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Potential impacts of climate change on grain sorghum (Sorghum bicolor) productivity were investigated using the CERES-sorghum model in the Decision Support System for Agrotechnology Transfer v4.5. The model was first calibrated for a sorghum cultivar grown in a free air CO2 enrichment experiment at the University of Arizona, Maricopa, Arizona, USA in 1998. The model was then validated with an independent dataset collected in 1999. The simulated grain yield, growth, and soil water of sorghum for the both years were in statistical agreement with the corresponding measurements, respectively. Neither simulated nor measured yields responded to elevated CO2, but both were sensitive to water supply. The validated model was then applied to simulate possible effects of climate change on sorghum grain yield and water use efficiency in western North America for the years 2080-2100. The projected CO2 fertilizer effect on grain yield was dominated by the adverse effect of projected temperature increases. Therefore, temperature appears to be a dominant driver of the global climate change influencing future sorghum productivity. These results suggest that an increase in water demand for sorghum production should be anticipated in a future high-CO2 world.

This study utilized (a) actual measured agricultural water use along with (b) geostatistical techniques, (c) crop simulation models, and (d) general circulation models (GCMs) to assess irrigation demand and the uncertainty associated with demand projections at spatial scales relevant to water resources management. In the first part of the study, crop production systems in Southwest Georgia are characterized and the crop simulation model error that may be associated with aggregated model inputs is estimated for multiple spatial scales. In the second portion of this study, a methodology is presented for characterizing regional irrigation strategies in the Lower Flint River basin and estimating regional water demand. Regional irrigation strategies are shown to be well represented with the moisture stress threshold (MST) algorithm, metered annual agricultural water use, and crop management data. Crop coefficient approaches applied at the regional scale to estimate agricultural water demand are shown to lack the interannual variability observed with this novel approach. In the third portion of this study, projections of regional agricultural demand under climate change in the Lower Flint River basin are presented. GCMs indicate a range of possible futures that include the possibility of relatively small changes in irrigation demand in the Lower Flint River basin. However, most of the GCMs utilized in this work project significant increases in median water demand towards the end of this century. In particular, results suggest that peak agricultural water demands in July and August may increase significantly. Overall, crop simulation models are shown to be useful tools for representing the intra-annual and interannual variability of regional irrigation demand. The novel approach developed may be applied to other locations in the world as agricultural water metering programs become more common.

In order to reduce the vulnerability of wheat production to drought, a calibrated and validated CERES Wheat crop simulation model was used to predict wheat yields on major soil textural groups using historical weather data at Swift Current, Saskatoon and Melfort. Yields were predicted using a run-out technique which involved the use of actual weather data to the prediction date and historical weather data from 1960 to 1990 for the remainder of the growing season. Yield predictions were made at five Julian dates during the crop calendar and these dates coincided with crop emergence, terminal spikelet initiation, end of the vegetative growth, heading and start of grain filling. Three sample years were used as case studies to test the applicability of the run-out method in making yield predictions. Sample base years were those with the lowest, medium and highest yields between 1960 and 1990 and these were selected from ranked yield values using quartiles. Test years were termed base years and weather files that were joined with the test years were run-out years. Each base year had 30 run-out years (1960-1990) and the mean of each run-out year was compared with the observed yield at the end of the season. Run-out yields for each base year were summarised as simple probability distributions so that yields exceeding certain values could be selected. Run-out yields at five prediction dates were found to be in close agreement with observed yields at the end of the growing season. To account for the variability in yields that can be found between places within the same climatic zone, simulated yields were re-classified by soil type and water stress level. These modifiers (soil type and water stress level) showed that chances of getting high yields diminish from Melfort to Swift Current at all prediction points due to the high variability of yield factors. Yield predictions that were made as above suggested that if historical weather records are combined with available weather data during the growing season, a good indication of yields can be obtained ahead of the harvest time and this could allow producers and those in the agri-business to decide on alternative actions of minimizing losses when prospects of getting a good yield are poor.

Growth models for the sugarcane crop express plant growth based on the net photosynthesis (gross photosynthesis less respiration). Determination of gross photosynthesis (GP) can be made according to the intercepted solar radiation by a rectangular hyperbolic equation. Therefore, this work, conducted at the Center for Agrarian Sciences, Federal University of Alagoas, aimed to evaluate RB varieties (RB92579, RB863129, RB931003, RB93509, RB72454, RB867515, RB951541, RB971755 and RB98710) of irrigated sugarcane in relation to the intercepted photosynthetic active radiation (PARINT) and the estimation of accumulated GP from February 2008 to February 2009. To achieve this, biometric measurements (leaf area and number of tillers) and production variables: tons of cane per hectare (TCH) and tons of sugar per hectare (TSH) were performed in a randomized outline with four repetitions. The photosynthetic irradiance (PAR) was estimated as 44% of the solar irradiation (Rg), and PARINT as the difference between PAR and transmitted PAR (PART). The PART was determined using the Beer law ( ( K IAF) T RFA = RFA exp − ), with the coefficient of light attenuation (K) equal to 0.58. The estimation of daily GP was obtained by a numerical integration with trapezoidal approach. It was also made a temporal analysis of growth through simulations of leaf area index (LAI) according to degree-days (GD). The start of the cultivation (365 days duration) was considered the 1st day of each month for the four years (2004-2007). The TCH and TSH had correlations with the accumulated PARINT and GP during the cycle. The average solar radiation of the region's rainy season, from May to August, (71 to 193 days after ratooning) was 14.9 MJ m-2.. The RB92579 variety had the highest values TCH and TSH as the highest estimates of accumulative PARINT and GP during the cycle. In simulations of growth for the RB varieties of sugarcane, the months of March and February had the highest accumulation PARINT and GP. / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Modelos de crescimento para a cultura da cana-de-açúcar expressam o crescimento das plantas baseados na fotossíntese liquida (fotossíntese bruta menos respiração). Determinações da fotossíntese bruta (FB) podem ser feitas em função da radiação solar interceptada através de uma equação hiperbólica retangular. Assim, objetivou-se com o presente trabalho, realizado no Centro de Ciências Agrárias da Universidade Federal de Alagoas, avaliar variedades RB (RB92579, RB863129, RB931003, RB93509, RB72454, RB867515, RB951541, RB971755 e RB98710) de canas-de-açúcar irrigadas em relação à radiação fotossinteticamente ativa interceptada (RFAINT) e a estimativa da FB acumulada, entre fevereiro de 2008 a fevereiro de 2009. Para isso, foram realizadas medidas biométricas (área foliar e número de perfilhos) e variáveis de produção (toneladas de colmos por hectare (TCH) e toneladas de açúcar por hectare (TPH)), em delineamento experimental em blocos casualizados, com quatro repetições. A irradiância fotossintética (RFA) foi estimada como 44 % da irradiância solar (Rg) e a RFAINT como diferença entre RFA e RFA transmitida (RFAT). A RFAT foi determinada utilizando a Lei de Beer ( ( K IAF) T RFA = RFA exp − ), com o valor do coeficiente de atenuação da luz (K) igual a 0,58. Na estimativa da FB diária, usouse uma integração numérica com abordagem trapezoidal. Também foi feita a análise temporal do crescimento, através de simulações do índice de área foliar (IAF) em função dos graus-dia (GD). Considero-se o inicio do cultivo (365 dias de duração) no 1° dia de cada mês de quatro anos (2004-2007). O TCH e o TPH tiveram correlações com a RFAINT acumulada e com a FB acumulada durante o ciclo. A média da irradiação solar do período chuvoso da região, maio - agosto, (71 - 193 dias após o corte), foi 14,9 MJ m-2. A variedade RB92579 foi a que teve maiores TCH e TPH, com também maiores valores das estimativas da RFAINT e FB acumuladas no longo do ciclo. Nas simulações de crescimento, para as variedades RB de cana-de-açúcar, os meses de março e fevereiro tiveram maiores acumulo RFAINT e FB.

Sendo o cereal mais produzido no mundo e em larga expansão, os sistemas de produção de milho são altamente complexos e sua produção é diretamente dependente de fatores ligados tanto ao clima local quanto ao manejo da cultura. Para auxiliar na determinação tanto dos patamares produtivos de milho quanto quantificar o impacto causado por condições adversas tanto de clima quanto de manejo, pode-se lançar mão do uso de modelos de simulação de culturas. Para que os modelos possam ser devidamente aplicados, uma base solida de dados meteorológicos deve ser consistida, a fim de alimentar esses modelos. Nesse sentido, o presente estudo teve como objetivos: i) avaliar dois sistemas de obtenção de dados meteorológicos, o NASA-POWER e o DailyGridded, comparando-os com dados medidos em estações de solo; ii) calibrar, testar e combinar os modelos de simulação MZA-FAO, CSM DSSAT Ceres-Maize e APSIM-Maize, a fim de estimar as produtividades potenciais e atingíveis do milho no Brasil; iii) avaliar o impacto na produtividade causado pelo posicionamento da semeadura em diferentes tipos de solo; iv) desenvolver e avaliar um sistema de previsão de safra baseado em modelos de simulação; v) mapear as produtividades potencial, atingível e real do milho no Brasil, identificando regiões mais aptas ao cultivo e vi) determinar e mapear as quebras de produtividade, ou yield gaps (YG) da cultura do milho no Brasil. Comparando os dados climáticos dos sistemas em ponto de grade com os dados de estações meteorológicas de superfície, na escala diária, encontrou-se boa correlação entre as variáveis meteorológicas, inclusive para a chuva, com R2 da ordem de 0,58 e índice d = 0,85. O desempenho da combinação dos modelos ao final da calibração e ajuste se mostrou superior ao desempenho dos modelos individuais, com erros absolutos médios relativamente baixos (EAM = 627 kg ha-1) e com boa precisão (R2 = 0,62) e ótima acurácia (d = 1,00). Durante a avaliação da influência das épocas de semeadura e do tipo de solo no patamar produtivo do milho, observou-se que esse varia de acordo com a região estudada e apresenta seus valores máximos e com menores riscos à produção quando a semeaduras coincidem com o início do período de chuvas do local. O sistema de previsão de safra, baseado em modelos de simulação de cultura teve seu melhor desempenho simulando produtividades de milho semeados no início da safra e no final da safrinha, sendo capaz de prever de forma satisfatória a produtividade com até 25 dias antes da colheita. Para o estudo dos YGs, 152 locais foram avaliados e suas produtividades potenciais e atingíveis foram comparadas às produtividades reais, obtidas junto ao IBGE. Os maiores YGs referentes ao déficit hídrico se deram em solos arenosos e durante os meses de outono e inverno, usualmente mais secos na maioria das regiões brasileiras, atingindo valores de quebra superiores a 12000 kg ha-1. Quanto ao YG causado pelo manejo, esse foi maior nas regiões menos tecnificadas, como na região Norte e na Nordeste, apresentando valores superiores a 6000 kg ha-1. Já as regiões mais tecnificadas e tradicionais na produção de milho, como a região Sul e a Centro-Oeste, os YGs referentes ao manejo foram inferiores a 3500 kg ha-1 na maioria dos casos. / Maize is the most important cereal cultivated in the world, being its production system very complex and its productivity directly affected by climatic and crop management factors. In order to quantify the impacts caused by water and crop management deficits on maize yield, the use of crop simulation models is very useful. For properly apply these models, a solid basis of meteorological data is required. In this sense, the present study had as objectives: i) to evaluate two meteorological gridded data, NASA-POWER and DailyGridded, by comparing them with measured data from surface stations; (ii) to calibrate, evaluate and combine the MZA-FAO, CSM DSSAT Ceres-Maize and APSIM-Maize simulation models to estimate the maize potential and attainable yields in Brazil; iii) to evaluate the impact caused by the different sowing dates and soil types on maize yield; iv) to develop and evaluate a crop forecasting system based on crop simulation models and climatological data; v) to map the potential and the attainable maize yields in Brazil, identifying the most suitable regions for cultivation, and vi) to determine and map maize yields and yield gaps (YG) in Brazil. Comparing the gridded climatic data with observed ones, on a daily basis, a good agreement was found for all weather variables, including rainfall, with R2 = 0.58 and d = 0,85. The performances of the combination of the models at the end of the calibration and evaluation phases were better than those obtained with the individual models, with relatively low mean absolute error (EAM = 627 kg ha-1) and with good precision (R2 = 0.62) and accuracy (d = 1.00). During the evaluation of different sowing dates and soil types on maize yield, it was observed that this variable depends on the region and presents the maximum values and, consequently, the minimum risk during the sowings in the beginning of the rainy season of each site. The crop forecasting system, based on crop simulation models, had its best performance for simulating maize yields when the sowings were performed at the beginning of the main season and at the end of the second season, when it was able to predict yield satisfactorily 25 days before harvest. For the YG analysis, 152 sites were assessed and their potential and attainable yields were compared to the actual yields reported by IBGE. The highest YGs caused by water deficit occurred for sandy soils and during the autumn and winter months, usually dry in most of Brazilian regions, reaching values above 12000 kg ha-1. For YG caused by crop management, the values were higher in the less technified regions, such as in the North and Northeast regions, with values above 6000 kg ha-1. In contrast, more traditional maize production regions, such as the South and Center-West, presented YG caused by crop management, lower than 3500 kg ha-1 in most cases.

Sendo o cereal mais produzido no mundo e em larga expansão, os sistemas de produção de milho são altamente complexos e sua produção é diretamente dependente de fatores ligados tanto ao clima local quanto ao manejo da cultura. Para auxiliar na determinação tanto dos patamares produtivos de milho quanto quantificar o impacto causado por condições adversas tanto de clima quanto de manejo, pode-se lançar mão do uso de modelos de simulação de culturas. Para que os modelos possam ser devidamente aplicados, uma base solida de dados meteorológicos deve ser consistida, a fim de alimentar esses modelos. Nesse sentido, o presente estudo teve como objetivos: i) avaliar dois sistemas de obtenção de dados meteorológicos, o NASA-POWER e o DailyGridded, comparando-os com dados medidos em estações de solo; ii) calibrar, testar e combinar os modelos de simulação MZA-FAO, CSM DSSAT Ceres-Maize e APSIM-Maize, a fim de estimar as produtividades potenciais e atingíveis do milho no Brasil; iii) avaliar o impacto na produtividade causado pelo posicionamento da semeadura em diferentes tipos de solo; iv) desenvolver e avaliar um sistema de previsão de safra baseado em modelos de simulação; v) mapear as produtividades potencial, atingível e real do milho no Brasil, identificando regiões mais aptas ao cultivo e vi) determinar e mapear as quebras de produtividade, ou yield gaps (YG) da cultura do milho no Brasil. Comparando os dados climáticos dos sistemas em ponto de grade com os dados de estações meteorológicas de superfície, na escala diária, encontrou-se boa correlação entre as variáveis meteorológicas, inclusive para a chuva, com R2 da ordem de 0,58 e índice d = 0,85. O desempenho da combinação dos modelos ao final da calibração e ajuste se mostrou superior ao desempenho dos modelos individuais, com erros absolutos médios relativamente baixos (EAM = 627 kg ha-1) e com boa precisão (R2 = 0,62) e ótima acurácia (d = 1,00). Durante a avaliação da influência das épocas de semeadura e do tipo de solo no patamar produtivo do milho, observou-se que esse varia de acordo com a região estudada e apresenta seus valores máximos e com menores riscos à produção quando a semeaduras coincidem com o início do período de chuvas do local. O sistema de previsão de safra, baseado em modelos de simulação de cultura teve seu melhor desempenho simulando produtividades de milho semeados no início da safra e no final da safrinha, sendo capaz de prever de forma satisfatória a produtividade com até 25 dias antes da colheita. Para o estudo dos YGs, 152 locais foram avaliados e suas produtividades potenciais e atingíveis foram comparadas às produtividades reais, obtidas junto ao IBGE. Os maiores YGs referentes ao déficit hídrico se deram em solos arenosos e durante os meses de outono e inverno, usualmente mais secos na maioria das regiões brasileiras, atingindo valores de quebra superiores a 12000 kg ha-1. Quanto ao YG causado pelo manejo, esse foi maior nas regiões menos tecnificadas, como na região Norte e na Nordeste, apresentando valores superiores a 6000 kg ha-1. Já as regiões mais tecnificadas e tradicionais na produção de milho, como a região Sul e a Centro-Oeste, os YGs referentes ao manejo foram inferiores a 3500 kg ha-1 na maioria dos casos. / Maize is the most important cereal cultivated in the world, being its production system very complex and its productivity directly affected by climatic and crop management factors. In order to quantify the impacts caused by water and crop management deficits on maize yield, the use of crop simulation models is very useful. For properly apply these models, a solid basis of meteorological data is required. In this sense, the present study had as objectives: i) to evaluate two meteorological gridded data, NASA-POWER and DailyGridded, by comparing them with measured data from surface stations; (ii) to calibrate, evaluate and combine the MZA-FAO, CSM DSSAT Ceres-Maize and APSIM-Maize simulation models to estimate the maize potential and attainable yields in Brazil; iii) to evaluate the impact caused by the different sowing dates and soil types on maize yield; iv) to develop and evaluate a crop forecasting system based on crop simulation models and climatological data; v) to map the potential and the attainable maize yields in Brazil, identifying the most suitable regions for cultivation, and vi) to determine and map maize yields and yield gaps (YG) in Brazil. Comparing the gridded climatic data with observed ones, on a daily basis, a good agreement was found for all weather variables, including rainfall, with R2 = 0.58 and d = 0,85. The performances of the combination of the models at the end of the calibration and evaluation phases were better than those obtained with the individual models, with relatively low mean absolute error (EAM = 627 kg ha-1) and with good precision (R2 = 0.62) and accuracy (d = 1.00). During the evaluation of different sowing dates and soil types on maize yield, it was observed that this variable depends on the region and presents the maximum values and, consequently, the minimum risk during the sowings in the beginning of the rainy season of each site. The crop forecasting system, based on crop simulation models, had its best performance for simulating maize yields when the sowings were performed at the beginning of the main season and at the end of the second season, when it was able to predict yield satisfactorily 25 days before harvest. For the YG analysis, 152 sites were assessed and their potential and attainable yields were compared to the actual yields reported by IBGE. The highest YGs caused by water deficit occurred for sandy soils and during the autumn and winter months, usually dry in most of Brazilian regions, reaching values above 12000 kg ha-1. For YG caused by crop management, the values were higher in the less technified regions, such as in the North and Northeast regions, with values above 6000 kg ha-1. In contrast, more traditional maize production regions, such as the South and Center-West, presented YG caused by crop management, lower than 3500 kg ha-1 in most cases.

Master of Science / Department of Agronomy / Scott A. Staggenborg / Irrigated cropping systems on the High Plains are dominated by water intensive continuous corn (Zea mays L.) production, which along with other factors has caused a decline in the Ogallala aquifer. Potentially demand for water from the aquifer could be decreased by including drought tolerant crops, like grain sorghum (Sorghum bicolor L.) and cotton (Gossypium hirsutum L.), in the cropping systems. This study calibrated the CERES-Maize, CERES-Sorghum, and CROPGRO-Cotton models for the High Plains and studied the simulated effects of different irrigation amounts and initial soil water contents on corn, cotton, and grain sorghum. Input files for calibration were created from irrigated and dryland research plots across Kansas. Information was collected on: soil physical properties, dry matter, leaf area, initial and final soil water content, management, and weather. CERES-Maize simulated grain yield, kernel number, ear number, and seed weight across the locations with root mean square errors (RMSE) of 2891 kg ha-1, 1283 kernels m-2, 1.6 ears m-2, and 38.02 mg kernel-1, respectively. CERES-Sorghum simulated grain yield, kernel number, head number, and seed weight with RMSEs of 2150 kg ha-1, 5755 kernels m-2, 0.13 heads m-2, and 4.51 mg kernel-1. CROPGRO-Cotton simulated lint yield and boll number with RMSEs of 487 kg ha-1 and 25.97 bolls m-2. Simulations were also conducted with CERES-Maize, CERES-Sorghum, and CROPGRO-Cotton to evaluate the effects of irrigation amounts and initial soil water content on yield, evapotranspiration (ET), water use efficiency (WUE), available soil water at maturity, and gross income per hectare. Simulations used weather data from Garden City, KS from 1961 to 1999. Irrigation amounts were different for all variables for corn and grain sorghum. For cotton, yield, WUE, soil water, and gross income were not different between the top two irrigation amounts. For corn and grain sorghum, initial soil water content was only different at 50% plant available water. Initial soil water had no affect on cotton, except for ET at 50%. Simulations showed that cotton yields are similar at lower irrigation. Also, cropping systems that include cotton have the potential to reduce overall irrigation demand on the Ogallala aquifer, potentially prolonging the life of the aquifer.