Dynamic simulation models can increase research efficiency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, difficulties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from five growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling efficiency, correlation coefficient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass.

El plátano es un producto básico en la alimentación para más de 400 millones de habitantes de las regiones tropicales y subtropicales del mundo. Los modelos del desarrollo de cultivos se han convertido en una herramienta de mucha utilidad para investigadores que estudian procesos fisiológicos básicos, y agricultores que los usan en la toma de decisiones para el manejo del cultivo. Con el objetivo de desarrollar un modelo de producción potencial de plátano, se usaron los datos de dos experimentos con la variedad Dominico-Hartón en Colombia, en los cuales se midieron variables fisiológicas de crecimiento y desarrollo, así como variables climáticas. Con base en los resultados se estimó el coeficiente de extinción de la luz (k) en 0,2817 y el uso eficiente de la luz (LUE) en 1,63 g MJ-1. En la etapa vegetativa, las hojas, el tallo y el cormo incrementaron su materia seca por partes iguales, pero en la etapa reproductiva las hojas y el cormo perdieron masa, mientras que el tallo continuó en aumento, aunque no tan aceleradamente como en la primera etapa. Del análisis de sensibilidad se deduce que LUE es el parámetro más sensible y, por lo tanto, los esfuerzos se deben dirigir a mejorar la conversión de luz incidente en materia seca.

Plantain is a basic food for more than 400 million people of the tropical and subtropical regions of the world. Crop modeling has become a useful agricultural tool whose outputs not only facilitate the scientific study of crop physiological processes, but also the adaptation of farmers' crop management decisions. By using physiological and climatic data from two experiments on variety Dominico-Harton (Musa AAB Simmonds), a plantain potential production model was developed. Based on the results, Light Extinction Coefficient (k) and Light Use Efficiency (LUE) were respectively estimated as 0.2817 and 1.63 g MJ-1. Likewise, aerial dry matter results allowed estimating partition coefficients for both the vegetative and the reproductive stage. Leaf, stem and corm dry matter were observed to increase in equal proportions during the vegetative stage. During the reproductive stage, only the stem was observed to increase its dry matter content (although not as much as in the vegetative stage), while leaves and corm were found to decrease it. A sensitivity analysis established LUE as the most sensitive parameter. In consequence, research efforts should be aimed at improving this conversion of radiant energy into dry matter.

Dados analisados durante o desenvolvimento de fisalis, espécie frutífera da família das solanáceas, foram usados para avaliar a aparição de nós em diferentes condições ambientais através de um modelo clássico termal de tempo. Os dados usados na análise foram obtidos de experimentos conduzidos em dois locais, com clima diferente, em campos abertos e em condições de estufa, na Colômbia. O modelo não-linear empírico segmentado foi usado para estimar a temperatura-base, e para estabelecer os parâmetros do modelo da simulação da aparição de nós em função do tempo. A temperatura-base (Tb) usada para calcular o tempo termal (TT, ºCd) para a aparição de nós foi 6,29 ºC. A inclinação do segmento linear foi de 0,023 e de 0,008 nós por TT, para o primeiro e segundo segmentos lineares, respectivamente. O tempo estimado para a aparição do nó foi de 1.039,5 ºCd depois do transplante, determinado a partir de uma análise estatística do modelo do primeiro segmento. Quando esses coeficientes foram usados para predizer a aparição de nós em todas as locações, o modelo ajustou-se dados observados adequadamente (RSME=2,1), especialmente para o primeiro segmento, onde a aparição de nós foi mais homogênea do que no segundo segmento. O número de nós e as taxas mínimas e máximas de aparição de nós foram maiores em plantas cultivadas em condições de estufa do que em plantas cultivadas nas demais condições.

Data was analyzed on development of the solanaceen fruit crop Cape gooseberry to evaluate how well a classical thermal time model could describe node appearance in different environments. The data used in the analysis were obtained from experiments conducted in Colombia in open fields and greenhouse condition at two locations with different climate. An empirical, non linear segmented model was used to estimate the base temperature and to parameterize the model for simulation of node appearance vs. time. The base temperature (Tb) used to calculate the thermal time (TT, ºCd) for node appearance was estimated to be 6.29 ºC. The slope of the first linear segment was 0.023 nodes per TT and 0.008 for the second linear segment. The time at which the slope of node apperance changed was 1039.5 ºCd after transplanting, determined from a statistical analysis of model for the first segment. When these coefficients were used to predict node appearance at all locations, the model successfully fit the observed data (RSME=2.1), especially for the first segment where node appearance was more homogeneous than the second segment. More nodes were produced by plants grown under greenhouse conditions and minimum and maximum rates of node appearance rates were also higher.