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Agricultural policy changes directly affect the economic viability of Texas crop producers because government payments make up a significant portion of their net farm income (NFI). NFI projections benefit producers, agribusinesses and policy makers, but an economic model making these projections for Texas did not previously exist. The objective of this study was to develop a model to project annual NFI for producers of major crops in Texas. The Texas crop model was developed to achieve this objective, estimating state prices, yields and production costs as a function of their national counterparts. Five hundred iterations of national price and yield projections from the Food and Agricultural Policy Research Institute (FAPRI), along with FAPRI’s average production cost projections, were used as input to the Texas crop model. The stochastic FAPRI Baseline and residuals for Ordinary Least Squares (OLS) equations relating Texas variables to national variables were used to incorporate the risk left unexplained by OLS equations between Texas and U.S. variables. Deterministic and probabilistic NFI projections for Texas crops were compared under the January 2005 and January 2006 FAPRI Baseline projections. With production costs increasing considerably and prices rising moderately in the January 2006 Baseline, deterministic projections of 2006-2014 Texas NFI decreased by an average of 26 percent for corn, 3 percent for cotton, 15 percent for peanuts, and 12 percent for rice, and were negative for sorghum and wheat. Probability distributions of projected NFI fell for all program crops, especially sorghum and wheat. Higher hay price projections caused deterministic projections of NFI for hay to rise roughly 13 percent, and increased the probability distributions of projected hay NFI. Deterministic and probabilistic projections of total NFI decreased for each year, especially for 2006-2008 when fuel price projections were the highest. The Texas crop model can be used to simulate NFI for Texas crop producers under alternative FAPRI baselines. The model shows the impact of baseline changes on probability distributions of NFI for each crop and for Texas as a whole. It can also be useful as a policy analysis tool to compare impacts of alternative farm and macroeconomic policies on NFI.

Limited water supplies for agriculture in the Texas High Plains will require new irrigation technologies and techniques for agriculture to continue in this area. The potential for using one such technology, site-specific irrigation, was evaluated using the Cotton2k crop simulation model. This model and two other simulation models were evaluated for their ability to track water movement and usage over three growing seasons. The models were tested for sites in Lubbock and Hale County, Texas. Cotton2k performed well compared to the other two models on tests of cumulative evapotranspiration and applied water yield relations and equal to the other models for tracking soil water profiles. A global optimization method, simulated annealing, was tested for its ability to spatially calibrate soil water parameters of Cotton2k. The algorithm found multiple parameter sets for the same objective function results. This result runs contrary to expectations for the simulated annealing algorithm, but is possibly from the relationship between available water capacity and crop yield. The annealing algorithm was applied to each sampling point at the Hale County site and improved yield predictions for 32 of 33 points as compared to simulations made with soil textural information alone. The spatially calibrated model was used with historic weather from five seasons to evaluate a site-specific strategy where water was shifted from lower to higher yielding areas of fields. Two irrigation strategies, one with irrigations weekly and one with irrigations applied when 30% of available water was depleted, were tested. With sitespecific management, the weekly interval strategy produced higher yields for two of three water levels, as compared to uniform management. With the soil moisture depletion strategy, site-specific management produced lower yields than uniform management for all three water levels examined. Yield improvement and water savings were also demonstrated for implementing site-specific irrigation when non-producing portions of fields were previously being watered.

Doctor of Philosophy / Department of Agronomy / Stephen M. Welch / Global crop production must be doubled by 2050 to feed 9 billion people. Novel crop improvement methods and management strategies are the sine qua non for achieving this goal. This requires reliable quantitative methods for predicting the behavior of crop cultivars in novel, time-varying environments. In the last century, two different mathematical prediction approaches emerged (1) quantitative genetics (QG) and (2) ecophysiological crop modeling (ECM). These methods are completely disjoint in terms of both their mathematics and their strengths and weaknesses. However, in the period from 1996 to 2006 a method for melding them emerged to support breeding programs. The method involves two steps: (1) exploiting ECM’s to describe the intricate, dynamic and environmentally responsive biological mechanisms determining crop growth and development on daily/hourly time scales; (2) using QG to link genetic markers to the values of ECM constants (called genotype-specific parameters, GSP’s) that encode the responses of different varieties to the environment. This can require huge amounts of computation because ECM’s have many GSP’s as well as site-specific properties (SSP’s, e.g. soil water holding capacity). Moreover, one cannot employ QG methods, unless the GSP’s from hundreds to thousands of lines are known. Thus, the overall objective of this study is to identify better ways to reduce the computational burden without minimizing ECM predictability. The study has three parts: (1) using the extended Fourier Amplitude Sensitivity Test (eFAST) to globally identify parameters of the CERES-Sorghum model that require accurate estimation under wet and dry environments; (2) developing a novel estimation method (Holographic Genetic Algorithm, HGA) applicable to both GSP and SSP estimation and testing it with the CROPGRO-Soybean model using 182 soybean lines planted in 352 site-years (7,426 yield observations); and (3) examining the behavior under estimation of the anthesis data prediction component of the CERES-Maize model. The latter study used 5,266 maize Nested Associated Mapping lines and a total 49,491 anthesis date observations from 11 plantings. Three major problems were discovered that challenge the ability to link QG and ECM’s: 1) model expressibility, 2) parameter equifinality, and 3) parameter instability. Poor expressibility is the structural inability of a model to accurately predict an observation. It can only be solved by model changes. Parameter equifinality occurs when multiple parameter values produce equivalent model predictions. This can be solved by using eFAST as a guide to reduce the numbers of interacting parameters and by collecting additional data types. When parameters are unstable, it is impossible to know what values to use in environments other than those used in calibration. All of the methods that will have to be applied to solve these problems will expand the amount of data used with ECM’s. This will require better optimization methods to estimate model parameters efficiently. The HGA developed in this study will be a good foundation to build on. Thus, future research should be directed towards solving these issues to enable ECM’s to be used as tools to support breeders, farmers, and researchers addressing global food security issues.

Doctor of Philosophy / Department of Agronomy / Johanna A. Dille / Palmer amaranth is a competitive weed and has caused variable corn yield losses in diverse environments of Kansas. The objectives of this study were to 1) determine corn and Palmer amaranth growth, development, and grain (seed) production, 2) determine soil water content throughout the growing season, and 3) evaluate the performance of the modified ALMANAC model for simulating monoculture corn yield and corn yield loss from Palmer amaranth competition when corn and Palmer amaranth were grown alone or in competition under dryland and irrigated environments. For the first objective, field experiments were conducted in 2005 and 2006 with whole-plots of dryland and furrow irrigation arranged in a side-by-side design. Within each soil water environment, sub-plot treatments were monoculture Palmer amaranth at one plant m-1 of row, and corn with zero, one, and four Palmer amaranth plants m-1 of row. Corn height, leaf number, LAI, and total plant dry weight were reduced with increasing water stress and were reduced further in the presence of Palmer amaranth. Corn yield losses were similar with increasing Palmer amaranth density across soil water environments in each year, except for 2006 dryland corn. Palmer amaranth growth and development were negatively impacted by corn interference and weed density. For the second objective, Time Domain Reflectometry measurements documented seasonal trends of volumetric soil water content at the 0 to 15 and 0 to 30 cm soil profile depths for treatments in dryland and irrigated environments each year. The soil water depletion rate increased as water received prior to a drying period increased at the 0 to 30 cm soil depth in the dryland and irrigated environments. For the third objective, the modified ALMANAC model was parameterized based on monoculture corn and Palmer amaranth growth data. The model underestimated monoculture corn yield but overestimated corn yield with Palmer amaranth competition. The model performance was not consistent when comparing simulation results to dryland and irrigated experiments conducted across Kansas. Overall, the experiment provided an improved understanding of corn yield loss risks associated with water management and Palmer amaranth competition.

Abiotic stresses cause extensive losses to agriculture production worldwide. Cotton (Gossypium hirsutum L.) is an important fiber crop grown widely in subtropical region where temperature, water and nutrients are the common factors limiting crop production. Such losses could be more severe in the future climate as intensity and frequency of those stresses are projected to increase. The overall goal of this study was to evaluate effects of abiotic stresses on cotton reproductive performance and develop functional algorithms for fiber properties in response to different stress factors. Three experiments were conducted to evaluate the effects of temperature, water, and nitrogen in naturally-lit growth chambers. Influence of potassium nutrition was conducted in outdoor pot culture facility. In all experiments, upland cotton cultivar TM-1, a genetic standard, was used by imposing treatments at flowering. In all experiments, growth and photosynthesis measurements were recorded frequently during the treatment period. Biomass of various plant- and boll-components determined at harvest when 80% bolls were opened. Boll developmental period was tracked by daily tagging of flowers and open bolls. Bolls were grouped on the basis of onset of anthesis and lint samples were pooled together for fiber analysis. Fiber quality was assessed using High Volume Instrumentation and Advanced Fiber Information System. Total plant biomass, boll weights, and numbers significantly declined for plants grown under low and high temperature, severe water stress and nitrogen and potassium deficient conditions compared to optimum conditions for the respective stresses. Gas exchange processes were severely affected by moisture, nitrogen, and potassium deficient conditions. Time required from flower to open boll was mostly affected by growing temperature but not modified by other stresses. Fiber micronaire was most the responsive to changes in temperature, followed by strength, length and uniformity. Water limiting conditions and nitrogen defficiency severely affected strength and micronaire, whereas potassium deficiency had significant effect on fiber micronaire. This study was used to develop functional algorithms between abiotic stresses and fiber properties, once integrated into the crop simulation model. The improved crop model will be useful assist producers in optimizing planting dates, scheduling irrigation and fertigation to improve and fiber quality.

Master of Science / Department of Statistics / Juan Du / Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Crop models are important tools to improve the management and yield of agricultural systems. These improvements are helpful to meet the growing food and fuel demand without increase the crop areas. The conventional approach for calibrating/validating a crop model considers few to many experiments. However, few experiments could lead to higher uncertainties and a large number of experiments is too expensive. Traditionally, the classical procedure use to share an experimental dataset one part to calibrate and the other to validate the model. However, if only few experiments are available, split it could increase the uncertainties on simulation performance. On the other hand, to calibrate/validate the model using several experiments is too expensive and time consuming. Methods that can optimize these procedures, decreasing the processing time and costs, with a reliable performance are always welcome. The first chapter of this study was conducted to evaluate and compare a statistically robust method with the classical calibration/validation procedure. These two procedure, were applied to estimate the genetic coefficients of the CROPGRO-soybean model, using multiple experiments. The cross-validation leave-one-out method, was applied to 21 experiments, using the NA 5909 RG variety, across a southern state of Brazil. The cross-validation reduced the classical calibration/validation procedure average RMSE from 2.6, 4.6, 4.8, 7.3, 10.2, 677 and 551 to 1.1, 4.1, 4.1, 6.2, 6.3, 347 and 447 for emergence, R1, R3, R5, R7 (days), grains.m-2 and kg.ha-1, respectively. There was stability in the estimated ecotype and genetic coefficient among the 21 experiments. Considering the wide range of environment conditions, the CROPGRO-soybean model provided robust predictions of phenology, biomass and grain yield. Finally, to improve the calibration/validation procedure performance, the cross-validation method should be used whenever possible. For the second chapter of this study, the main objectives were to evaluate the calibration/validation uncertainties using different numbers of experiments and to find out the minimum number of experiments required for a reliable CROPGRO-Soybean simulation. This study also used 21 field experiments (BMX Potencia RR variety) sown in eight different locations of Southern Brazil between 2010 and 2014. The experiments were grouped in four classes (Individual sowings, season/year per location, experimental sites, and all data together). As the grouping level increase, the developmental stages RRMSE (%), decreased from 22.2% to 7.8% from individual sowings to all data together, respectively. The use of only one individual sowings experiment could lead to a RRMSE of 28.4, 48, and 36% for R1, LAI and yield, respectively. However, the largest decrease occurred from the individual sowings to the season/year per location. Then, is recommended, use at least the season/year per location (early, recommended and late sowing dates) class. It will allow understand the behavior of the variety, avoiding the high costs of several experiments and keeping a reliable performance of the model. / Modelos agrícolas são ferramentas importantes para aprimorar técnicas de manejo e consequentemente a eficiência dos sistemas agrícolas. Esse acréscimo na eficiência são úteis para atender a crescente demanda de alimentos e combustíveis, sem avançar a fronteira agrícola. A calibração e validação de um modelo agrícola, historicamente considerou conjuntos de dados que variam de poucos á muitos experimentos. Poucos experimentos podem aumentar as incertezas e muitos experimentos tem alto custo financeiro e demanda de tempo. Pelo método de partição em dois grupos, o conjunto de experimentos é dividido em duas partes, uma para calibrar e a outra validar o modelo. Se apenas um conjunto pequeno de experimentos está disponível, dividi-los pode prejudicar o desempenho do modelo. Assim, métodos que otimizem esses processos, diminuindo o tempo e o custo de experimentos necessários para a calibração e validação, são sempre bem vindos. O objetivo do primeiro capítulo desta tese, foi comparar o método tradicionalmente utilizado na calibração e validação de modelos com um método mais robusto (cross-validation). Ambos os métodos foram aplicados para estimar os coeficientes genéticos na calibração e validação do modelo CROPGRO-soybean, utilizando múltiplos experimentos. Um conjunto com os 3 experimentos mais detalhados foram utilizados para calibração utilizando o método de partição em dois grupos. Já o método cross-validation, foi aplicado utilizando 21 experimentos. A cultivar NA5909 RG foi selecionada por ser uma das mais cultivadas no sul do Brasil nos últimos 5 anos, conduzida em experimentos distribuídos em oitos locais do Estado do Rio Grande do Sul durante as safras de 2010/2011 ate 2013/2014. O método cross-validation reduziu os RMSEs encontrados no método tradicionalmente utilizado de 2.6, 4.6, 4.8, 7.3, 10.2, 677 e 551 para 1.1, 4.1, 4.1, 6.2, 6.3, 347 e 447 para emergência, R1, R3, R5, R7 (em dias), grãos.m-2 e kg.ha-1, respectivamente. Foi observado estabilidade na maioria das estimativas de coeficientes genéticos, o que sugere a possibilidade de utilizar um menor número de experimentos no processo. Considerando a ampla faixa de condições ambientais, o modelo apresentou desempenho satisfatório na previsão fenológica, de biomassa e produtividade. Para otimizar os processos de calibração e validação, indica-se que o método cross-validation seja utilizado sempre que possível. No segundo capítulo, o principal objetivo foi avaliar o desempenho do uso de diferentes números de experimentos, e estimar o número mínimo necessário para garantir desempenho satisfatório do modelo CROPGRO-soybean. Esse estudo também utilizou 21 experimentos, com a cultivar BMX Potência RR. Os experimentos foram organizados em quatro grupos: Grupo 1 (semeaduras individuais), grupo 2 (ano agrícola por local), grupo 3 (local experimental) e grupo 4 (todos os experimentos juntos). Conforme o número de experimentos aumentou, a variabilidade dos coeficientes e os erros relativos (RRMSE) diminuíram. O primeiro grupo apresentou os maiores erros relativos, com até 28.4, 48 e 36% de erros nas simulações de R1, IAF e produtividade, respectivamente. O maior decréscimo nos erros relativos, ocorreu quando avançamos do grupo 1 para o grupo 2. Em alguns casos os erros foram reduzidos em mais que duas vezes. Assim, considerando o elevado custo financeiro e a demanda de tempo que os grupos 3 e 4 apresentam, recomenda-se a escolha de pelo menos o grupo 2, com 3 experimentos no mesmo ano agrícola. Essa estratégia vai permitir um melhor entendimento sobre o desempenho da cultivar, além de calibrar e validar o modelo CROPGRO-soybean, evitando os altos custos de vários experimentos, garantindo o desempenho satisfatório do modelo.

Les modèles de croissance de plantes cherchent à décrire la croissance de la plante en interaction avec son environnement. Idéalement, les paramètres du modèle ainsi défini doivent être stables pour une large gamme de conditions environnementales, et caractéristiques d'un génotype donné. Ils offrent ainsi des nouveaux outils d'analyse des interactions génotype X environnement et permettent d'envisager de nouvelles voies dans le processus d'amélioration génétique chez les semenciers. Malgré tout, la construction de ces modèles et leur paramétrisation restent un challenge, en particulier à cause du coût d'acquisition des données expérimentales. Dans ce contexte, le premier apport de cette thèse concerne l'étude de modèles de croissance. Pour le tournesol (Helianthus annuus L.), il s'agit du modèle SUNFLO [Lecoeur et al., 2011]. Il simule la phénologie de la plante, sa morphogenèse, sa photosynthèse, sous les contraintes de stress abiotiques. Une amélioration de ce modèle a été proposée : il s'agit du modèle SUNLAB, implémentant dans le modèle SUNFLO les fonctions d'allocation de biomasse aux organes, en utilisant les concepts sources puits du modèle GREENLAB [De Reffye et Hu, 2003]. Pour le maïs (Zea mays L.), le modèle CORNFLO, basé sur les mêmes principes que SUNFLO a également été étudié. Ces modèles permettent la différenciation entre génotypes. D'autre part, afin de paramétrer ces modèles, une méthodologie originale est conçue, adaptée au contexte de l'amélioration variétale chez les semenciers : la méthode MSPE (\multi-scenario parameter estimation") qui utilise un nombre restreint de traits expérimentaux mais dans un grand nombre de configurations environnementales pour l'estimation paramétrique par inversion de modèles. Les questions d'identifiabilité, d'analyse de sensibilité, et du choix des méthodes d'optimisation sont discutées. L'influence du nombre de scénarios sur la capacité de prévision du modèle, ainsi que sur l'erreur d'estimation est également étudiée. Enfin, il est démontré que le choix des scénarios dans des classes environnementales différentes (définies par des méthodes de classification - clustering) permet d'optimiser le processus expérimental pour la paramétrisation du modèle, en réduisant le nombre de scénarios nécessaires. / Plant growth models aim at describing the interaction between the growth of plants and their environment. Ideally, model parameters are designed to be stable for a wide range of environmental conditions, and thus to allow characterizing genotypes. They offer new tools to analyze the genotype X environment interaction and they open new perspectives in the process of genetic improvement. Nevertheless, the construction of these models and their parameterization remain a challenge, in particular because of the cost of experimental data collection. In this context, the first contribution of this thesis concerns the study of plant growth models. For sunower (Helianthus annuus L.), the model SUNFLO [Lecoeur et al., 2011] is considered. It simulates the plant phenology, morphogenesis and photosynthesis under abiotic stresses. An extension of this model is proposed: this new SUNLAB model adapts into SUNFLO a module of biomass allocation to organs, using the source-sink concepts inspired by the GREENLAB model [De Reffye and Hu, 2003]. For maize (Zea mays L.), the CORNFLO model, based on the same principles as SUNFLO, was also studied. These models helps discriminating genotypes and analyzing their performances. On the other hand, in order to parameterize these models, an original methodology is designed, adapted to the context of plant variety improvement by breeders. The MSPE methodology (\multi-scenario parameter estimation") uses a limited number of experimental traits but in a large number of environmental configurations for the parameter estimation by model inversion. Issues including identifiability, sensitivity analysis, and the choice of optimization methods are discussed. The influences of environmental scenarios amount on the model predictive ability and on estimation error are also studied. Finally, it is demonstrated that selecting scenarios in different environmental classes (obtained by data clustering methods) allows to optimize the multi-scenario parameter estimation performances, by reducing the required number of scenarios.

La culture de l'ananas s'est fortement développée à la Réunion et représente la première production fruitière de l'île en termes de valeur et de tonnage exporté. L'hétérogénéité des conditions climatiques de l'île ainsi que la diversité des pratiques culturales, notamment en ce qui concerne la fertilisation azotée et l'irrigation, mène à une forte variabilité des rendements, de la qualité gustative des fruits et d'utilisation des ressources naturelles du milieu. Le développement de systèmes de culture plus durables impose de repenser et d'optimiser l'assemblage des pratiques culturales, en prenant en compte les spécificités des différentes zones de production. Un modèle ad-hoc, SIMPIÑA a été développé afin de décrire la croissance et le développement de la plante et la qualité gustative des fruits (teneur en sucres et en acides) en fonction du climat et des pratiques culturales (poids de rejets plantés, densité, date d'induction florale, fertilisation et irrigation). Ce modèle présente la particularité d'intégrer des modules mécanistes (croissance de la plante, teneur en sucre des fruits, bilans hydriques et azotés) et des modules statistiques pour la prévision de l'acidité des fruits à la récolte et la partie économique. Les pratiques culturales sont prises en compte au travers de règles de décision qu'il est ainsi possible d'évaluer. Une typologie des pratiques culturales a été élaborée sur 40 exploitations de l'île, en amont, afin de réduire le champ des possibles et permettre de proposer des systèmes de culture innovants, en optimisant les performances des systèmes tout en prenant en compte les principales contraintes des exploitations. SIMPIÑA a été utilisé pour identifier des combinaisons de pratiques culturales des systèmes qu'il conviendra de tester « au champ ». Cette approche intégrative a permis des avancées significatives au niveau de la modélisation de la culture de l'ananas et de la définition de systèmes de culture innovants. / Pineapple production is increasing on Réunion Island and represents the first fruit production, in terms of value and yield exported. The heterogeneity of climatic conditions on the island and the diversity of cultural practices, particularly with regard to nitrogen fertilization and irrigation, lead to a high variability in yield, gustatory quality of fruit and use of natural resources. The development of more sustainable cropping systems requires rethinking and optimizing the combination of agricultural practices, by taking into account the specificities of the different production areas. An ad-hoc model, SIMPIÑA was developed to describe the growth and development of pineapple plant and fruit quality (sugar and acid content) depending on climate and cultural practices (sucker weight at planting, planting density, date of flowering induction, fertilization and irrigation). This model has the particularity to integrate process-based model modules (plant growth, sugar content, water and nitrogen balance) and statistical modules (for predicting the acidity of fruit at harvest and the economic part). Cultural practices are taken into account through decision rules that may thus be assessed with the model. A typology of cultural practices was carried out based on interviews of 40 farmers all over Réunion Island and led to three farm's types with specific climatic and organizational constraints. SIMPIÑA was used to explore a wide range of combination of cultural practices, taking into account the constraints of each farm-type. We identified trends of cultural practices combinations which optimize the performances of the systems and that should be tested in the field. This integrative approach has led to significant advances in modeling pineapple production and in defining innovative cropping systems.