The world is progressing towards a more sustainable society, where renewable energy sources, including solar energy, play a crucial role. This study aims to address the conflict between agriculture and energy production by exploring the installation of solar panels on farmland. Four scenarios are considered, with varying parameters such as latitude, azimuth, slope, and row distance between photovoltaic (PV) modules. The study focuses on two different crops, lettuce and potato, which have varying tolerances to shading. The objective is to understand how the shadows cast by solar panels affect the growth of these crops. To analyze the impact of shading, the PVsyst software program is utilized to obtain PAR values for each scenario. The calculations are performed using Excel equations. The literature review encompasses scientific sources that provide insights into both PV technology and agriculture, bolstering the research findings. To ensure realism and manage simulation time, certain delimitations were made. These include limiting the study to two cities in Sweden, comparing only two crops, and conducting simulations during the summer period. The results reveal a significant potential for growing potatoes under PV modules. However, lettuce faces difficulties due to its high requirement for solar intensity (PAR), making it less adaptable to shade. The findings of this study indicate that crops like potatoes, which have a lower requirement for PAR, can be successfully cultivated in conjunction with photovoltaic (PV) systems. However, it is not advisable to implement AV systems in areas where sensitive crops like lettuce, which necessitate a significant number of sun hours with high solar intensity.

Agriculture and food security are interlinked. New technologies and instruments are making the agricultural system easy to operate and increasing the food production. Remote sensing technology is widely used as a non-destructive method for crop growth monitoring, climate analysis, and forecasting crop yield. The objectives of this study are to (1) monitor crop growth remotely, (2) identify climate impacts on crop yield, and (3) forecasting crop yield. This study proposed methods to improve crop growth monitoring and yield predictions by using remote sensing technology. In this study, we developed crop vegetative growth metrics (VGM) from the MODIS (Moderate Resolution Imaging Spectroradiometer) 250m NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data. We developed 19 NDVI and EVI based VGM metrics for soybean crop from a time series of 2000 to 2018, but the methods are applicable to other crops as well. We found VGMmax, VGM70, VGM85, VGM98T are about 95% crop yield predictable. However, these metrics are independent of climatic events. We modelled the climatic impacts on soybean crop from the time series data from1980-2019 collected from NOAA's National Climatic Data Center (NCDC). Therefore, we estimated the impacts of increase and decrease of temperature (maximum, mean, and minimum) and precipitation (average) pattern on crop yields which will be helpful to monitor climate change impacts on crop production. Lastly, we made crop yield forecasting statistical model across different climatic regions in USA using Google Earth Engine. We used remotely sensed MODIS Terra surface reflectance 8-day global 250m data to calculate VGM metrics (e.g. VGM70, VGM85, VGM98T, VGM120, VGMmean, and VGMmax), MODIS Terra land surface temperature and Emissivity 8-Day data for average day-time and night-time temperature and CHIRPS (Climate Hazards Group Infra-red Precipitation with station data) data for precipitation, from a time series data of 2000-2019. Our predicted models showed a NMPE (Normalized Mean Prediction error) with in a range of -0.002 to 0.007. These models will be helpful to get an overall estimate of crop production and aid in national agricultural strategic planning. Overall, this study will benefit farmers, researchers, and management system of U.S. Department of Agriculture (USDA).

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The federal crop insurance program provides crop insurance for millions of acres and many commodities every year. The Risk Management Agency of the USDA is responsible for determining the premium rates for these covered commodities. Currently, the quality of soil is not considered when determining baseline yields and expected premium rates. This study utilizes the moment-based maximum entropy method to assess the effect of incorporating soil in the rate making methodology. Several moments of upland cotton yield in Arkansas, Mississippi, and Texas are conditioned on weather, irrigation, and soil control variables. Ultimately, I find evidence of mispriced premium rates for counties in all three states for both irrigated and non-irrigated upland cotton yield.

In the last decade, Brazilian soybean and maize, cultivated in succession, accounted for 23.8 ± 1.9% and 6.9 ± 0.9% of world\'s production, respectively. More than 80% of soybean and maize production in Brazil is under rainfed conditions, which results in a high interannual yield variability and, consequently, increasing the risks for food supply, not only in the country but also around the world. Among the natural phenomena that cause climate and yield variability in Brazil, El Niño Southern Oscillation (ENSO) is the most important. The best way to minimize the impacts of ENSO, mainly those associated to water deficit in rainfed crops, is by defining the most favorable sowing dates, when the probability of crop failure is small. Based on that, this study aimed: to determine the best sowing dates for the soybean-maize production system, based on the economic profitability at national scale; to assess the influence of the ENSO phases (El Niño, La Niña and Neutral) on spatial and temporal soybean and maize off-season yield variabilities for different sowing dates; and to determine the magnitude of the current soybean- maize succession yield gap due to water deficit and crop management in different Brazilian producing regions. To achieve such goals, soybean and maize off-season simulations were performed using three previously calibrated and validated crop simulation models (FAO-AZM, DSSAT and APSIM), in a multi-model approach. Soybean and maize yields were simulated for 29 locations in 12 states, with soybean sowing dates ranging from 21st September to 1st January, for a period of 34 years (1980-2013). Maize sowings were simulated in the same day soybean was harvested. The optimal sowing dates for soybean-maize succession varied according to the Brazilian region, with water deficit, solar radiation and air temperature being the main weather variables that influenced this crop system. ENSO phases affected soybean and maize yields across the country, having, in general, opposite effects during the warm (El Niño) and cold (La Niña) phases, but also depending on the sowing date considered. The yield gap (YG) of soybean-maize succession varied among locations, sowing dates and growing seasons. However, the yield gaps caused by water deficit (YGw) were, on average, higher than those caused by sub-optimal crop management (YGm), which can be explained by the high inter-annual and spatial climate variability observed in the Brazilian territory. / Na última década, a soja e o milho safrinha, cultivados em sucessão no Brasil, contribuíram com 23.8 ± 1.9% e 6.9 ± 0.9% da produção mundial, respectivamente. Mais de 80% da soja e do milho brasileiro são produzidos em condições de sequeiro, o que resulta em uma alta variabilidade interanual da produtividade e, consequentemente, aumenta os riscos de falhas no abastecimento alimentar no Brasil e no mundo. Entre os fenômenos causadores da variabilidade climática e da produtividade agrícola no Brasil, o El Niño Oscilação Sul (ENOS) é o mais importante. A melhor maneira para minimizar os impactos do ENOS, principalmente os associados ao déficit hídrico em culturas de sequeiro, é definindo as datas de semeaduras mais favoráveis, onde as chances de grandes perdas são menores. Assim, os objetivos deste estudo foram: determinar a melhor data de semeadura para o sistema de produção em sucessão soja - milho safrinha, baseado na rentabilidade econômica em escala nacional; indicar a influência das fases do ENOS (El Niño, La Niña e Neutro) sobre a sucessão soja - milho safrinha em escala espacial e temporal, em diferentes datas de semeaduras; e determinar a magnitude da quebra de produtividade da sucessão soja - milho safrinha devido ao déficit hídrico e ao manejo sub ótimo do cultivo. Para atingir os objetivos, simulações de produtividade para soja e milho safrinha foram realizadas usando três modelos de simulação de cultura (FAO-AZM, DSSAT e APSIM), previamente calibrados, em uma abordagem multi-modelos. As produtividades das culturas da soja e do milho foram simuladas para 29 locais em 12 estados, com as datas de semeadura da soja variando de 21 de setembro a 1º de janeiro, para um período de 34 anos (1980-2013). A semeadura do milho ocorreu imediatamente após a colheita da soja. A data de semeadura ótima para a sucessão soja - milho safrinha variou de acordo com a região brasileira, tendo o déficit hídrico, radiação solar e a temperatura do ar como as principais variáveis que influenciam o sistema. As fases do ENOS afetaram a produtividade da soja e do milho safrinha no Brasil, tendo, efeitos opostos durante as fases quentes (El Niño) e frias (La Niña). Os impactos das fases do ENOS também variaram de acordo com as datas de semeadura. As quebras de produtividade da sucessão soja - milho safrinha variaram entre os locais, datas de semeadura e safras. Entretanto, as quebras de produtividade causadas pelo déficit hídrico foram, em média, superiores àquelas causadas pelo manejo subótimo das culturas, o que pode ser explicado pela alta variabilidade espacial e interanual das condições meteorológicas no território brasileiro.

Bibliography: leaves 155-169. Studies the effect of nitrogen fertilizer on dry matter production under differing watering regimes. Investigates the accuracy of different crop and soil organic matter models for predicting crop yield, nitrogen uptake and changes in soil organic carbon and nitrogen. Compares the models with data from long-term field experiments on wheat in Australia and sorghum in Ethiopia. Finds that a higher crop yield and better nitrogen and water utilisation can be achieved if addition of nitrogen fertilizer is balanced with soil water.

In 2025, there will be almost 8 billion people to feed as the worlds population rapidly increases. To meet domestic and export demands, Australian grain productivity needs to approximately triple in the next 20 years, and this production needs to occur in an environmentally sustainable manner. The advent of Hi-tech Precision Farming in Australia has shown promise in recent time to optimize the use of resources. Most

Bibliography: leaves 155-169. / xiv, 169 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Studies the effect of nitrogen fertilizer on dry matter production under differing watering regimes. Investigates the accuracy of different crop and soil organic matter models for predicting crop yield, nitrogen uptake and changes in soil organic carbon and nitrogen. Compares the models with data from long-term field experiments on wheat in Australia and sorghum in Ethiopia. Finds that a higher crop yield and better nitrogen and water utilisation can be achieved if addition of nitrogen fertilizer is balanced with soil water. / Thesis (Ph.D.)--University of Adelaide, Dept. of Agronomy & Farming Systems, 1997?

Bibliography: leaves 155-169. / xiv, 169 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Studies the effect of nitrogen fertilizer on dry matter production under differing watering regimes. Investigates the accuracy of different crop and soil organic matter models for predicting crop yield, nitrogen uptake and changes in soil organic carbon and nitrogen. Compares the models with data from long-term field experiments on wheat in Australia and sorghum in Ethiopia. Finds that a higher crop yield and better nitrogen and water utilisation can be achieved if addition of nitrogen fertilizer is balanced with soil water. / Thesis (Ph.D.)--University of Adelaide, Dept. of Agronomy & Farming Systems, 1997?

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2003. / Härtill 5 uppsatser.