam= =C3=A9rica y, m=C3=A1s espec=C3=ADficamente, en el contexto ecuatoriano. Ha= y poca evidencia emp=C3=ADrica disponible de modelos predictivos estad=C3= =ADsticamente verificados para estimar el FCR en granjas av=C3=ADcolas come= rciales automatizadas utilizando datos reales de sensores en ambientes trop= icales h=C3=BAmedos. Esta ausencia es especialmente relevante porque el cli= ma de lugares como Santo Domingo de los Ts=C3=A1chilas, con alta temperatur= a media anual, humedad permanente y presi=C3=B3n sanitaria, es muy diferent= e al de los climas templados en los que se realiz=C3=B3 la mayor=C3=ADa de = estudios experimentales que se encuentran en la literatura internacional. <= /span><a id=3D"_Hlk213272489">La i= ndustria av=C3=ADcola en Ecuador es un pilar para la seguridad alimentaria = y el desarrollo econ=C3=B3mico del pa=C3=ADs. Seg=C3=BAn cifras de la </spa= n>= Corporaci=C3=B3n Nacional de Avicultores del Ecuador (CONAVE, 2024) en los =C3=BAltimos 30 a=C3=B1os el sector crec= i=C3=B3 de manera permanente, al pasar de producir 50 millones de pollos de= engorde en 1990 a m=C3=A1s de 300 millones en 2024. Pero la producci=C3=B3= n de pollos broilers en zonas tropicales se enfrenta a grandes desaf=C3=ADo= s por los cambios clim=C3=A1ticos que ocurren durante todo el a=C3=B1o, afe= ctando el rendimiento productivo y el bienestar de las aves (Hidalgo</s= pan> et al., 2024).<sp= an style=3D"font-family:'Times New Roman'"> </a>La extrapolaci=C3=B3n directa de resultados de= climas templados a climas tropicales h=C3=BAmedos es metodol=C3=B3gicament= e inapropiada, ya que existen diferencias significativas en la din=C3=A1mic= a ambiental, presencia de pat=C3=B3genos, desaf=C3=ADos de manejo y respues= ta fisiol=C3=B3gica de las aves expuestas a estr=C3=A9s t=C3=A9rmico perman= ente. Por eso, es necesario desarrollar y validar modelos ajustados a condi= ciones tropicales, teniendo en cuenta sus peculiaridades ambientales, gen= =C3=A9ticas y de manejo. La falta de dichas herramientas limita la optimiza= ci=C3=B3n de la eficiencia alimenticia en un sector clave para la seguridad= alimentaria regional y nacional. En este c= ontexto problem=C3=A1tico, la pregunta cient=C3=ADfica que gu=C3=ADa la pre= sente investigaci=C3=B3n es: =C2=BFen qu=C3=A9 medida se puede estimar el f= actor de conversi=C3=B3n alimenticia semanal en pollos de engorde criados e= n galpones automatizados, a partir de datos ambientales capturados por sist= emas IoT, mediante un modelo de regresi=C3=B3n lineal m=C3=BAltiple validad= o estad=C3=ADsticamente y listo para ser utilizado operativamente por el pe= rsonal de producci=C3=B3n? = El objetivo general de la investigaci=C3=B3n consiste en desarrollar un mod= elo predictivo basado en regresi=C3=B3n lineal m=C3=BAltiple que estime el = FCR en granjas av=C3=ADcolas automatizadas a partir de variables ambientale= s cr=C3=ADticas medidas en tiempo real. De manera espec=C3=ADfica, se busca= : (i) caracterizar el comportamiento conjunto de temperatura, humedad relat= iva, Di=C3=B3xido de Carbono (CO=E2=82=82)<span= style=3D"font-family:'Times New Roman'"> y Amon=C3=ADaco (NH=E2=82=83= ) bajo condiciones com= erciales ecuatorianas; (ii) identificar cu=C3=A1les de estas variables expl= ican con mayor peso estad=C3=ADstico la variabilidad del FCR acumulado por = semana; (iii) construir la ecuaci=C3=B3n predictiva y evaluar su ajuste, es= tabilidad y ausencia de colinealidad; y (iv) proponer su uso como herramien= ta de apoyo a la toma de decisiones para optimizar la eficiencia alimentici= a y, por extensi=C3=B3n, la rentabilidad del sistema productivo.= 2.<sp= an style=3D"width:9pt; font:7pt 'Times New Roman'; display:inline-block">&#= xa0; Metodolog=C3=ADaSe realiz=C3=B3 un estudio de enfoque cuantitativo, observaci= onal y longitudinal retrospectivo, utilizando datos hist=C3=B3ricos generad= os por granjas automatizadas de pollos de engorde en el periodo de enero de= 2022 a marzo de 2025 (39 meses). El estudio se llev=C3=B3 a cabo en la pro= vincia de Santo Domingo de los Ts=C3=A1chilas, Ecuador, zona tropical h=C3= =BAmeda con temperatura media anual de 25-27=C2=B0C, humedad relativa >8= 0% y r=C3=A9gimen de lluvia estacional (Hidalgo et al., 2024; Quintana-Ospina<span= style=3D"font-family:'Times New Roman'"> et al., 2023). Las granjas de est= udio utilizan galpones modernos con ventilaci=C3=B3n mec=C3=A1nica forzada = tipo t=C3=BAnel, cortinas y sistemas automatizados de comederos y bebederos= (Pereira et al., = 2020; Liu et al., = 2023). Este ambiente tecnol=C3=B3gico permite el monitoreo en tiempo real d= e las condiciones ambientales y productivas de las aves. 2.1. Recolecci=C3=B3n y registro semanal de datosLas condiciones ambientales se controlaron co= n sensores IoT colocados a nivel de las aves y distribuidos uniformemente e= n los galpones. Estos sensores tomaron medidas continuas de temperatura, hu= medad relativa, Di=C3=B3xido de Carbono (CO=E2=82=82) y Amon=C3=ADaco (NH=E2=82=83)</sp= an> ambiental (Ahmed</spa= n> et al., 2024; <span= style=3D"font-family:'Times New Roman'; background-color:#ffff00">Elwakeel= , 2025). Los datos de = producci=C3=B3n se tomaron de los sistemas internos de gesti=C3=B3n de cada= granja, tales como consumo de alimento, peso promedio de las aves y mortal= idad diaria (Astill et al., 2020). Todos estos datos se recopilaron y revisaron semanalmente = utilizando protocolos estandarizados de captura de datos en sistemas intens= ivos (Liu et al., = 2024). <span= style=3D"font-family:'Times New Roman'; font-style:italic">2.2.</spa= n>&#= xa0; Poblaci=C3=B3n y muestraLa poblaci=C3=B3n estuvo compuesta por lotes comerciales complet= os de las l=C3=ADneas gen=C3=A9ticas COBB 500 y ROSS 308, seleccionadas por= su predominio en sistemas intensivos ecuatorianos (Gholami et al</span= >., 2020; Quintana-Ospin= a et al., 2023). La un= idad de an=C3=A1lisis fue el registro semanal consolidado por granja, integ= rando promedios ambientales semanales con el FCR acumulado. El proceso de selecci=C3=B3n inici=C3=B3= con m=C3=A1s de 8000 registros brutos, estructurado en m=C3=BAltiples hoja= s, pertenecientes a 18 granjas numeradas. Se aplicaron criterios de inclusi= =C3=B3n: galpones completamente automatizados; capacidad mayor o igual a 30= ,000 aves por lote; sensorizaci=C3=B3n ambiental con registro horario conti= nuo; historial digitalizado mayor o igual a 20 ciclos productivos consecuti= vos; y densidad de alojamiento entre 30-40 kg/m=C2=B2 (Gallard= et= al., 2022; <sp= an style=3D"font-family:'Times New Roman'; background-color:#ffff00">Shynka= ruk et al., 2023). Los= criterios de exclusi=C3=B3n incluyeron: semanas con fallas de sensorizaci= =C3=B3n >20% del tiempo; brotes sanitarios; cambios significativos en fo= rmulaci=C3=B3n alimenticia o l=C3=ADnea gen=C3=A9tica; y registros con valo= res ausentes. Tras la depuraci=C3=B3n, la muestra final consolid=C3=B3 936 = observaciones semanales de siete granjas automatizadas, codificadas como Gr= anja A, B, C, D, E, F y G, para confidencialidad industrial.2.3. <s= pan style=3D"font-family:'Times New Roman'; font-style:italic">Procesamient= o de datosEl procesamiento = se llev=C3=B3 a cabo utilizando una cadena ETL (Extract, Transform, Load</span= >) estandarizada y documentad= a (Li et al., 2024= ; Gonz=C3=A1lez et= al., 2021). En la etapa de extracci=C3=B3n, se unificaron las fuentes oper= ativas heterog=C3=A9neas de las siete granjas. La transformaci=C3=B3n impli= c=C3=B3: estandarizaci=C3=B3n de variables y formatos, creaci=C3=B3n de ide= ntificadores =C3=BAnicos lote-semana y el c=C3=A1lculo de variables derivad= as, tales como: FCR acumulado semanal, mortalidad acumulada (%), consumo y = ganancia semanal por ave. Para garantizar la calidad de los datos, se verif= ic=C3=B3 que todos estuvieran completos, que la informaci=C3=B3n registrada= fuera consistente en el tiempo, que los valores estuvieran dentro de rango= s normales. Adem=C3=A1s, se utiliz=C3=B3 un m=C3=A9todo de dispersi=C3=B3n = de valores para detectar outliers de forma precisa. Finalmente, la etapa de carga unific=C3=B3 la in= formaci=C3=B3n en una =C3=BAnica base de datos anal=C3=ADtica, consolidando= las variables descritas en la Tabla 1. Tabla 1Variables de la base de datos<table style=3D"width:100%; margi= n-right:auto; margin-left:auto; margin-bottom:0pt; padding:0pt; border-coll= apse:collapse"><tr style=3D"height:26.05pt"><td style=3D"width:39.3%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 5.4pt; vertical-align:top">Variable</= span></td><td style=3D"width:23.26%; border-top:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:10pt; line-height:115%; font-size:10pt">Tipo (unidad)</td><td style= =3D"width:37.44%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n breve</td></tr><tr><td style=3D"wi= dth:39.3%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top">fecha_sema= na</td><td style=3D"width:23.26%; border-top:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">Fecha (AAAA-MM-DD)</td><td style=3D"widt= h:37.44%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-alig= n:top">Fecha de co= rte semanal (ISO).</td></tr><tr><td style=3D"width:39.3%; paddin= g:0pt 5.4pt; vertical-align:top">A=C3=B1o</td><td style=3D"width:23.26%; padding:0= pt 5.4pt; vertical-align:top">Entero</td><td style=3D"width:37.44%; padding:0pt 5.= 4pt; vertical-align:top">A=C3=B1o calendario (2022=E2=80=932025).</td></tr><tr><td= style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Mes</td><td style=3D= "width:23.26%; padding:0pt 5.4pt; vertical-align:top">Entero (1=E2=80=9312)</td><t= d style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top">= Mes calendario.</t= d></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top"= >Ciclo<= /td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top">Entero</td><= td style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top">ID interno de ciclo/lote.<= /span></td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertic= al-align:top">Sema= na</td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-al= ign:top">Entero (1= =E2=80=937)</td><td style=3D"width:37.44%; padding:0pt 5.4pt; ve= rtical-align:top">= Semana del ciclo productivo.</td></tr><tr><td style=3D"width:39.= 3%; padding:0pt 5.4pt; vertical-align:top">granja_id</td><td style=3D"width:23.26%= ; padding:0pt 5.4pt; vertical-align:top">Categ=C3=B3rica (A=E2=80=93G)</td><td sty= le=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Identificador an=C3=B3nimo de gran= ja.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; ve= rtical-align:top">= linea_genetica</td><td style=3D"width:23.26%; padding:0pt 5.4pt;= vertical-align:top">Categ=C3=B3rica</td><td style=3D"width:37.44%; padding:0pt 5.= 4pt; vertical-align:top">COBB 500 / ROSS 308.</td></tr><tr><td style=3D"width:39.3= %; padding:0pt 5.4pt; vertical-align:top">ave_madura</td><td style=3D"width:23.26%= ; padding:0pt 5.4pt; vertical-align:top">Binaria (0/1)</td><td style=3D"width:37.4= 4%; padding:0pt 5.4pt; vertical-align:top">0 =3D sem. 1=E2=80=934; 1 =3D sem. 5=E2=80=937.</s= pan></td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical= -align:top">fcr_ac= umulado (fcr_acumulativo)</td><td style=3D"width:23.26%; padding= :0pt 5.4pt; vertical-align:top">Continua (kg/kg)</td><td style=3D"width:37.44%; pa= dding:0pt 5.4pt; vertical-align:top">Alimento acum. / peso vivo acum. a semana t.<= /td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:to= p">mortalidad_acum= ulada_pct0</td><td style=3D"width:23.26%; padding:0pt 5.4pt; ver= tical-align:top">C= ontinua (%)</td><td style=3D"width:37.44%; padding:0pt 5.4pt; ve= rtical-align:top">= Fallecidas acum. / poblaci=C3=B3n inicial =C3=97100.</td></tr><t= r><td style=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top">consumo_semanal_g_ave</spa= n></td><td style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top= ">Continua (g/ave)= </td><td style=3D"width:37.44%; padding:0pt 5.4pt; vertical-alig= n:top">Consumo sem= anal / aves vivas promedio.</td></tr><tr><td style=3D"width:39.3= %; padding:0pt 5.4pt; vertical-align:top">ganancia_semanal_g</td><td style=3D"widt= h:23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (g/ave)</td><td style=3D= "width:37.44%; padding:0pt 5.4pt; vertical-align:top"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABgAAAASCAYAA= ABB7B6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAACdJREFUOI1jYBgF= o2Awgf9DzmCaGk4Vg7EZMrhdjM1QmkbgKBgmAABh8gj4zupwOAAAAABJRU5ErkJggg=3D=3D" w= idth=3D"24" height=3D"18" alt=3D"" style=3D"margin-top:14.82pt; margin-left= :1.55pt; position:absolute" />Incremento de peso semanal por ave.</td></tr><tr><td sty= le=3D"width:39.3%; padding:0pt 5.4pt; vertical-align:top">peso_promedio_g</td><td = style=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Continua (g)</td><td= style=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><= span style=3D"font-family:'Times New Roman'">Peso promedio de muestra seman= al (=E2=89=A5100 aves).</td></tr><tr><td style=3D"width:39.3%; p= adding:0pt 5.4pt; vertical-align:top">temperatura_promedio</td><td style=3D"width:= 23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (=C2=B0C)</td><td style=3D= "width:37.44%; padding:0pt 5.4pt; vertical-align:top">Promedio semanal de temperatura interio= r.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4pt; ver= tical-align:top">h= umedad_promedio</td><td style=3D"width:23.26%; padding:0pt 5.4pt= ; vertical-align:top">Continua (%)</td><td style=3D"width:37.44%; padding:0pt 5.4p= t; vertical-align:top">Promedio semanal de HR interior.</td></tr><tr><td style=3D"= width:39.3%; padding:0pt 5.4pt; vertical-align:top">co2_promedio_ppm</td><td styl= e=3D"width:23.26%; padding:0pt 5.4pt; vertical-align:top">Continua (ppm)</td><td s= tyle=3D"width:37.44%; padding:0pt 5.4pt; vertical-align:top"><sp= an style=3D"font-family:'Times New Roman'">Promedio semanal de CO=E2=82=82 = interior.</td></tr><tr><td style=3D"width:39.3%; padding:0pt 5.4= pt; vertical-align:top">nh3_promedio_ppm</td><td style=3D"width:23.26%; padding:0p= t 5.4pt; vertical-align:top">Continua (ppm)</td><td style=3D"width:37.44%; padding= :0pt 5.4pt; vertical-align:top">Promedio semanal de NH=E2=82=83 interior.</td></tr= ><tr><td style=3D"width:39.3%; border-bottom:0.75pt solid #000000; padding:= 0pt 5.4pt; vertical-align:top">densidad_aves_m2</td><td style=3D"width:23.26%; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Continua (aves/m=C2= =B2)</td><td style=3D"width:37.44%; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Aves / =C3=A1rea =C3=BAtil del galp=C3=B3n.</s= pan></td></tr></table> <= span style=3D"font-family:'Times New Roman'; font-style:italic">2.4.<= /span> An=C3=A1lisis estad=C3=ADstico El an=C3=A1lisis se inici=C3=B3 con 18 variables candidatas= de la base de datos. La elecci=C3=B3n de predictores se realiz=C3=B3 en tr= es pasos para asegurar robustez metodol=C3=B3gica (Freitas et al., 2025; Li<span= style=3D"font-family:'Times New Roman'"> et al., 2024). En la primera etap= a se usaron criterios te=C3=B3rico-pr=C3=A1cticos de literatura av=C3=ADcol= a tropical y disponibilidad operativa. En la segunda etapa se realiz=C3=B3 = un an=C3=A1lisis exploratorio cuantitativo usando una matriz de correlacion= es y modelos univariados iniciales (p < 0.25), seg=C3=BAn metodolog=C3= =ADas informadas en estudios previos de producci=C3=B3n av=C3=ADcola (</spa= n>Q= uintana-Ospina et al.,= 2023; Gonz=C3=A1lez et al., 2021). La tercera etapa estableci=C3=B3 las variables predictora= s finales combinando significancia estad=C3=ADstica (p < 0.05) con justi= ficaci=C3=B3n te=C3=B3rica encontrada en literatura cient=C3=ADfica sobre f= isiolog=C3=ADa aviar en ambientes tropicales (Oke et al., 2024; Hidalgo et al., 2024). Se estim=C3=B3 un modelo de regresi=C3=B3n lineal m= =C3=BAltiple (RLM) por M=C3=ADnimos Cuadrados Ordinarios (MCO), tomando com= o variable dependiente el FCR acumulado semanal. Candidatas a variables pre= dictoras: temperatura promedio semanal (=C2=B0C), humedad relativa promedio= semanal (%), CO=E2=82=82 promedio semanal (ppm), NH=E2=82=83 promedio sema= nal (ppm), ave madura (0=3Dsemanas 1-4; 1=3Dsemanas 5-7) y l=C3=ADnea gen= =C3=A9tica (0=3DCOBB 500; 1=3DROSS 308). La elecci=C3=B3n de RLM sobre mode= los m=C3=A1s complejos se alinea con trazabilidad operativa y facilidad de = interpretaci=C3=B3n en ambientes de producci=C3=B3n av=C3=ADcola comercial = (Freitas et al., 2= 025; Granda et al.= , 2023; Vilema et = al., 2022). La Ecuaci=C3=B3n 1 inicial del modelo de RLM que considera las variables antes mencionas:= <img src=3D"data:i= mage/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhAAAAApCAYAAABk6hDFAAAABHNCSVQICAg= IfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeJzsnXd8lFXa978zmZlkJmXSQ0moAQ= LSqxQBUemIiKCwKiAoygIKLiorWFdW7IgKqKhYFkVBQAVREEUglAAJJRASICSkJzOZ3u/z/sE75= yEGFBWfdfeZ3+fDH2Rm7vuU61z9XJdKCCEIIYQQQgghhBBCuEyoVCqV+t89iBBCCCGEEEII4T8P= IQUihBBCCCGEEEL41dD8uwfwR0EIgc/nw+/3ExYWhlarRa3+9+tLgUAAr9eLEAKdTodG81+7BSF= cQfxZ6fnPhkAggNvtRgiBXq8nLCzsNz/L6/Xi9XpRq9VERET8YesdCATw+XwoioJWq0Wj0aBSqf= 6Qd/03QQiB1+vF7/ej0WjQ6XShdfsNuJD+fk4mBXmQRqORZ0ET/MBisWCxWFAUpc6PwsPDiY+PJ= yIiQr7M4XDgcDjw+/2oVCr0ej1GoxGNRkMgEKC6uhqXy4UQApVKhVarxWAwYDQa/1eYnt/vx2w2= s2fPHjIzM4mNjeXGG28kPT0djUaDEAKHw4Hb7UZRFDQaDdHR0Wi12j90XDabjcLCQrZs2UJ1dTW= 9e/dm6NChhIeHy+8oiiLXNjY2NnQgQsDv92Mymfjxxx/Zu3cvDRo04KabbqJp06YA2O12vF6v/G= 5ERAQxMTG/S3ja7XZMJhMAycnJREREEAgEMJlMOBwOIiMjiY+P/13v+CNgs9n461//islk4s033= 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MtERUW1CTBpTbxYUlLCF198QWRkJGPHjlXnVRqNhqFDh6rlb06DZDQa1QyrN3O75a7w8PA2aZue= eOIJ9f8mk4mEhAT175vbk2XZ71zrRiRJIikpiYaGBlwuF4qi8NxzzxESEoLX62X48OHtTsda4zs= 6Mk5ptVq0Wm27I/iBAwcYNWoUzc3NrFmzhokTJzJ8+PB2A8cURenQ/hBATZzysNMpqkaWZSZNms= Qrr7zS6bnO/Dy6SJKELMtYrVYqKytZvHgxISEhOBwOzpw5Q3p6ervTMZfLxZQpU9rdkwGQlpbGG= 2+80a5SOXPmDHa7nRkzZmAymVi+fHmHOmtzczOzZ8/mzJkzdy0nSRLz589n6dKl7dbZ3XSKAoiJ= ibnnKDs/3x5KSkoQQjBw4EA0Gg3Xrl1DkqQ2ufzvhE6nY/v27R1KRhocHNyu+dwajHbo0CEAJk+= erB4g2h5Go5HMzMwORWN2ZV7A+6FTFMCjeo68nwePoihcvHiRxx57TM04nJeXhyzL6PV6ioqK6N= u37x07rkaj6dTt5pcuXcLn85GQkKAeDpKdnc3o0aNpaGi4a3pxWZYfmI9ACNEtCXb9PdfPA8Xpd= JKbm0vfvn3V6aHL5aKmpobVq1dz/PjxLl16O3LkCMOHD6dHjx7qGQMrVqxgyZIlHdpo1Zn4fD7q= 6+s5ceIExcXF/Otf/6KwsBC73d5lyqDDqwB+/HwT7HY7e/fuZeDAgerW6tYz/vr160dCQkKXLgN= +8cUXeL1eJk+ejBCC2tpaDhw4wIQJE+54JsKDoqamhu3bt7c5rRdg9OjRXRKvIkmS5FcAfvx8S5= EkSfp/OpFbrJ1kX5oAAAAASUVORK5CYII=3D" width=3D"256" height=3D"39" alt=3D"" = style=3D"vertical-align:middle" />= Donde n representa el tama=C3=B1o muestral y k el n=C3=BAmero de predictore= s. Se estableci=C3=B3 el reporte de error est=C3=A1ndar de estimaci=C3=B3n,= significancia global mediante prueba F (ANOVA), y significancia individual= de coeficientes mediante pruebas t bilaterales (=CE=B1 =3D 0.05). Se calcu= laron intervalos de confianza del 95% para todos los coeficientes de regres= i=C3=B3n.Se implement=C3=B3= validaci=C3=B3n tipo hold-out 80/20 con partici=C3=B3n estratificada por c= iclo productivo para garantizar representatividad temporal (Freitas</sp= an> et al., 2025; <spa= n style=3D"font-family:'Times New Roman'; background-color:#ffff00">You</sp= an> et al., 2021). En el conj= unto de validaci=C3=B3n independiente (20% de observaciones no utilizadas e= n el ajuste) se estimaron R=C2=B2 de validaci=C3=B3n, MAE (Mean Absolute Error) y RMSE (Root Mean Square Error) como m=C3=A9tricas de capacidad predictiva en datos n= o vistos. Los supuestos fundamentales de la RLM se verificaron mediante: gr= =C3=A1ficos de dispersi=C3=B3n y residuos parciales para evaluar linealidad= ; prueba de Durbin-Watson para verificar independencia de residuos (ausenci= a de autocorrelaci=C3=B3n); prueba de Breusch-Pagan para evaluar homocedast= icidad (varianza constante de errores); prueba de Shapiro-Wilk para verific= ar normalidad de residuos; y Factores de Inflaci=C3=B3n de la Varianza (VIF<spa= n style=3D"font-family:'Times New Roman'">) para detectar multicolinealidad= entre predictores, con umbral cr=C3=ADtico VIF < 5 (Freitas= et al., 2025; Li<= span style=3D"font-family:'Times New Roman'"> et al., 2024). Ante evidencia= de heterocedasticidad residual, la inferencia estad=C3=ADstica se compleme= nt=C3=B3 con errores est=C3=A1ndar robustos agrupados por ciclo productivo = (cl= uster-robust standard errors).El an=C3=A1lisis es= tad=C3=ADstico se realiz=C3=B3 en RStudio (versi=C3=B3n 2023.12.0) utilizan= do los paquetes stats, car, lmtest y sandwich, con verificaci=C3=B3n cruzad= a en IBM SPSS Statistics = (versi=C3=B3n 27) para garantizar reproducibilidad de resultados.</p= >2.5. <span= style=3D"font-family:'Times New Roman'; font-style:italic">Consideraciones= =C3=A9ticasEl estudio empl= e=C3=B3 =C3=BAnicamente datos secundarios anonimizados, autorizados por la = gerencia t=C3=A9cnica bajo acuerdos de confidencialidad industrial. Todas l= as granjas av=C3=ADcolas fueron codificadas (A-G) y no se realizaron manipu= laciones experimentales ni cambios en los protocolos productivos. La inform= aci=C3=B3n confidencial se manej=C3=B3 con protocolos estrictos de confiden= cialidad para no poner en riesgo la posici=C3=B3n competitiva de las unidad= es participantes. = ; 3. = ResultadosEl presente estudio cumpli=C3=B3 satisfactoriamente su objetivo de desa= rrollar un modelo predictivo basado en regresi=C3=B3n lineal m=C3=BAltiple = para estimar el FCR semanal en granjas av=C3=ADcolas automatizadas bajo con= diciones tropicales h=C3=BAmedas del Ecuador. 3.1.&= #xa0;Estad=C3=ADsticos descriptivos de las variables del estudioLa Tabla 2 presenta los estad=C3=ADsticos descriptivos de las 936 = observaciones semanales analizadas. El FCR acumulado mostr=C3=B3 media de 1= .23 (DE =3D 0.22) con rango entre 0.81 y 1.67, reflejando la heterogeneidad= natural entre fases productivas tempranas (semanas 1-4) y tard=C3=ADas (se= manas 5-7).Tabla 2E= stad=C3=ADsticos descriptivos de las variables del estudio (n =3D 936)</spa= n><table style=3D"width:100%; margin-bottom:0pt; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:= 1.5pt"><thead><tr><td style=3D"width:22.68%; border-bottom:0.75pt solid #00= 0000; padding:0.75pt; vertical-align:top">Variable</td><td style=3D"width:0.86%; border-bottom:0.75pt so= lid #000000; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%;= border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">Media</td><td style=3D"width:= 12.54%; border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-align:= top">DE</td><td style=3D"widt= h:14.86%; border-bottom:0.75pt solid #000000; padding:0.75pt; vertical-alig= n:top"><spa= n style=3D"font-family:'Times New Roman'">M=C3=ADnimo</td><td st= yle=3D"width:15.76%; border-bottom:0.75pt solid #000000; padding:0.75pt; ve= rtical-align:top">M=C3=A1ximo= </td><td style=3D"width:11.82%; border-bottom:0.75pt solid #000000; padding= :0.75pt; vertical-align:top">CV (%)</spa= n></td></tr></thead><tbody><tr><td style=3D"width:22.68%; padding:0.75p= t; vertical-align:top">FCR acumulativo (= kg/kg)</td><td style=3D"width:0.86%; padding:0.75pt; vertical-al= ign:top"><s= pan style=3D"font-family:'Times New Roman'"> </td><td style= =3D"width:15.76%; padding:0.75pt; vertical-align:top">1,23</td><td style=3D"width:12.54%; padding:0.75pt= ; vertical-align:top">0,22</t= d><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top">0,81</td><td style=3D"width:15.76%; p= adding:0.75pt; vertical-align:top">1,67<= /span></td><td style=3D"width:11.82%; padding:0.75pt; vertical-align:to= p">17,9</td></tr><tr><td styl= e=3D"width:22.68%; padding:0.75pt; vertical-align:top">Temperatura interna (=C2=B0C)</td><td style=3D"wi= dth:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.75pt; ver= tical-align:top">27,42</td><t= d style=3D"width:12.54%; padding:0.75pt; vertical-align:top">2,10</td><td style=3D"width:14.86%; padding= :0.75pt; vertical-align:top">22,90</span= ></td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">32,00</td><td style=3D"width:= 11.82%; padding:0.75pt; vertical-align:top">7,7</td></tr><tr><td style=3D"width:22.68%; padding:0.75pt; = vertical-align:top">Humedad relativa (%)= </td><td style=3D"width:0.86%; padding:0.75pt; vertical-align:to= p"> </td><td style=3D"wi= dth:15.76%; padding:0.75pt; vertical-align:top">82,58</td><td style=3D"width:12.54%; padding:0.75pt; ver= tical-align:top">4,18</td><td= style=3D"width:14.86%; padding:0.75pt; vertical-align:top">68,50</td><td style=3D"width:15.76%; padding= :0.75pt; vertical-align:top">89,80</span= ></td><td style=3D"width:11.82%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">5,1</td></tr><tr><td style=3D= "width:22.68%; padding:0.75pt; vertical-align:top">CO=E2=82=82 (ppm)</td><td style=3D"width:0.86%; paddi= ng:0.75pt; vertical-align:top"> </s= pan></td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:top"= >589,25</td><td style=3D"widt= h:12.54%; padding:0.75pt; vertical-align:top">36,28</td><td style=3D"width:14.86%; padding:0.75pt; verti= cal-align:top">476,30</td><td= style=3D"width:15.76%; padding:0.75pt; vertical-align:top">700,00</td><td style=3D"width:11.82%; paddin= g:0.75pt; vertical-align:top">6,2= </td></tr><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align= :top"><span= style=3D"font-family:'Times New Roman'">NH=E2=82=83 (ppm)</td><= td style=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; paddi= ng:0.75pt; vertical-align:top">18,46</sp= an></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">= 2,02</td><td style=3D"width:1= 4.86%; padding:0.75pt; vertical-align:top">12,60</td><td style=3D"width:15.76%; padding:0.75pt; vertical= -align:top">24,70</td><td sty= le=3D"width:11.82%; padding:0.75pt; vertical-align:top">10,9</td></tr><tr><td style=3D"width:22.68%; pad= ding:0.75pt; vertical-align:top">Peso pr= omedio (g)</td><td style=3D"width:0.86%; padding:0.75pt; vertica= l-align:top"> </td><td s= tyle=3D"width:15.76%; padding:0.75pt; vertical-align:top">1.476,91</td><td style=3D"width:12.54%; paddin= g:0.75pt; vertical-align:top">957,24</sp= an></td><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top">= 166,20</td><td style=3D"width= :15.76%; padding:0.75pt; vertical-align:top">3.389,30</td><td style=3D"width:11.82%; padding:0.75pt; ver= tical-align:top">64,8</td></t= r><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align:top">Mortalidad acumulada (%)</td><td st= yle=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.= 75pt; vertical-align:top">1,70</p= ></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">1,00</td><td style=3D"width:14.86%;= padding:0.75pt; vertical-align:top">0,1= 0</td><td style=3D"width:15.76%; padding:0.75pt; vertical-align:= top">3,58</td><td style=3D"wi= dth:11.82%; padding:0.75pt; vertical-align:top">58,8</td></tr><tr><td style=3D"width:22.68%; padding:0.7= 5pt; vertical-align:top">Consumo semanal= (g/ave)</td><td style=3D"width:0.86%; padding:0.75pt; vertical-= align:top">= </td><td sty= le=3D"width:15.76%; padding:0.75pt; vertical-align:top">1.666,60</td><td style=3D"width:12.54%; padding:= 0.75pt; vertical-align:top">866,36</span= ></td><td style=3D"width:14.86%; padding:0.75pt; vertical-align:top"><p= style=3D"text-align:center; line-height:115%; font-size:9pt">426,50</td><td style=3D"width= :15.76%; padding:0.75pt; vertical-align:top">3.483,80</td><td style=3D"width:11.82%; padding:0.75pt; ver= tical-align:top">52,0</td></t= r><tr><td style=3D"width:22.68%; padding:0.75pt; vertical-align:top">Densidad (aves/m=C2=B2)</td><td sty= le=3D"width:0.86%; padding:0.75pt; vertical-align:top"> </td><td style=3D"width:15.76%; padding:0.7= 5pt; vertical-align:top">16,38</p= ></td><td style=3D"width:12.54%; padding:0.75pt; vertical-align:top">0,17</td><td style=3D"width:14.86%;= padding:0.75pt; vertical-align:top">16,= 10</td><td style=3D"width:15.76%; padding:0.75pt; vertical-align= :top"><span= style=3D"font-family:'Times New Roman'">16,60</td><td style=3D"= width:11.82%; padding:0.75pt; vertical-align:top">1,0</td></tr></tbody></table> Las condi= ciones ambientales reflejaron caracter=C3=ADsticas t=C3=ADpicas de clima tr= opical h=C3=BAmedo: temperatura promedio de 27.42=C2=B0C (DE =3D 2.10) con = rango entre 22.90 y 32.00=C2=B0C, y humedad relativa de 82.58% (DE =3D 4.18= ), valor que excede las gu=C3=ADas t=C3=A9cnicas =C3=B3ptimas establecidas = (60-75%). Los gases ambient= ales se mantuvieron en concentraciones moderadas (CO=E2=82=82: 589.25 =C2= =B1 36.28 ppm; NH=E2=82=83: 18.46 =C2=B1 2.02 ppm). La variabilidad observa= da en peso corporal (1,476.91 =C2=B1 957.24 g) y FCR (1.23 =C2=B1 0.22) ref= leja la progresi=C3=B3n natural del ciclo productivo, proporcionando adecua= da dispersi=C3=B3n para la modelaci=C3=B3n predictiva.3.2. Selecci=C3=B3n de = variables predictores mediante an=C3=A1lisis de correlacionesEl an=C3=A1lisis de correlaciones bivari= adas revel=C3=B3 patrones definidos entre el FCR acumulado y las variables = ambientales evaluadas. Como se observa en la Tabla 3, cuatro variables mostraron correlaciones estad= =C3=ADsticamente significativas (p < 0.001) con el FCR acumulado, mientr= as que otras tres variables no alcanzaron el umbral de significancia establ= ecido.Tabl= a 3Correl= aci=C3=B3n de variables candidatas con FCR acumulado<table style= =3D"width:100%; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><= tr style=3D"height:22.2pt"><td style=3D"width:44.84%; border-top:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical= -align:top">Variable</td><td = style=3D"width:27.58%; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 3.5pt; vertical-align:top">Correlaci=C3=B3n FCR</td><td style=3D"width:27.5= 8%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 3.5pt; vertical-align:top">Sig= nificancia</td></tr><tr style=3D"height:30.6pt"><td style=3D"wid= th:44.84%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">Temperatura ambiental (=C2=B0C)= </td><td style=3D"width:27.58%; border-top:0.75pt solid #000000;= padding:0pt 3.5pt; vertical-align:top">-0.682***</td><td style=3D"width:27.58%; border-top:0.75pt soli= d #000000; padding:0pt 3.5pt; vertical-align:top">p < 0.001</td></tr><tr style=3D"height:30.6pt"><td= style=3D"width:44.84%; padding:0pt 3.5pt; vertical-align:top">Humedad relativa (%)</td><td style=3D"wi= dth:27.58%; padding:0pt 3.5pt; vertical-align:top">0.512***</td><td style=3D"width:27.58%; padding:0pt = 3.5pt; vertical-align:top">p < 0.001= </td></tr><tr style=3D"height:30.6pt"><td style=3D"width:44.84%;= padding:0pt 3.5pt; vertical-align:top">Ave_madura (semanas 5-7)</td><td style=3D"width:27.58%; padding= :0pt 3.5pt; vertical-align:top">0.745**= *</td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">p < 0.001</td></t= r><tr style=3D"height:30.6pt"><td style=3D"width:44.84%; padding:0pt 3.5pt;= vertical-align:top">L=C3=ADnea gen=C3= =A9tica (ROSS 308)</td><td style=3D"width:27.58%; padding:0pt 3.= 5pt; vertical-align:top">0.321***</span= ></td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top"= >p < 0.001</td></tr><tr s= tyle=3D"height:20.4pt"><td style=3D"width:44.84%; padding:0pt 3.5pt; vertic= al-align:top">CO=E2=82=82 promedio (ppm= )</td><td style=3D"width:27.58%; padding:0pt 3.5pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'">0.045</td><td style= =3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top">p =3D 0.324</td></tr><tr style=3D"height:20.4p= t"><td style=3D"width:44.84%; padding:0pt 3.5pt; vertical-align:top">NH=E2=82=83 promedio (ppm)</td><td= style=3D"width:27.58%; padding:0pt 3.5pt; vertical-align:top">0.038</td><td style=3D"width:27.58%; pad= ding:0pt 3.5pt; vertical-align:top">p = =3D 0.287</td></tr><tr style=3D"height:20.4pt"><td style=3D"widt= h:44.84%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-a= lign:top">= Densidad_aves_m=C2=B2<= /p></td><td style=3D"width:27.58%; border-bottom:0.75pt solid #000000; padd= ing:0pt 3.5pt; vertical-align:top">0.06= 2</td><td style=3D"width:27.58%; border-bottom:0.75pt solid #000= 000; padding:0pt 3.5pt; vertical-align:top">p =3D 0.156</td></tr></table> La temperatura= ambiental exhibi=C3=B3 correlaci=C3=B3n negativa fuerte (r =3D -0.682, p &= lt; 0.001), evidenciando su papel en la din=C3=A1mica del FCR. Ave_madura p= resent=C3=B3 la correlaci=C3=B3n positiva m=C3=A1s alta (r =3D 0.745, p <= ; 0.001), reflejando el incremento progresivo del FCR en semanas finales de= l ciclo productivo. La humedad relativa mostr=C3=B3 correlaci=C3=B3n positi= va moderada (r =3D 0.512, p < 0.001), mientras que la l=C3=ADnea gen=C3= =A9tica evidenci=C3=B3 correlaci=C3=B3n positiva significativa (r =3D 0.321= , p < 0.001), reflejando diferencias en eficiencia alimenticia entre COB= B 500 y ROSS 308 bajo las condiciones evaluadas.Las variables CO=E2=82=82 (r =3D 0.045, p =3D 0.324) y= NH=E2=82=83 (r =3D 0.038, p =3D 0.287) no alcanzaron significancia estad= =C3=ADstica. La densidad de aves (r =3D 0.062, p =3D 0.156) mostr=C3=B3 baj= a variabilidad operativa entre granjas (16.38 =C2=B1 0.17 aves/m=C2=B2), li= mitando su capacidad predictiva diferencial.3.3. Verificaci=C3=B3n de supuestos estad= =C3=ADsticosEl diagn=C3=B3s= tico exhaustivo de supuestos confirm=C3=B3 la adecuaci=C3=B3n del modelo de= RLM. La prueba de Shapiro-Wilk (W =3D 0.994, p =3D 0.089) valid=C3=B3 la n= ormalidad de la distribuci=C3=B3n de residuos, cumpliendo con el supuesto d= e errores normalmente distribuidos. La prueba de Durbin-Watson arroj=C3=B3 = un valor de 1.98, evidenciando independencia residual y ausencia de autocor= relaci=C3=B3n significativa (valor cercano a 2 indica independencia =C3=B3p= tima). La prueba de Breusch-Pagan (=CF=87=C2=B2 =3D 4.32, p =3D 0.115) conf= irm=C3=B3 homocedasticidad, descartando problemas de varianza heterog=C3=A9= nea de residuos. Los Factores de Inflaci=C3=B3n de la Varianza (VIF < 2.= 5 para todos los predictores) descartaron problemas sustanciales de multico= linealidad entre variables independientes.La Figura 1= complementa estos resultados mostrando la distribuci=C3=B3n de residuales = en funci=C3=B3n de los valores predichos de FCR. Figura 1Diagn=C3=B3stico de Residuales del Mo= delo de RLM<img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAARMAA= ADWCAYAAADy+2YLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFU= eJzsveeTXFl65vc759o0lZnlPQq24dub6e6Znh4/pLjc4XLIXe4uRUVIWn2QIhQKReijNNIfIH1= RKEIKeS65wV1xaZYzHC7HtZ2emZ62cIUCUBblbdprz9GHm5WoLIcCUEBXd9cTgQCQmffec88957= 3nvO/zPq/QWmseIdYvJ4R4lJe9K7TWB7JNcNhXe8FBbRN8fp6f3Pcz3gVaax6x/doTlFIHsl0HD= VprlFKfdDO2II7jT7oJW3BQx/rD6qtHbkwOsXcc1MF4ENt0iE8eh8bkEIc4xL7g0Jgc4hCH2Bcc= GpNDHOIQ+4JDY3KIQxxiX/CJRHM+79AaYqU57In9w+G4+uRh3usBSqkHCi0d1Ni7UgopH41t9SP= N8FLA2U4Ha5dLHuS+OmiT96CGhuHgPb84jvft+UkpMQwDuA9jIqV8oM5Z5yg8qom7V8RxfM/35k= WauXJIX87Ckns/bmTF5+0pj96cQ2/Lzo/gIA7GdcLT+gA6SDDNex7ODxUHdazDw+mr+zrjgwzu9= WMP0gSBpD3rf/aKpVrET8cq/MapFnqy1p6P685a9OUscq6x6/UOojFZx0Fr070+u0eBgz7W9xsH= z2TuEyKlWarFBPHDW447hqDgGjjGzt0YxppYNbch70q+cixLerc9ziEO8SnDZ3Y0lwPFW+MV5sr= Rvp43jDXXl3zCWJN3Db40lCHnbN+NodJcnve4suA3fW5KQVvK4B52Roc4xIHHZ9aYREqz5sfUwv= 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RhIpbBMA8/zCTyXUsUlkXSoVl0sy0KoED9QJBMOSgjcSgklTHKZJB/duEHvwDA6Pv/zz/8UPdHN= 7/7m11BBSLEwy3//0z/nwkuXSeqCVy69jPGUL5keFOJgEtNiFOVSGTuRQCjFw4cTGHaKnu5ONCH= w3CoTDx6Q7+hC03Ry2fR+NzimRcTBJCYmpiXE48uYmJiW8P8B4tG4sYELJFUAAAAASUVORK5CYI= I=3D" width=3D"275" height=3D"214" alt=3D"" /> Los punto= s se dispersan aleatoriamente alrededor de la l=C3=ADnea de residual cero (= l=C3=ADnea s=C3=B3lida negra), sin evidenciar patrones sistem=C3=A1ticos de= curvatura o heterocedasticidad. La l=C3=ADnea suavizada (morada) permanece= pr=C3=B3xima a cero en todo el rango de predicci=C3=B3n, y el =C3=A1rea so= mbreada (intervalo de confianza) mantiene amplitud constante, confirmando l= a estabilidad de la varianza residual. La diferenciaci=C3=B3n por fase prod= uctiva (semanas 1-4 en azul, semanas 5-7 en naranja) muestra distribuci=C3= =B3n equilibrada en ambos per=C3=ADodos, con la mayor=C3=ADa de los residua= les contenidos dentro del rango de =C2=B10.1 unidades de FCR, validando vis= ualmente la ausencia de sesgo y el cumplimiento de los supuestos de regresi= =C3=B3n lineal. = 3.4. Modelo de RLM finalEl modelo final explic=C3=B3 77.4% de la varianza total del FCR acumulad= o (R=C2=B2 =3D 0.774; R=C2=B2 ajustado =3D 0.773), evidenciando excelente c= apacidad explicativa. El an=C3=A1lisis de varianza confirm=C3=B3 significan= cia global altamente robusta (F(6,929) =3D 158.3, p < 0.001), validando = la utilidad conjunta de los predictores incluidos. El error est=C3=A1ndar r= esidual fue de 0.107 puntos de FCR.La Tabla 4 muestra= que cuatro predictores fueron altamente significativos (p < 0.001). La = temperatura exhibi=C3=B3 efecto negativo sustancial (=CE=B2 =3D -0.0445, p = < 0.001), indicando que cada incremento de 1=C2=B0C reduce el FCR en 0.0= 445 puntos. La fase fisiol=C3=B3gica mostr=C3=B3 el mayor impacto absoluto = (=CE=B2 =3D 0.198, p < 0.001), reflejando el deterioro natural de la efi= ciencia de conversi=C3=B3n al avanzar hacia semanas finales. La humedad pre= sent=C3=B3 efecto positivo moderado (=CE=B2 =3D 0.00858, p < 0.001). La = l=C3=ADnea gen=C3=A9tica mostr=C3=B3 diferencia significativa (=CE=B2 =3D 0= .072, p < 0.001), con ROSS 308 exhibiendo FCR superior a COBB 500 bajo l= as condiciones evaluadas. CO=E2=82=82 (=CE=B2 =3D 0.000017, p =3D 0.879) y = NH=E2=82=83 (=CE=B2 =3D 0.000655, p =3D 0.742) no alcanzaron significancia = estad=C3=ADstica. Tabla 4Coeficiente del modelo de regresi=C3=B3n para FCR</s= pan><table style=3D"width:100%; margin-bottom:0pt; padding:0pt; border-= collapse:collapse"><tr><td style=3D"width:30.12%; border-top:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Predictor</td><td style=3D"width:24.2= 8%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"= >=CE=92</= td><td style=3D"width:22.18%; border-top:0.75pt solid #000000; padding:0pt = 5.4pt; vertical-align:top">p-valor</td><td style=3D"width:23.42%; border-top:0.75pt = solid #000000; padding:0pt 5.4pt; vertical-align:top">IC 95%</td></tr><tr><td style= =3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Intercepto</td><td style=3D"width:24.28%; padd= ing:0pt 5.4pt; vertical-align:top">1,63= 1</td><td style=3D"width:22.18%; padding:0pt 5.4pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'"><0,001</td><td st= yle=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[1,400; 1,862]</td></tr><tr><td style=3D"wi= dth:30.12%; padding:0pt 5.4pt; vertical-align:top">Temperatura (=C2=B0C)</td><td style=3D"width:24.28%;= padding:0pt 5.4pt; vertical-align:top">-0,0445</td><td style=3D"width:22.18%; padding:0pt 5.4pt; verti= cal-align:top"><0,001</td= ><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[-0,049; -0,040]</td></tr><tr><td st= yle=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Humedad relativa (%)</td><td style=3D"width= :24.28%; padding:0pt 5.4pt; vertical-align:top">0,00858</td><td style=3D"width:22.18%; padding:0pt 5.4p= t; vertical-align:top"><0,001= </td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">= [0,0067; 0,0105]</td></tr><t= r><td style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">Ave madura</td><td style=3D"width:2= 4.28%; padding:0pt 5.4pt; vertical-align:top">0,198</td><td style=3D"width:22.18%; padding:0pt 5.4pt; v= ertical-align:top"><0,001= </td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:top">[0,176; 0,220]</td></tr><tr><td = style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">L=C3=ADnea gen=C3=A9tica</td><td style=3D= "width:24.28%; padding:0pt 5.4pt; vertical-align:top">0,072</td><td style=3D"width:22.18%; padding:0pt = 5.4pt; vertical-align:top"><0,001</s= pan></td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align:t= op">[0,058; 0,086]</td></tr>= <tr><td style=3D"width:30.12%; padding:0pt 5.4pt; vertical-align:top">CO=E2=82=82 (ppm)</td><td style= =3D"width:24.28%; padding:0pt 5.4pt; vertical-align:top">0,000017</td><td style=3D"width:22.18%; paddin= g:0pt 5.4pt; vertical-align:top">0,879<= /span></td><td style=3D"width:23.42%; padding:0pt 5.4pt; vertical-align= :top"><spa= n style=3D"font-family:'Times New Roman'">[-0,0002; 0,0002]</td>= </tr><tr><td style=3D"width:30.12%; border-bottom:0.75pt solid #000000; pad= ding:0pt 5.4pt; vertical-align:top">NH= =E2=82=83 (ppm)</td><td style=3D"width:24.28%; border-bottom:0.7= 5pt solid #000000; padding:0pt 5.4pt; vertical-align:top">0,000655</td><td style=3D"width:22.18%; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">0,742</td><td style=3D"width:23.4= 2%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:t= op">[-0,0033; 0,0046]</td></= tr></table> La Ecuaci=C3=B3n 3 predictiva final del modelo se define de la siguiente= manera:= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACo= AAAAgCAYAAABkWOo9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAoFJRE= FUWIXtlz1LI1EYRk+WBRFtgwQErf0gBLHUOCqiYGFh41+wswoINoJYKCJoYSOiQtIIYisWIgbUQ= o0oQghCIvEDEUQ0+MU8WxkY4y4zs7O7uMyBae77vMzhDvedmYAk8QX49q8F7OKLeo0v6jW+qNf8= 36IXFxfEYjGur6+RRCKRoL29ndbWVhKJRDE3NjZGMpn0xlQOOT09VSQS0crKikzT1MLCggDLtbu= 7K0nKZDIyDEPxeNzpbUpwJHp3d6eWlhZNT09Lkm5ubrS8vKxCoaBcLqfe3l4Bmp+fL/bs7+8L0M= 7Ozt8TnZmZEaDz83NJ0tvbm6U+Pj4uQNvb25b1/v5+dXd3l+T/iOjLy4vq6+sVDodlmqalVigUt= L6+rpqaGk1MTJT0Tk1NCdDe3p5rUduH6ezsjJOTExoaGggEApba7OwsXV1dZLNZ4vE4h4eHlnp1= dTUAqVTK9VmyLXp7ewtAZWVlSW1wcJCNjQ2i0SgHBwcMDQ1Z6uXl5QBcXl66Fv1uN/i+ix93E6C= iooKOjg7q6uqIRqNsbm7y/PxMWVkZAKZpAqDf+PS1LRoMBgF4fHz8aSYUCtHT00NjY2NREuDp6Q= mAqqoqt572H31tbS2RSITj4+Pi2uLiIoZhsLa2xsPDA7lcjlQqxcjIiKU3n88DEA6HXYs6Gk9zc= 3MClM1mJUnJZFJ9fX0KhUJqbm7W8PCw0ul0Sd/AwIA6Ozv1+vrq+tQ7Er2/v1dbW5smJydt9xwd= HX06W53i+BWaTqfV1NSk1dXVknn6WdYwDC0tLbkWfMexqCTl83nFYjFdXV39Mjc6OqqtrS1XYh8= JSP7vsqf4ol7ji3qNL+o1X0b0B7r3N/AN7Y34AAAAAElFTkSuQmCC" width=3D"42" height= =3D"32" alt=3D"" style=3D"margin-top:2.03pt; margin-left:400.66pt; position= :absolute" />FCR =3D 1,631=E2=88=92 0,0445 =E2=8B=85 Temperatura + 0,00858 <span= style=3D"font-family:'Cambria Math'; font-style:italic">=E2=8B=85<s= pan style=3D"font-family:'Times New Roman'; font-style:italic"> Humedad + 0= ,198 = =E2=8B=85 Ave_madura + 0,072 =E2=8B=85 L=C3=ADnea_gen=C3=A9ticaDonde temperatura se expresa en =C2=B0C, humedad en= %, Ave_madura es una variable binaria (0 =3D semanas 1-4; 1 =3D semanas 5-= 7), y L=C3=ADnea_gen=C3=A9tica es una variable binaria (0 =3D COBB 500; 1 = =3D ROSS 308).La <sp= an style=3D"font-family:'Times New Roman'; font-weight:bold">Figura 2</span= > muestra los coeficientes de= l modelo de regresi=C3=B3n, cuantificando el impacto de cada variable sobre= el FCR. Las barras rojas indican factores que aumentan el FCR (menor efici= encia), mientras que las verdes lo reducen (mayor eficiencia).<p= style=3D"text-align:justify; line-height:115%; font-size:12pt">La fase productiva presenta el mayor efe= cto, con un incremento de +0.198 unidades al pasar de las semanas 1-4 a las= semanas 5-7, reflejando la disminuci=C3=B3n natural de la eficiencia de co= nversi=C3=B3n con la edad. La l=C3=ADnea gen=C3=A9tica ROSS 308 registra un= FCR superior en +0.072 unidades respecto a COBB 500, se=C3=B1alando difere= ncias en el potencial gen=C3=A9tico de conversi=C3=B3n bajo condiciones tro= picales.La temperatura ambi= ental constituye la variable de control m=C3=A1s influyente, con un coefici= ente de -0.044, indicando que cada incremento de 1=C2=B0C mejora la eficien= cia alimenticia, posiblemente al reducir el gasto energ=C3=A9tico de termor= regulaci=C3=B3n dentro de la zona de confort t=C3=A9rmico. La humedad relat= iva muestra un efecto m=C3=ADnimo (+0.009 por cada 1%), con impacto limitad= o en comparaci=C3=B3n con las dem=C3=A1s variables.Estos resultados destacan que, si bien los factores= biol=C3=B3gicos (fase y gen=C3=A9tica) ejercen la mayor influencia sobre e= l FCR, las variables ambientales monitorizadas mediante IoT ofrecen oportun= idades tangibles de optimizaci=C3=B3n productiva mediante el control microc= lim=C3=A1tico preciso. 3.5. Validaci=C3=B3n y capacidad predictivaLa validaci=C3=B3n externa= con 186 observaciones independientes (20% del conjunto de datos) confirm= =C3=B3 robustez predictiva del modelo, alcanzando R=C2=B2 =3D 0.739 en dato= s no vistos durante el proceso de ajuste. Esta p=C3=A9rdida predictiva de 4= .5 puntos porcentuales (de 77.4% a 73.9%) evidencia excelente capacidad de = generalizaci=C3=B3n y estabilidad del modelo ante nuevos datos.<= p class=3D"NoSpacing" style=3D"text-align:justify; line-height:115%; font-s= ize:12pt"> <p= class=3D"NoSpacing" style=3D"text-align:justify; line-height:115%; font-si= ze:12pt">Las m=C3=A9tricas de= error absoluto mostraron alta precisi=C3=B3n operativa: MAE =3D 0.088 y RM= SE =3D 0.109 puntos de FCR. Estos valores representan aproximadamente 7% y = 9% respectivamente de la desviaci=C3=B3n est=C3=A1ndar observada en FCR (DE= =3D 0.22), indicando utilidad pr=C3=A1ctica para aplicaciones de monitoreo= en granjas automatizadas.La Figura 2 presenta la relaci=C3=B3n entre valores predichos y observados de FCR,= mostrando un ajuste satisfactorio del modelo de regresi=C3=B3n desarrollad= o. Los puntos se distribuyen pr=C3=B3ximos a la l=C3=ADnea de predicci=C3= =B3n perfecta (y =3D x, l=C3=ADnea gris), mientras que la l=C3=ADnea roja y= su intervalo de confianza del 95% evidencian la capacidad del modelo para = estimar el =C3=ADndice de conversi=C3=B3n alimenticia a partir de variables= ambientales. <= /p>Figura 2</p= >Relaci=C3=B3n entre= valores predichos y observados de FCR<img src=3D"data:image/png;base64,iV= BORw0KGgoAAAANSUhEUgAAARMAAAD0CAYAAAC4n8I2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzA= AAOxAAADsQBlSsOGwAAIABJREFUeJzsvWeQXdeZrvesHU/qjM6N0AiNDIIAwQTmJJKSKJIapdEd= zdXIdzQ1vjX3lstlV025ylUul8v+Yf/wretbnqnxndFMaTQaBVIiKTGKJEgCBJFjowF0o3OOJ+2= 01vKPfdAkSBAkwCZFCvv5QzR79z777D777bW+8H5Ca61J+IzRzExNYqZrqMm6lz1CKYnnBWQy6S= V5xSgMiLRByrGu+RyeV6ZU8qitq8UQ4rLHKBkRKoFrG0R+GcNOYxiXP/ZKKBVRKPlU57LXfL0Jn= y0iEZPfD0ophBCID3koQaM1V/j+1RH/mgWf5HQXPyofdU3xK136r2t4tSV9/wmfPomYJCQkLAnG= 7/sCEhIS/jBIxCQhIWFJuPZo3HtIdkoJCQmfSEy01kgpUUot1fUkJCR8QfnEYlIul/F9P4m6E2d= 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Edbzf77734XM5GfHKa8EDSV6cC1pISfy0uS3EJCQk5JPntpiADQkJ+eQJxSQkJOS6EIpJSEjIdS= EUk5CQkOtCKCYhISHXhVBMQkJCQkJCQm4e/n97KXscX4rwyAAAAABJRU5ErkJggg=3D=3D" wid= th=3D"275" height=3D"244" alt=3D"" /> El an=C3=A1lisis por l=C3=ADnea gen=C3=A9tica (COBB 5= 00 y ROSS 308) no revela diferencias sustanciales en el patr=C3=B3n predict= ivo, validando la aplicabilidad del modelo para ambas estirpes. La estratif= icaci=C3=B3n temporal muestra que las semanas 1-4 (azul) presentan menor di= spersi=C3=B3n que las semanas 5-7 (naranja), sugiriendo mayor sensibilidad = del modelo en fases tempranas cuando las variables ambientales ejercen mayo= r impacto sobre el desempe=C3=B1o productivo. La distribuci=C3=B3n homog=C3= =A9nea de residuales confirma la ausencia de sesgo sistem=C3=A1tico y respa= lda la validez del enfoque de regresi=C3=B3n lineal m=C3=BAltiple para cond= iciones de producci=C3=B3n av=C3=ADcola en clima tropical h=C3=BAmedo.</spa= n> 4. Discusi=C3=B3n</span= ><spa= n style=3D"font-family:'Times New Roman'">Los resultados obtenidos demuestr= an consistentemente que el modelo de regresi=C3=B3n lineal m=C3=BAltiple de= sarrollado exhibe capacidad predictiva robusta y operativamente =C3=BAtil p= ara estimar el factor de conversi=C3=B3n alimenticia semanal en granjas av= =C3=ADcolas automatizadas bajo condiciones tropicales h=C3=BAmedas. El R=C2= =B2 de 0,774 en entrenamiento y 0,739 en validaci=C3=B3n externa, acompa=C3= =B1ado de errores de predicci=C3=B3n bajos (MAE =3D 0,088; RMSE =3D 0,109),= supera los valores t=C3=ADpicamente reportados en la literatura para model= os similares en contextos comerciales reales. Investigaciones previas en gr= anjas brasile=C3=B1as reportaron R=C2=B2 entre 0,65-0,72 para modelos de pr= edicci=C3=B3n de FCR utilizando variables ambientales, mientras que estudio= s en M=C3=A9xico alcanzaron R=C2=B2 de 0,69-0,75 (Freitas et al., 2025). La capacidad predict= iva de nuestro modelo se mantuvo estable a trav=C3=A9s de diferentes ciclos= productivos, fases fisiol=C3=B3gicas y condiciones estacionales, evidencia= ndo robustez para implementaci=C3=B3n operativa inmediata.El efecto negativo sustancial de la temperat= ura ambiental sobre el FCR (-0,0445 puntos por =C2=B0C) constituye un halla= zgo que requiere interpretaci=C3=B3n cuidadosa dentro del contexto tropical= espec=C3=ADfico. Dentro del rango de temperaturas observado (22,9-32,0=C2= =B0C), incrementos t=C3=A9rmicos moderados parecen favorecer la eficiencia = alimenticia, posiblemente debido a reducciones en los requerimientos energ= =C3=A9ticos de mantenimiento para termorregulaci=C3=B3n. Este hallazgo cont= rasta con estudios realizados en climas templados donde temperaturas superi= ores a 28=C2=B0C generalmente deterioran el FCR mediante mecanismos de estr= =C3=A9s t=C3=A9rmico bien documentados (Oke et al., 2024). La aparente discrepancia sugiere t= res explicaciones plausibles no mutuamente excluyentes.Primero, las aves criadas bajo condiciones tro= picales h=C3=BAmedas podr=C3=ADan desarrollar aclimataci=C3=B3n fisiol=C3= =B3gica que modifica sus rangos =C3=B3ptimos de termoneutralidad. La exposi= ci=C3=B3n cr=C3=B3nica a temperaturas elevadas puede inducir adaptaciones m= etab=C3=B3licas y conductuales que mejoran la tolerancia t=C3=A9rmica. Segu= ndo, el rango de temperaturas observado (22,9-32,0=C2=B0C) podr=C3=ADa ubic= arse mayoritariamente dentro o ligeramente por encima de la zona de termone= utralidad para pollos en fase de crecimiento acelerado (t=C3=ADpicamente 18= -26=C2=B0C), donde incrementos moderados efectivamente reducen gastos energ= =C3=A9ticos de mantenimiento sin alcanzar umbrales de estr=C3=A9s t=C3=A9rm= ico severo. Tercero, podr=C3=ADa existir confusi=C3=B3n parcial con variabl= es estacionales no capturadas: per=C3=ADodos de mayor temperatura (=C3=A9po= ca seca) podr=C3=ADan coincidir con mejor calidad de cama, menor carga micr= obiana y condiciones generales m=C3=A1s favorables para el desempe=C3=B1o a= v=C3=ADcola.La magnitud del= coeficiente indica que variaciones de 2-3=C2=B0C en la temperatura promedi= o semanal comunes entre =C3=A9pocas secas y lluviosas en Santo Domingo de l= os Ts=C3=A1chilas pueden traducirse en diferencias de 0,09-0,13 puntos en e= l FCR, representando impactos econ=C3=B3micos sustanciales considerando que= el alimento constituye 60-70% de los costos operativos (Muyulema</span= > et al., 2020). Esta sensibi= lidad econ=C3=B3mica subraya la importancia cr=C3=ADtica del control ambien= tal automatizado en condiciones tropicales.El efecto positivo de la humedad relativa sobre el FCR (0,0= 0858 puntos por %) confirma los desaf=C3=ADos particulares de la avicultura= en ambientes tropicales h=C3=BAmedos, consistente con hallazgos previos en= condiciones similares (Hidalgo et al., 2024). El coeficiente estimado indica que, por cada a= umento de 10 puntos porcentuales en la humedad relativa dentro del rango ob= servado, el FCR se incrementa en aproximadamente 0,086 puntos lo que implic= a menor eficiencia alimenticia. Este efecto probablemente opera a trav=C3= =A9s de m=C3=BAltiples mecanismos fisiol=C3=B3gicos y ambientales interrela= cionados: estr=C3=A9s t=C3=A9rmico por reducci=C3=B3n de la capacidad de p= =C3=A9rdida de calor evaporativo, deterioro de la calidad f=C3=ADsica de la= cama con consecuentes problemas podales y dermatitis de contacto, y potenc= ial aumento de la carga microbiana ambiental que compromete la salud intest= inal y respiratoria.El hech= o de que la humedad relativa emergiera como predictor significativo a pesar= de la relativa homogeneidad de los valores observados (CV =3D 5,1%, rango = 68,5-89,8%) resalta su importancia cr=C3=ADtica en estos sistemas. Los valo= res promedio de 82,58% exceden consistentemente las gu=C3=ADas t=C3=A9cnica= s =C3=B3ptimas internacionales (60-75%), reflejando la limitaci=C3=B3n f=C3= =ADsica fundamental de controlar humedad en climas tropicales mediante sist= emas de ventilaci=C3=B3n convencionales sin deshumidificaci=C3=B3n activa, = tecnolog=C3=ADa a=C3=BAn no econ=C3=B3micamente viable para producci=C3=B3n= comercial en la regi=C3=B3n.La fase fisiol=C3=B3gica present=C3=B3 el mayor efecto individual sobre e= l FCR, al pasar de las semanas 1=E2=80=934 a 5=E2=80=937, el FCR aumenta en= 0,198 puntos, en l=C3=ADnea con la menor eficiencia alimenticia propia del= cierre del ciclo productivo. Este resultado es consistente con el conocimi= ento fisiol=C3=B3gico establecido: a medida que las aves maduran, la compos= ici=C3=B3n de la ganancia de peso cambia progresivamente hacia mayor deposi= ci=C3=B3n de grasa (energ=C3=A9ticamente costosa) en detrimento de prote=C3= =ADna muscular, los requerimientos energ=C3=A9ticos de mantenimiento aument= an proporcionalmente m=C3=A1s que el potencial de ganancia, y pueden manife= starse reducciones en la capacidad digestiva y de absorci=C3=B3n intestinal= (Quintana-Ospina = et al., 2023).La magnitud d= e este efecto (0,198 puntos) resalta la importancia estrat=C3=A9gica de imp= lementar pr=C3=A1cticas de manejo diferenciadas seg=C3=BAn la fase producti= va, particularmente concerniente a densidad de alojamiento efectiva, estrat= egias de ventilaci=C3=B3n, y formulaci=C3=B3n alimenticia adaptada a los ca= mbios en requerimientos nutricionales. La capacidad del modelo de mantener = precisi=C3=B3n predictiva equivalente en ambas fases (demostrada en = Figura 1</s= pan>) confirma su utilidad op= erativa a lo largo del ciclo productivo completo.Las diferencias gen=C3=A9ticas entre l=C3=ADneas tamb= i=C3=A9n emergieron como predictores significativos, con la l=C3=ADnea ROSS= 308 mostrando un FCR 0,072 puntos superior al COBB 500 bajo condiciones co= mparables. Esta diferencia, aunque moderada en magnitud, es consistentement= e reportada en la literatura t=C3=A9cnica comercial y refleja distintos equ= ilibrios establecidos por los programas de selecci=C3=B3n gen=C3=A9tica: RO= SS 308 t=C3=ADpicamente exhibe velocidad de crecimiento ligeramente superio= r pero eficiencia alimenticia marginalmente inferior comparada con COBB 500= (Gholami et al., = 2020). Desde una perspectiva operativa, este hallazgo justifica el establec= imiento de metas de FCR diferenciadas por l=C3=ADnea gen=C3=A9tica en siste= mas de monitoreo y la consideraci=C3=B3n expl=C3=ADcita de estas diferencia= s en an=C3=A1lisis econ=C3=B3micos de selecci=C3=B3n de gen=C3=A9tica.</spa= n><sp= an style=3D"font-family:'Times New Roman'">La ausencia de significancia est= ad=C3=ADstica de CO=E2=82=82 y NH=E2=82=83 como predictores dentro de los r= angos operativos observados constituye un hallazgo relevante desde la persp= ectiva del manejo pr=C3=A1ctico. Las concentraciones promedio de CO=E2=82= =82 (589,25 ppm) y NH=E2=82=83 (18,46 ppm) se mantuvieron consistentemente = por debajo de los umbrales cr=C3=ADticos establecidos en la literatura para= afectaci=C3=B3n del desempe=C3=B1o productivo (>3,000 ppm y >25 ppm,= respectivamente). Esto sugiere que, bajo las condiciones de ventilaci=C3= =B3n mec=C3=A1nica y manejo de cama prevalentes en las granjas estudiadas c= aracterizadas por sistemas de t=C3=BAnel con alta capacidad de renovaci=C3= =B3n de aire estas variables no constituyeron limitantes principales para l= a eficiencia alimenticia durante el per=C3=ADodo de estudio.Sin embargo, es importante destacar enf= =C3=A1ticamente que este resultado no minimiza la importancia de mantener a= decuados controles de calidad de aire, particularmente considerando los efe= ctos documentados de estos gases sobre la salud respiratoria, el bienestar = animal y la incidencia de enfermedades respiratorias a m=C3=A1s largo plazo= . La ausencia de efecto detectado probablemente refleja el =C3=A9xito de la= s pr=C3=A1cticas de manejo implementadas m=C3=A1s que la irrelevancia intr= =C3=ADnseca de estos par=C3=A1metros.La capacidad predictiva del modelo compara favorablemente con est= udios utilizando t=C3=A9cnicas de machine learning m=C3=A1s complejas. A di= ferencia de algoritmos que alcanzan exactitudes superiores al 98% mediante = optimizaci=C3=B3n exhaustiva de hiperpar=C3=A1metros (Vilema<spa= n style=3D"font-family:'Times New Roman'"> et al., 2022), pero carecen de i= nterpretabilidad directa, nuestro modelo mantiene un equilibrio =C3=B3ptimo= entre capacidad predictiva (R=C2=B2 =3D 0,774) y transparencia operativa. = Esta caracter=C3=ADstica es particularmente valiosa en contextos donde el p= ersonal t=C3=A9cnico debe justificar intervenciones de manejo espec=C3=ADfi= cas ante gerencia operativa, y donde la comprensi=C3=B3n de la direcci=C3= =B3n y magnitud de los efectos facilita la toma de decisiones fundamentada = (Granda et al., 20= 23).Desde la perspectiva de= implementaci=C3=B3n pr=C3=A1ctica, el modelo ofrece ventajas significativa= s. La Ecuaci=C3=B3n 3 resu= ltante FCR =3D 1,631 - 0,0445=C3=97Temperatura + 0,00858=C3=97Humedad + 0,1= 98=C3=97Fase + 0,072=C3=97ROSS puede programarse f=C3=A1cilmente en hojas d= e c=C3=A1lculo o dashboards operativos, requiriendo =C3=BAnicamente insumos= rutinariamente disponibles en granjas automatizadas. La simplicidad comput= acional lo hace accesible para empresas medianas y peque=C3=B1as sin infrae= structura especializada para algoritmos complejos. La naturaleza lineal de = las relaciones facilita sustancialmente la comunicaci=C3=B3n de hallazgos y= la justificaci=C3=B3n de intervenciones basadas en pron=C3=B3sticos genera= dos.Las implicaciones econ= =C3=B3micas son sustanciales. Considerando que variaciones de 0,1 puntos en= FCR pueden traducirse en diferencias de 3-5% en rentabilidad en operacione= s medianas, la capacidad de predecir y gestionar proactivamente este indica= dor tiene impacto directo en sostenibilidad financiera. La implementaci=C3= =B3n del modelo como sistema de alerta temprana permitir=C3=ADa intervencio= nes correctivas ajustes en ventilaci=C3=B3n, densidad, estrategia alimentic= ia antes de que las ineficiencias se consoliden en sobrecostos irreversible= s.4.1.</= span> Limitaciones del estudioEs importante contextualizar las limitaciones= . El modelo fue desarrollado y validado espec=C3=ADficamente para condicion= es tropicales h=C3=BAmedas de Ecuador y podr=C3=ADa requerir recalibraci=C3= =B3n para otros contextos clim=C3=A1ticos. El rango de valores observado pa= ra gases ambientales fue relativamente estrecho debido a las buenas pr=C3= =A1cticas de ventilaci=C3=B3n, limitando potencialmente la detecci=C3=B3n d= e efectos que podr=C3=ADan manifestarse en condiciones m=C3=A1s extremas. E= l modelo no incorpora variables relacionadas con salud aviar espec=C3=ADfic= a, calidad nutricional del alimento, o factores de estr=C3=A9s agudo, que p= odr=C3=ADan aportar varianza explicativa adicional. La generalizaci=C3=B3n = a sistemas con diferente nivel de automatizaci=C3=B3n o capacidad de contro= l ambiental requiere evaluaci=C3=B3n espec=C3=ADfica.Los hallazgos abren varias l=C3=ADneas de investi= gaci=C3=B3n. Ser=C3=ADa valioso explorar interacciones no lineales mediante= splines o transformaciones polin=C3=B3micas, particularmente para temperat= ura en rangos extremos. La incorporaci=C3=B3n de variables adicionales como= velocidad de viento, calidad de cama medida objetivamente, e indicadores d= e salud del lote podr=C3=ADa mejorar la capacidad predictiva. El desarrollo= de sistemas de integraci=C3=B3n en tiempo real con plataformas IoT existen= tes facilitar=C3=ADa la implementaci=C3=B3n operativa. Estudios longitudina= les evaluando el impacto econ=C3=B3mico de la implementaci=C3=B3n del model= o en condiciones comerciales reales permitir=C3=ADan cuantificar m=C3=A1s p= recisamente su valor agregado y retorno de inversi=C3=B3n. 5. = 0; ConclusionesEl m= odelo de regresi=C3=B3n lineal m=C3=BAltiple desarrollado demostr=C3=B3 cap= acidad predictiva robusta y operativamente =C3=BAtil para estimar el FCR se= manal en granjas av=C3=ADcolas automatizadas bajo condiciones tropicales h= =C3=BAmedas. Los indicadores de desempe=C3=B1o alcanzados (R=C2=B2 =3D 0,77= 4 en entrenamiento; R=C2=B2 =3D 0,739 en validaci=C3=B3n externa; MAE =3D 0= ,088; RMSE =3D 0,109) superan los umbrales t=C3=ADpicamente considerados ac= eptables para aplicaciones comerciales y se mantienen estables a trav=C3=A9= s de diferentes ciclos productivos y condiciones estacionales, evidenciando= robustez para implementaci=C3=B3n operativa inmediata.Las variables ambientales monitorizadas contin= uamente fueron predictores significativos del FCR (p < 0,001). En el ran= go observado (22,9=E2=80=9332,0 =C2=B0C), la temperatura se asoci=C3=B3 inv= ersamente con el FCR (=E2=88=920,0445 puntos/=C2=B0C), coherente con una me= nor demanda de termorregulaci=C3=B3n. La humedad relativa mostr=C3=B3 asoci= aci=C3=B3n positiva (+0,00858 puntos/%), reflejando los retos del ambiente = tropical h=C3=BAmedo. En conjunto, los resultados obtenidos en granjas auto= matizadas en operaci=C3=B3n resaltan la importancia del control ambiental a= utomatizado para optimizar la eficiencia alimenticia.Las caracter=C3=ADsticas del lote incidieron sign= ificativamente en el FCR. La fase final (semanas 5=E2=80=937) increment=C3= =B3 el FCR en 0,198 puntos respecto de la fase inicial (semanas 1=E2=80=934= ), en concordancia con el mayor costo energ=C3=A9tico de mantenimiento y lo= s cambios en la composici=C3=B3n del crecimiento al cierre del ciclo. La l= =C3=ADnea gen=C3=A9tica ROSS 308 present=C3=B3 un FCR 0,072 puntos superior= a COBB 500 bajo condiciones comparables, lo que sugiere diferencias sistem= =C3=A1ticas entre perfiles de crecimiento y eficiencia. Estos resultados re= spaldan metas de FCR diferenciadas por fase productiva y gen=C3=A9tica.</sp= an><s= pan style=3D"font-family:'Times New Roman'">El modelo representa una herram= ienta operativamente viable para gesti=C3=B3n preventiva del FCR en granjas= comerciales, ofreciendo un balance =C3=B3ptimo entre capacidad predictiva,= simplicidad computacional y transparencia interpretativa. A diferencia de = algoritmos de machine learning tipo "caja negra", la ecuaci=C3=B3n de regre= si=C3=B3n lineal desarrollada puede implementarse f=C3=A1cilmente en hojas = de c=C3=A1lculo o dashboards operativos existentes, requiriendo =C3=BAnicam= ente insumos rutinariamente disponibles en granjas automatizadas. Esta acce= sibilidad facilita su adopci=C3=B3n por empresas medianas y peque=C3=B1as s= in infraestructura (galpones autom=C3=A1ticos) , democratizando el acceso a= herramientas anal=C3=ADticas avanzadas.La implementaci=C3=B3n del modelo como sistema de alerta tempr= ana permitir=C3=ADa intervenciones correctivas proactivas (ajustes en venti= laci=C3=B3n, densidad, estrategia alimenticia) antes de que las ineficienci= as se consoliden en sobrecostos irreversibles. Considerando que el alimento= representa 60-70% de costos operativos y que variaciones de 0,1 puntos en = FCR impactan 3-5% la rentabilidad, la capacidad de predecir y gestionar est= e indicador tiene consecuencias directas en sostenibilidad financiera. Se r= ecomienda la implementaci=C3=B3n piloto en granjas seleccionadas para valid= ar utilidad operativa y la integraci=C3=B3n futura con dashboards IoT en ti= empo real para maximizar su valor pr=C3=A1ctico en la toma de decisiones di= arias. </= p>6. Conflicto de in= teresesLos autores declaran= que no existe conflicto de intereses en relaci=C3=B3n con el art=C3=ADculo= presentado. Ninguna de las instituciones o granjas involucradas en la gene= raci=C3=B3n de los datos condiciono el an=C3=A1lisis, la interpretaci=C3=B3= n de resultados ni la redacci=C3=B3n del manuscrito.7. Declaraci=C3=B3n de contribuci= =C3=B3n de los autoresTodos= los autores contribuyeron significativamente en la elaboraci=C3=B3n del ar= t=C3=ADculo.8. Costos de financiamientoLa presente investigaci=C3=B3n fue financiada en su totalidad con recursos= propios de los autores. No se recibieron fondos externos de entidades p=C3= =BAblicas ni privadas. Las granjas participantes proporcionaron acceso a da= tos operativos bajo acuerdos de confidencialidad, pero no financiaron el an= =C3=A1lisis estad=C3=ADstico ni la elaboraci=C3=B3n del manuscrito.<= /p>9. <span= style=3D"font-family:'Times New Roman'; font-weight:bold">Referencias Bibl= iogr=C3=A1ficasAdli, D. 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Visionario Digital</s= pan>, 4(1), 43=E2=80=9366. </= span><a href=3D"https://doi.org/10.33262/visionariodigital.v4i1.1089" style= =3D"text-decoration:none">https://doi.org/10.33262/visionariodigital.v4i1.1089</span= ></a> <div style= =3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAA= BCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSI= ntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740"= height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; p= osition:absolute" /> &= #xa0; Nombre 1</spa= n> </div></div> <div class=3D"Section_3"><div st= yle=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2= wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8f= ExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4= eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQ= 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