The maize (Zea mays L.), due to its importance in human and animal diet, is one of the most widespread crop in the world. In Brazil, it is cultivated in almost all regions, due to this, has been the focus of agrometeorological modeling for decades. The AquaCrop model was used in this work in order to simulate the total biomass and daily yield, and get the corn crop forecast for the region of Arapiraca, Alagoas. The model uses the canopy cover (CC), instead of leaf area index (LAI) as a basis for separate calculations of the plant transpiration and evaporation of soil water. The productivity is calculated as the product of biomass and harvest index (HI). The input data of model experiments were performed by Medeiros (2008), in Batingas town in the country of Arapiraca-AL. For four seasons of sowing, the results of soil water storage simulated by the model AquaCrop tended to be similar to those observed variation. However, for the third sowing date had observed the storage maximum value (171.66 mm) at 35 DAE, whereas the maximum simulated (115.0 mm) occurred at 24 DAE. For the final yield biomass (kg ha-1) the maximum and minimum values observed (simulated) ranged from 13.059 (11.861) and 9.873 (8.306) for 3rd and 4th season of planting, respectively. The simulated grain yield was between 4.406 and 2.069 kg ha-1 for the 3rd and 4th sowing time, underestimating by 2.0% (3rd SS) and overestimated by 5.1% (4th SS). The overestimation of the 4th season of sowing due to the adjustment of the depth of the root system at 0.75 m, where for the other seasons of sowing depth considered was 0.60 m (MEDEIROS et al., 2008). The AquaCrop model is a tool to predict corn yield of the AL Bandeirante variety. This procedure allows for adequate estimation of grain yield with 18 days prior to harvest in the Agreste region of Alagoas, providing end users of the model program storage, logistics and marketing of grain crop to be harvested. / Fundação de Amparo a Pesquisa do Estado de Alagoas / O milho (Zea mays L.), devido a sua importância na dieta alimentar humana e animal, é uma das culturas mais difundidas no mundo. No Brasil, é cultivado em praticamente todas as regiões, devido a isto, tem sido foco da modelagem agrometeorológica por décadas. O modelo AquaCrop foi utilizado nesse trabalho com o objetivo de simular a produção de biomassa total e diária, produtividade de grãos, bem como obter a previsão de safra do milho para região de Arapiraca, Alagoas. O modelo usa a cobertura do dossel (CD), em vez do índice de área foliar (IAF), como base para calcular separadamente a transpiração das plantas e a evaporação da água do solo. A produtividade é calculada como o produto da biomassa e do índice de colheita (IC). Os dados de entrada do modelo foram de experimento realizado por Medeiros (2008), no povoado Batingas no município de Arapiraca-AL. Para as quatro épocas de semeio, os resultados do armazenamento de água no solo simulados pelo modelo AquaCrop apresentaram tendência de variação similar aos valores observados. Porém, para terceira época de semeadura o armazenamento observado apresentou valor máximo (171,66 mm) aos 35 DAE, enquanto que o valor máximo simulado (115,0 mm) ocorreu aos 24 DAE. Para a produção de biomassa final (kg ha-1) os valores máximos e mínimos observados (simulados) variaram entre 13.059 (11.861) e 9.873 (8.306) para 3ª e 4ª época de semeadura, respectivamente. A produtividade de grãos simulada foi entre 4.406 e 2.069 kg ha-1, para a 3ª e 4ª época de semeadura, subestimando em 2,0% (3ª ES) e superestimando em 5,1 % (4ª ES). A superestimativa da 4ª época de semeadura deve-se ao ajustamento da profundidade do sistema radicular em 0,75 m, em que para as demais épocas de semeadura a profundidade considerada foi 0,60 m (MEDEIROS et al., 2008). O modelo AquaCrop é uma ferramenta para previsão da produtividade de milho da variedade AL Bandeirante. Esse procedimento permite obter adequada estimativa do rendimento de grãos com 18 dias de antecedência à colheita na região do Agreste Alagoano, disponibilizando aos usuários finais do modelo programar o armazenamento, logística e comercialização da safra de grãos a ser colhida.

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-08-15T13:02:14Z No. of bitstreams: 1 ROBERTA ARAÚJO E SILVA - TESE (PPGMet) 2018.pdf: 3397527 bytes, checksum: d7b0de649c1e410cbeabdeea5fe40e82 (MD5) / Made available in DSpace on 2018-08-15T13:02:14Z (GMT). No. of bitstreams: 1 ROBERTA ARAÚJO E SILVA - TESE (PPGMet) 2018.pdf: 3397527 bytes, checksum: d7b0de649c1e410cbeabdeea5fe40e82 (MD5) Previous issue date: 2018-01-30 / CNPq / Neste estudo foram avaliadas as condições atuais e os efeitos das mudanças climáticas sobre a produtividade e pegada hídrica da soja cultivada na região de Matopiba. Para simular a produtividade da cultura foi usado o modelo AquaCrop versão 5.0 da FAO, calibrado com dados do ano de 2016 e validado com os de 2014, usando parâmetros de clima, solo, cultura e manejo coletados em duas campanhas experimentais realizadas entre os meses de junho e outubro nos anos de 2014 e 2016 em Palmas, TO. O desempenho do modelo foi avaliado utilizando os indicadores estatísticos: erro de previsão (Ep), coeficiente de determinação (R2), raiz quadrada do erro médio (RMSE), erro médio absoluto (EMA), eficiência de Nash e Sutcliffe (NSE), e o índice de concordância de Willmontt´s (d). A calibração e validação da produtividade da cultura de soja estimada pelo modelo AquaCrop, apresentaram resultados satisfatórios, ilustrando a robustez e a aplicabilidade geral do modelo. O modelo AquaCrop subestima a produtividade de grãos de soja, para condições de estresse hídrico severo durante todo o ciclo de cultivo. Após a calibração e validação, o AquaCrop foi utilizado como ferramenta de simulação de produtividade da cultura da soja para o cenário atual (2016) e de mudanças climáticas a médio (2045/2046; 2055/2056) e longo prazo (2075/2076; 2064/2095), alimentado por dados de dois modelos climáticos (HadGEM2-ES e MIROC5) e considerando as RCP 4.5 e 8.5. Em seguida, calculou-se a pegada hídrica (verde, azul e cinza) de soja atual dos principais municípios produtores, de cada estado que compõem a região do Matopiba. Posteriormente, avaliaram-se os efeitos das possíveis mudanças climáticas sob a produtividade e pegada hídrica da soja, considerando as variações climáticas com foco na temperatura, precipitação e CO2. Os modelos climáticos projetaram aumento da produtividade em ambas as RCP consideradas, porém mais acentuado sob a RCP 8.5, em decorrência do aumento da temperatura e concentração de CO2 e a precipitação, que mesmo sofrendo redução nos totais pluviométricos ao longo do tempo, ainda atendendo a necessidade hídrica da soja. As PHsoja atuais da região do Matopiba, variaram de 2036,60 m³t-1 a 2584,12 m³t-1, valores similares aos encontradas na literatura. Sob cenários de mudanças climáticas, a PHsoja decresce ao longos os anos. A PHsoja futura decresce, especialmente a componente verde, devido ao aumento menos acentuado da evapotranspiração, resultando em maior rendimento final. As PHverde diminuem ao longos dos anos, as PHazul aumenta na mesma proporção e as PHcinza apresentam comportamento praticamente continuo. Os resultados deste estudo podem ser usados para quantificar a produtividade futura da soja, a demanda de água e a sua utilização, bem como obter informações úteis para a gestão dos recursos hídricos na região de estudo. / This study evaluated the current conditions and effects of climate change on the productivity and water footprint of soybean cultivated in Matopiba region. To simulate crop productivity, the FAO AquaCrop version 5.0 model was used, calibrated with data from 2016 and validated with 2014, using climate, soil, crop and management parameters collected in two experimental campaigns conducted between the months of June and October in the years 2014 and 2016 in Palmas, TO. The performance of the model was evaluated using the statistical indicators: prediction error (Ep), coefficient of determination (R2), square root mean error (RMSE), mean absolute error (EMA), Nash efficiency and Sutcliffe (NSE) and Willmontt's agreement index (d). Calibration and validation of soybean crop productivity estimated by the AquaCrop model presented satisfactory results, illustrating the robustness and general applicability of the model. The AquaCrop model underestimates soybean grain yield for severe water stress conditions throughout the growing cycle. After calibration and validation, AquaCrop was used as a simulation tool for soybean crop productivity for the current scenario (2016) and medium-term (2045/2046; 2055/2056) and long-term (2075/2076; 2064/2095), fed by data from two climatic models (HadGEM2-ES and MIROC5) and considering RCPs 4.5 and 8.5. Then, the water footprint (green, blue and gray) of the current soybean of the main producing municipalities of each state that compose the Matopiba region was calculated. Subsequently, the effects of possible climatic changes under soybean productivity and water footprint, considering the climatic variations with focus on temperature, precipitation and CO2, were evaluated. The climatic models projected increase of productivity in both RCP considered, but more accentuated under RCP 8.5, due to the increase in temperature and concentration of CO2 and precipitation, that even undergoing a reduction in rainfall totals over time, still taking into account water requirement of soybeans. The current PHsoja of the Matopiba region, ranged from 2036.60 m³t-1 to 2584.12 m³t-1, values similar to those found in the literature. Under scenarios of climate change, the PHsoja decreases over the years. The future PHsoja decreases, especially the green component, due to the less accentuated increase of the evapotranspiration, resulting in greater final yield. PHverde decreases over the years, PHazul increases in the same proportion and PHcinza show practically continuous behavior. The results of this study can be used to quantify future soybean yield, water demand and utilization, as well as to obtain useful information for the management of water resources in the study region.

The Mediterranean basin is widely recognized as a climate change hotspot, with climate models projecting increasingly warmer and drier conditions that will impact local ecosystems, communities, and economies. Agriculture will be among the most affected sectors, with harsher conditions for crops’ growth, greater water needs, and lower yields. One of the most resilient crops to limiting and stressful conditions is barley, which is often sown in areas where other crops and cereals would struggle. This work analyzed the impacts of climate change on rainfed barley using the province of Almeria as a case study. This is one of the most arid areas of the Mediterranean basin, where agriculture is among the main economic resources, and where barley is the main crop produced outside greenhouses. Barley growth was modeled using the AquaCrop model in its Python implementation, AquaCrop-OSPy. Setting the model up to avoid local re-calibration of the barley parameters and to capture multi-year trends in productivity change, rather than its interannual variability. The study focused on two 30-year time periods: mid-century (2041-2070), and end-century (2071-2100); and on Shared Socioeconomic Pathways scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. For each time period and SSP scenario, the research also evaluated three sub-scenarios of soil water content at sowing: with the parameter set respectively at 10%, 20%, and 30% of the Total Available Water (the water present in the soil available for the crop to sustain its life). Having estimated climate change impact, the research analyzed different adaptation pathways (irrigation, the application of mulches, and the change in sowing date), to evaluate their performances for climate change adaptation in the area.  The results indicate the importance of soil water content for maintaining good yields, or reducing losses, and indicate the possible average yield change to be between +14% and -45% at mid-century, and between +12% and -55% at end-century. The greater variability in productivity is associated with the soil water content at sowing rather than on the SSP scenario, with SSP5-8.5 being the only one showing a marked difference compared to the others. Regarding irrigation, the results show how with a soil water content at sowing of 10% of the Total Available Water, irrigation up to 100 m³/ha might not be sufficient to avoid productivity losses. Also, the study indicates that an optimal threshold to trigger irrigation for adaptation purposes might be found between 0% and 20% of the Total Available Water. Overall, it indicates how adaptation through irrigation can be viable in the province. The work moreover suggests the effectiveness of mulches as an adaptation strategy to partially limit irrigation water needs in the future and improve the yield performance of the crop. However, the research does not indicate a clear benefit linked to changing the sowing date to earlier or later sowing dates but suggests the importance of correctly seizing the sowing window to reach optimum yield in the future. Lastly, the work shows that the approach used to carry out this research is suitable to assess trends in yield change at multi-year scale, if the analyzed time window is indicatively larger or equal to 10 years, and if an error of around 10% on the results is accepted. / Medelhavsområdet är allmänt erkänt som en hotspot för klimatförändringar, och klimatmodellerna förutspår allt varmare och torrare förhållanden som kommer att påverka lokala ekosystem, samhällen och ekonomier. Jordbruket kommer att vara en av de mest drabbade sektorerna, med tuffare förhållanden för grödornas tillväxt, större vattenbehov och lägre avkastning. En av de grödor som är mest motståndskraftiga mot begränsande och stressande förhållanden är korn, som ofta sås i områden där andra grödor och spannmål skulle ha svårt att klara sig. I det här arbetet analyserades klimatförändringarnas inverkan på regnkorn med provinsen Almeria som fallstudie. Detta är ett av de torraste områdena i Medelhavsområdet, där jordbruket är en av de viktigaste ekonomiska resurserna, och där korn är den viktigaste grödan som produceras utanför växthus. Kornets tillväxt modellerades med hjälp av AquaCrop-modellen i dess Python-implementering, AquaCrop-OSPy. Modellen ställdes in för att undvika lokal omkalibrering av kornparametrarna och för att fånga fleråriga trender i produktivitetsförändringar, snarare än den mellanårliga variationen. Studien fokuserade på två 30-årsperioder: mitten av århundradet (2041-2070) och slutet av århundradet (2071-2100), och på scenarierna SSP1-2,6, SSP2-4,5 och SSP5-8,5 för de gemensamma socioekonomiska vägarna. För varje tidsperiod och SSP-scenario utvärderade forskningen också tre underscenarier av markvatteninnehåll vid sådd: med parametern inställd på 10%, 20% respektive 30% av det totala tillgängliga vattnet (det vatten som finns i jorden som är tillgängligt för grödan för att upprätthålla sitt liv). Efter att ha uppskattat effekterna av klimatförändringarna analyserade forskningen olika anpassningsvägar (bevattning, applicering av mulcher och förändring av sådatum) för att utvärdera deras prestanda för anpassning till klimatförändringar i området.  Resultaten visar att markvattenhalten är viktig för att upprätthålla god avkastning eller minska förlusterna, och visar att den möjliga genomsnittliga avkastningsförändringen är mellan +14% och -45% vid mitten av århundradet och mellan +12% och -55% vid slutet av århundradet. Den större variationen i produktivitet är förknippad med markvatteninnehållet vid sådd, snarare än på SSP-scenariot, med SSP5-8.5 som det enda som visar en markant skillnad jämfört med de andra. När det gäller bevattning visar resultaten att med en markvattenhalt vid sådd på 10% av det totala tillgängliga vattnet, kan bevattning upp till 100 m³ / ha inte vara tillräcklig för att undvika produktivitetsförluster. Studien visar också att en optimal tröskel för att utlösa bevattning i anpassningssyfte kan hittas mellan 0% och 20% av det totala tillgängliga vattnet. Sammantaget visar studien hur anpassning genom bevattning kan vara genomförbar i provinsen. Arbetet tyder dessutom på att mulcher är effektiva som en anpassningsstrategi för att delvis begränsa bevattningsvattenbehovet i framtiden och förbättra grödans avkastning. Forskningen visar dock inte på någon tydlig fördel med att ändra sådatumet till tidigare eller senare sådatum, men antyder vikten av att korrekt utnyttja såfönstret för att nå optimal avkastning i framtiden. Dessutom visar arbetet att den metod som används för att genomföra denna forskning är lämplig för att bedöma trender i avkastningsförändringar på flerårig skala, om det analyserade tidsfönstret är större eller lika med 10 år, och om ett fel på cirka 10% på resultaten accepteras.

Doctor of Philosophy / Department of Agricultural Economics / Nathan P. Hendricks / Risk averse farmers face a substantial challenge managing irrigation water when they face limited water availability. The two primary reasons for limited water availability in the High Plains Aquifer region of the United States are limited well capacity (i.e., the rate at which groundwater can be extracted) or a constraint imposed by a policy. In this dissertation, I study how risk averse farmers optimally manage limited water availability in the face of weather uncertainty and also the impact of limited water availability on farmer welfare. I use AquaCrop, a daily biophysical crop simulation model, to predict corn yield under alternative irrigation scenarios with historical weather. Since no simple functional form exists for the crop production function, I use discrete optimization and consider 234,256 potential irrigation strategies. I also account for risk preferences by using expected utility analysis to determine the optimal irrigation strategy. Using a daily biophysical model is important because water stress in a short period of the growing season can impact crop yield (even if average water availability throughout the growing season is sufficient) and well capacity is a constraint on daily water use. The daily biophysical crop simulation model accounts for the dynamic response of crop production to water availability. First, I examine how optimal irrigation strategies change due to limited water availability. I find that it is never optimal for irrigators to apply less than a particular minimum instantaneous rate per irrigated acre. An optimal required instantaneous rate implies that a farmer with a low well capacity focuses on adjustment at the extensive margin. On the other hand, farmers who initially have a high well capacity should adjust at the intensive margin in response to well capacity declining. I also find that total water use increases as the degree of risk aversion increases. More risk averse farmers increase water use by increasing irrigation intensity to reduce the variance in corn yields. Another important finding is that a higher well capacity could actually promote less water use because the higher well capacity allows a greater instantaneous rate of application that allows the farmer to decrease irrigation intensity while still maintaining or increasing corn yield. This finding may imply an accelerated rate of groundwater extraction when the groundwater depletion reaches a particular threshold. Second, I analyze the welfare loss due to limited water availability. The relationship between welfare loss and well capacity due to a policy constraint differs by soil type. I found the welfare loss from a water constraint policy does not always increase as well capacity increases. Farmers with very high well capacity may make small or no adjustment at the extensive margin due to a higher instantaneous rate and higher soil water holding capacity. However, that is not the case for a farmer with land that has lower soil water holding capacity as the increase in well capacity results in greater welfare loss. I also investigate the effect of risk averse behavior on the magnitude of welfare loss. I found that the welfare loss per unit of reduced water use is lower for the farmer with more risk aversion. Thus, economic models that ignore risk aversion misestimate the cost of reducing water use. Finally, I investigate the incentive for adopting drip irrigation and its effect on water use. I find that a decrease in well capacity increases the benefits of adopting drip irrigation but is not sufficient to overcome the high initial investment cost without government support. While subsidies of the magnitude offered by current U.S. programs are sufficient to induce drip irrigation adoption, I find that such subsidies have the unintended consequence of increasing total water use, particularly for small well capacities.

[EN] The main objective was aimed at determining the biomass production from water based on water relations, levels of water consumption of the species, and climate variables that affect the formation of biomass and soil present and specifically it focused on two forage crops which were Forage Oats (Avena sativa L.) and ryegrass (Lolium perenne). The solution to the problem is based on AquaCrop model (FAO) Steduto et al, (2009) which was recently proposed for the determination of both biomass and agricultural production from water transpired by herbaceous species, this is gather scientific and technological advances on the effect of water in the estimation of crop production has been achieved in recent years. In the present research we have identified key variables AquaCrop model input for the two species mentioned, through field research conducted using the methodology gradient Hanks et al., (1980), and a new methodology proposal for determining water stress. A total of 18 major experiments were developed to generate the levels of biomass production of forage oats and ryegrass, determining 32 variables or parameters for each crop that requires the model to estimate the biomass produced by the forage and pasture. The experiments were conducted mainly during the years 2008-2013 in "Centro de Investigación Tibaitatá (CORPOICA)" located in the savannah of Bogota (Colombia). For forage oats were developed five cycles and ryegrass crops three complete cycles, with seven, four and two cuts were made respectively. Based on the information provided in the field, forage oats and ryegrass crops were calibrated and validated for AquaCrop model reaching adjustments R2=0.92, RMSE=1,86t.ha-1, NRMSE=17.67%, EF=0.91, d=0.97 for forage oat and R2=0.97, RMSE=0,47t.ha-1, NRMSE=13.6%, EF=0.88, d=0.98 for ryegrass. Two synthetic crops were determined one for each specie, which are "prototypes" for estimating biomass by the water use in different conditions of crops. The total biomass was determined to forage oats at 22.2 t.ha-1 on average with peaks that can reach up to 27 t.ha-1 in the production environment of the savannah of Bogota. For ryegrass total biomass reached levels of up to 9 t.ha-1 on average with peaks that can reach up to 9,9 t.ha-1 for the period from planting to first cut, and 6 t.ha-1 for others cuts. The intrinsic water footprint for fodder oats was determined in 175 L.kg-1 and 442 ryegrass L.kg-1 and 431 L.Kg-1. As alternative outcomes that support the calibration and validation of the model is also obtained, determining reference evapotranspiration in the region, the curves of Kc, Kcb and Ke determining the intrinsic water footprint of the two species, the development of canopy cover over the phenological cycles of the species studied, the curves of soil moisture and biomass in response to six levels of irrigation and various planting and production functions of both species. The conservative parameters of forage oats and ryegrass crops were determined like a sample referenced parameters and local variables nonconservative of these species, which is globally in a significant advance in calibration and validation for AquaCrop model of forage crops. / [ES] El objetivo principal estuvo orientado a la determinación de la producción de biomasa a partir del agua con base en las relaciones hídricas, los niveles de consumo de agua de las especies, y las variables del clima que inciden en la formación de biomasa y el suelo presentes y se centró específicamente en dos cultivos forrajeros los cuales fueron Avena Forrajera (Avena sativa, L.) y Raigrás (Lolium perenne). La solución al problema se basa en el modelo AquaCrop (FAO) Steduto et al, (2009) el cual fue propuesto recientemente para la determinación tanto de la biomasa como de la producción agrícola a partir del agua transpirada por las especies herbáceas, en este se reúnen los avances científicos y tecnológicos que sobre el efecto del agua en la estimación de la producción de cultivos se ha logrado en los últimos años. En el presente trabajo de investigación se han determinado las principales variables de entrada al modelo AquaCrop, para las dos especies mencionadas, por medio de investigación de campo realizada con la metodología del gradiente de Hanks et al, (1980), y con una nueva metodología propuesta para la determinación del estrés hídrico. Se desarrollaron un total de 18 experimentos principales generando los niveles de producción de biomasa de avena forrajera y raigrás, determinando 32 variables o parámetros para cada uno de los cultivos que requiere el modelo para la estimación de la biomasa producida por las especies forrajeras y pastos. Los experimentos fueron realizados principalmente durante los años 2008 a 2013 en el Centro de Investigación Tibaitatá (CORPOICA) ubicado en la Sabana de Bogotá (Colombia). Para avena forrajera se desarrollaron cinco ciclos del cultivo y para raigrás se hicieron tres ciclos completos con siete, cuatro y dos cortes respectivamente. Con base en la información establecida en campo se calibraron y validaron los cultivos de avena forrajera y raigrás para el modelo AquaCrop alcanzando ajustes de R2=0,92, RMSE=1,86t.ha-1, NRMSE=17,67%, EF=0,91, y d=0,97 para avena forrajera y de R2=0,97, RMSE=0,47t.ha-1, NRMSE=13,6%, EF=0,88, y d=0,98 para raigrás. Se determinaron dos cultivos sintéticos uno para cada especie, los cuales constituyen los "prototipos" que son el punto de partida para la estimación de la biomasa a partir del uso del agua en diferentes condiciones de los cultivos mencionados. La biomasa total para avena forrajera fue determinada en 22,2t.ha-1 en promedio con valores máximos que pueden llegar a alcanzar hasta 27t.ha-1 en el entorno productivo de la sabana de Bogotá. Para raigrás la biomasa total alcanzó niveles de hasta 9t.ha-1, para el período de siembra a primer corte, y de 6t.ha-1 para los cortes posteriores al primero. La huella hídrica intrínseca para avena forrajera fue determinada en 175L.kg-1 y para raigrás en 442L.kg-1 y 431L.Kg-1. Como resultados alternativos que fundamentan la calibración y validación del modelo se obtuvieron además, la determinación de la evapotranspiración de referencia de la región, las curvas de Kc, Kcb y Ke que determinan la huella hídrica de las dos especies, los desarrollos de las coberturas del dosel a través de los ciclos fenológicos de las especies estudiadas, las curvas de humedad del suelo y de biomasa en respuesta a seis niveles de riego y varias épocas de siembra y las funciones de producción de ambas especies. Se determinaron los parámetros conservativos de los cultivos de avena forrajera y raigrás, así como una muestra referenciada localmente de los parámetros y variables no conservativos de estas especies, lo que constituye a nivel mundial en un avance significativo en la calibración y validación del modelo AquaCrop para cultivos forrajeros. / [CAT] L'objectiu principal va estar orientat a la determinació de la producció de biomassa a partir de l'aigua amb base en les relacions hídriques, els nivells de consum d'aigua de les espècies, i les variables del clima que incideixen en la formació de biomassa i el sòl presents i es va centrar específicament en dos cultius forrajeros els quals van ser Civada Forrajera (Avena sativa, L.) i Raigrás (Lolium perenne). La solució al problema es basa en el model AquaCrop (FAO) Steduto et al, (2009) el qual va ser proposat recentment per a la determinació tant de la biomassa com de la producció agrícola a partir de l'aigua transpirada per les espècies herbàcies, en aquest es reuneixen els avanços científics i tecnològics que sobre l'efecte de l'aigua en l'estimació de la producció de cultius s'ha aconseguit en els últims anys. En el present treball de recerca s'han determinat les principals variables d'entrada al model AquaCrop, per a les dues espècies esmentades, per mitjà de recerca de camp realitzada amb la metodologia del gradient d'Hanks et al, (1980), i amb una nova metodologia proposada per a la determinació de l'estrès hídric. Es van desenvolupar un total de 18 experiments principals generant els nivells de producció de biomassa de civada forrajera i raigrás, determinant 32 variables o paràmetres per a cadascun dels cultius que requereix el model per a l'estimació de la biomassa produïda per les espècies forrajeras i pastures. Els experiments van ser realitzats principalment durant els anys 2008 a 2013 en el Centre de Recerca Tibaitatá (CORPOICA) situat en la Sabana de Bogotà (Colòmbia). Per a civada forrajera es van desenvolupar cinc cicles del cultiu i per a raigrás es van fer tres cicles complets amb set, quatre i dues corts respectivament. Amb base en la informació establida en camp es van calibrar i van validar els cultius de civada forrajera i raigrás per al model AquaCrop aconseguint ajustos de R2=0,92, RMSE=1,86t.ha-1, NRMSE=17,67%, EF=0,91, i d=0,97 per a civada forrajera i de R2=0,97, RMSE=0,47t.ha-1, NRMSE=13,6%, EF=0,88, i d=0,98 per a raigrás. Es van determinar dos cultius sintètics un per a cada espècie, els quals constitueixen els "prototips" que són el punt de partida per a l'estimació de la biomassa a partir de l'ús de l'aigua en diferents condicions dels cultius esmentats. La biomassa total per a civada forrajera va ser determinada en 22,2t.ha-1 en mitjana amb valors màxims que poden arribar a aconseguir fins a 27t.ha-1 en l'entorn productiu de la sabana de Bogotà. Per a raigrás la biomassa total va aconseguir nivells de fins a 9t.ha-1, per al període de sembra a primer tall, i de 6t.ha-1 per als corts posteriors al primer. La petjada hídrica intrínseca per a civada forrajera va ser determinada en 175L.kg-1 i per a raigrás en 442L.kg-1 i 431L.kg-1. Com a resultats alternatius que fonamenten el calibratge i validació del model es van obtenir a més, la determinació de la evapotranspiración de referència de la regió, les corbes de Kc, Kcb i Ke que determinen la petjada hídrica de les dues espècies, els desenvolupaments de les cobertures del dosel a través dels cicles fenológicos de les espècies estudiades, les corbes d'humitat del sòl i de biomassa en resposta a sis nivells de reg i diverses èpoques de sembra i les funcions de producció d'ambdues espècies. Es van determinar els paràmetres conservatius dels cultius de civada forrajera i raigrás, així com una mostra referenciada localment dels paràmetres i variables no conservatius d'aquestes espècies, la qual cosa constitueix a nivell mundial en un avanç significatiu en el calibratge i validació del model AquaCrop per a cultius forrajeros. / Terán Chaves, CA. (2015). Determinación de la huella hídrica y modelación de la producción de biomasa de cultivos forrajeros a partir del agua en la Sabana de Bogotá (Colombia) [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/54133 / TESIS

Agriculture is the mainstay of the economy in Malawi and accounts for 40% of the Gross Domestic Product (GDP) and 90% of the export revenues. Corn (maize) is the major cereal crop grown as staple food under rainfed conditions, covers over 92% of the total agricultural area, and contributes 54% of the caloric intake. Corn production is the principle occupation and major source of income for over 85% of the total population in Malawi. Issues of hunger and food insecurity for the entire nation are associated with corn scarcity and low production. Global warming is expected to cause climate change in Malawi, including changes in temperature and precipitation amounts and patterns. These climate changes are expected to affect corn production in Malawi. This study evaluates the impacts of climate change on rainfed corn production in Malawi. Lilongwe District, with about 1,045 square miles of agriculture area, has been selected as a representative area. First, outputs of 15 General Circulation Models (GCMs) under different emission scenarios are statistically downscaled. For this purpose, a weather generator (LARSWG) is calibrated and validated for the study area and daily precipitation as well as minimum and maximum temperature are projected for 15 GCMs for three time horizons of 2020s, 2050s and 2090s. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. AquaCrop, a new model developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed corn production in the study area with and without climate change. Study results indicate an average temperature increase of 0.52 to 0.94oC, 1.26 to 2.20oC and 1.78 to 3.58oC in the nearterm (2020s), mid-term (2050s) and long-term (2090s) future, respectively. The expected changes in precipitation during these periods are -17 to 11%, -26 to 0%, and -29 to -3%. Corn iii yields are expected to change by -8.11 to 0.53%, -7.25 to -14.33%, and -13.19 to -31.86%, during the same time periods. The study concludes with suggestion of some adaptation strategies that the Government of Malawi could consider to improve national food security under climate change.

The assessment of regional bioenergy potentials from different types of natural land cover is an integral part of simulation tools that aim to assess local renewable energy systems. This work introduces a new workflow, which evaluates regional bioenergy potentials and its impact on water demand based on geographical information system (GIS)-based land use data, satellite maps on local crop types and soil types, and conversion factors from biomass to bioenergy. The actual annual biomass yield of crops is assessed through an automated process considering the factors of local climate, crop type, soil, and irrigation. The crop biomass yields are validated with historic statistical data, with deviation less than 7% in most cases. Additionally, the resulting bioenergy potentials yield between 10.7 and 12.0 GWh/ha compared with 13.3 GWh/ha from other studies. The potential contribution from bioenergy on the energy demand were investigated in the two case studies, representing the agricultural-dominant rural area in North Germany and suburban region in South Germany: Simulation of the future bioenergy potential for 2050 shows only smaller effects from climate change (less than 4%) and irrigation (below 3%), but the potential to cover up to 21% of the transport fuels demand in scenario supporting biodiesel and bioethanol for transportation.

West Africa is subject to frequent yield losses due to erratic rainfall and degraded soils. At the same time, its population is expected to double by 2050. This situation is alarming in northern Togo, a West African dry savannah area, where rainfed maize is a staple food. Thus, it is necessary to improve agricultural productivity, e.g., by evaluating and introducing alternative irrigation management strategies, which may be implemented in this region. For this purpose, the present investigation focused on evaluating the potential of deficit and supplemental irrigation, as well as assessing the impact of climate and soil variability on maize yield under irrigated agriculture using irrigation optimisation strategies in northern Togo. The Optimal Climate Change Adaption Strategies in Irrigation (OCCASION) framework was adapted and employed to address the research objectives. It involves: (i) a weather generator for simulating long-term climate time series; (ii) the AquaCrop model, which was utilised to simulate the irrigation during the growing periods and the maize yield response to given irrigation management strategies; and (iii) a problem-specific algorithm for optimal irrigation scheduling with limited water supply. Five irrigation management strategies viz. T1: no irrigation (NI), T2: controlled deficit irrigation (CDI) and T3: full irrigation (FI) in the wet season, T4: controlled deficit irrigation (CDI) and T5: full irrigation (FI) in the dry season were assessed regarding their impact on maize yield in northern Togo. The results showed high variability in rainfall during the wet season, which led to substantial variability in the expected yield for NI. This variability was significantly lessened when optimised supplemental irrigation management strategies (CDI or FI) were applied. This also holds for the irrigation scenarios under the dry season. Finally, these findings were validated by an irrigation field experiment conducted at an agricultural research institute in northern Togo. Under a moderate level of deficit irrigation during the vegetative and reproductive growth stages, the above-ground biomass and the maize grain yield were reduced. However, a moderate level of deficit irrigation during the vegetative growth stage could result in similar values of water productivity to that of fully irrigated treatment. It was found that, based on the values of the statistical indicators, AquaCrop has accurately simulated the maize grain yield for all the irrigation strategies evaluated. The results of this study revealed that climate variability might engender a higher variability in the maize yields of northern Togo than soil variability does. Large- and smallscale water harvesting, access to groundwater, and irrigation infrastructures would be required for implementing the irrigation management strategies assessed in this study.:Declaration iii Declaration of Conformity v Dedication vii Acknowledgements ix Abstract xi Table of Contents xv List of Figures xvii List of Tables xix List of Acronyms and Abbreviations xxi 1. Introduction 1 1.1 Background and Problem Statement 1 1.1.1 Global Fresh and Agricultural Water Use 1 1.1.2 Erratic Rainfall, Rising Temperatures, and Soil fertility depletion in West Africa 2 1.1.3 Transboundary Water Issues in West Africa 3 1.1.4 Agriculture and Water Use in Togo 3 1.2 Objectives of the Study 4 2. State of the Art 6 2.1 Relevant Agroecosystems, Farming Systems and Irrigation Management in West Africa 6 2.2 Key Performance Indicators: Water productivity and Food Security 8 2.3 Common Approaches Used to Evaluate Crop Water Productivity 9 2.4 Key production Factors: Climate, Soil and Management 9 2.5 Crop Yield Modelling 12 2.6 Integrated Modelling 13 3. Novel Framework for Optimising Irrigation Systems in West Africa 15 3.1 Model-based Sensitivity Analysis of Climate and Management Impact on Crop Water Productivity, Water Demand and Food Security 15 3.2 Experimental Validation of the Farm Model and Management Strategies, Soil Data Analysis and Modelling 17 3.3 Joint Stochastic Analysis of the Impact of Climate and Soil Variability on Crop Water Productivity and Food Security 19 4. Overview of Publications 21 4.1 Potential of Deficit and Supplemental Irrigation under Climate Variability in Northern Togo, West Africa 21 4.2 Impact of Irrigation Strategies on Maize (Zea mays L.) Production in the Savannah Region of Northern Togo (West Africa) 22 4.3 Impact of climate and soil variability on maize (Zea mays L.) yield under full and deficit irrigation in the savannah region of northern Togo, West Africa 23 5. Conclusion and Outlook 26 References 28 A. Selected Publications of the Author 37 A.1 Potential of Deficit and Supplemental Irrigation under Climate Variability in Northern Togo, West Africa 39 A.2 Impact of Irrigation Strategies on Maize (Zea mays L.) Production in the Savannah Region of Northern Togo (West Africa) 61 A.3 Impact of Climate and Soil Variability on Maize (Zea mays L.) Yield under Full and Deficit Irrigation in the Savannah Region of Northern Togo, West Africa 81 B. Histograms of distributions of the expected maize yield in northern Togo (scenarios in the third paper) 121

Crop improvement is necessary for food security as the global population is expected to exceed 9 billion by 2050. Limitations in water resources and more frequent droughts and floods will make it increasingly difficult to manage agricultural resources and increase yields. Therefore, we must improve our ability to monitor agronomic research plots and use the information they provide to predict impacts of moisture stress on crop growth and yield. Towards this end, agronomists have used reductions in leaf expansion rates as a visible ‘plant-based’ indicator of moisture stress. Also, modeling researchers have developed crop models such as AquaCrop to enable quantification of the severity of moisture stress and its impacts on crop growth and yield. Finally, breeders are using Unmanned Aircraft Systems (UAS) in field-based High-Throughput Phenotyping (HTP) to quickly screen large numbers of small agronomic research plots for traits indicative of drought and flood tolerance. Here we investigate whether soybean canopy cover and color time series from high-resolution UAS ortho-images can be collected with enough spatial and temporal resolution to accurately quantify and differentiate agronomic research plots, pinpoint the timing of the onset of moisture stress, and constrain crop models such as AquaCrop to more accurately simulate the timing and severity of moisture stress as well as its impacts on crop growth and yield. We find that canopy cover time series derived from multilayer UAS image ortho-mosaics can reliably differentiate agronomic research plots and pinpoint the timing of reductions in soybean canopy expansion rates to within a couple of days. This information can be used to constrain the timing of the onset of moisture stress in AquaCrop resulting in a more realistic simulation of moisture stress and a lower likelihood of underestimating moisture stress and overestimating yield. These capabilities will help agronomists, crop modelers, and breeders more quickly develop varieties tolerant to moisture stress and achieve food security.

With a changing climate and an increased competition over water resources for agricultural irrigation, the need to improve crop water productivity using time and cost-efficient methodologies have become critically important. The Malwathu Oya river basin in Sri Lanka is struggling with water scarcity, which threatens food security and the income of farmers. In this study, freely available remote sensed land- and water productivity data from FAO’s WaPOR database was evaluated. The evaluation consisted of a comparison of the WaPOR data and primary collected field data using the crop water model, AquaCrop, for three irrigation schemes in the Malwathu Oya river basin. Additionally, the spatio-temporal variability in crop water use within and across these three irrigation schemes was assessed using indicators derived from the WaPOR portal. The evaluation was conducted for the main cultivation season, called Maha, between 2010 and 2021.  The WaPOR and AquaCrop actual evapotranspiration (ETa) values were found to be in relatively good agreement (312–537 and 400–465 mm respectively). WaPOR yield values (2.5–2.9 ton/ha) were however lower compared to the AquaCrop simulated yield values and historical yield data (4.6–5.7 and 4.4–5.6 ton/ha respectively). Difference in calculation methodology, possible sources of error in WaPOR conversion calculations and limitations in accuracy caused by cloud coverage when collecting satellite data could be explanations for this. Prior knowledge and accurate allocation of the crop type and parameters used in conversion calculations in WaPOR is therefore of significant influence. From the spatio-temporal variation assessment with WaPOR indicators, a fair uniformity of the water distribution within the irrigation schemes was shown (CV 11–19 %). The beneficial water use (BWU) in the irrigation schemes showed lower values (50–90 % allocated to T) for years when the available water amount was higher, which could be explained by the higher rate of water lost through soil evaporation. Crop water productivity (CWP) values showed higher values (about 0.70 kgDM/m3) when the available water amount was higher, indicating that yield production is sensitive to water-scarce environments. Applying a yield boundary function, representing the best attainable yield in relation to water resource, showed that there is potential to achieve the same yield with less amount of water. There are thus possibilities for improved water productivity in the three irrigation schemes investigated. For future research it is recommended to perform a sensitivity analysis for WaPOR and ground truth with yield data to obtain a better understanding of potential limitations. To obtain more precise site descriptions it is also recommended to ground truth AquaCrop with yield and soil data.