Paper/Poster/Presentation Title
Keywords
Remote sensing, Heterogenous agroecosystems, Agricultural systems, Models
Start Date
16-9-2020 2:00 PM
End Date
16-9-2020 2:20 PM
Abstract
Most African agricultural landscapes have constantly reported low productivities due to water scarcities, poor water management systems and poor agricultural practices. Poor understanding of these agroecosystem functioning has made it hard to run them productively. Development of robust agricultural system models are needed for improved system understanding. However, this has been a great challenge due to absence of vital data or presence of poor-quality data. In this study we implemented an approach of integrating high resolution remote sensing data into an agricultural systems model, RZWQM2, for improved simulation of an African agroecosystem. A stepwise modelling approach was employed that began with soil system simulation followed by a soil-plant-atmosphere system simulation using the RZWQM2. In each modelling step, remote sensing data was utilized for model development, calibration and validation. Integration of field measurements into the model also supported with improved simulations of ET, biomass, yield and the surface energy balance components. The study revealed that high evaporation losses occurred during early crop development stages from exposed soils. This greatly affected the amount of soil moisture available for the crops. Soil water storage and depletion insights linked to water stress during the different crop development stages were also revealed which were analysed against existing agricultural practises, water sharing arrangements and irrigation amounts. This provided vital information for evaluating the performance of the rainfed/irrigated agricultural system in place while also providing information on crop water requirements and advice on optimum irrigation amounts and schedules. From this study, it’s clear that modern remote sensing products are needed for agricultural system modelling for improved modelling and understanding. There is still need to better incorporate management practices into these models to better understand and quantify their impacts on water management and yield.
Integrating high resolution remotely sensed data into agricultural systems models in African agroecosystems
Most African agricultural landscapes have constantly reported low productivities due to water scarcities, poor water management systems and poor agricultural practices. Poor understanding of these agroecosystem functioning has made it hard to run them productively. Development of robust agricultural system models are needed for improved system understanding. However, this has been a great challenge due to absence of vital data or presence of poor-quality data. In this study we implemented an approach of integrating high resolution remote sensing data into an agricultural systems model, RZWQM2, for improved simulation of an African agroecosystem. A stepwise modelling approach was employed that began with soil system simulation followed by a soil-plant-atmosphere system simulation using the RZWQM2. In each modelling step, remote sensing data was utilized for model development, calibration and validation. Integration of field measurements into the model also supported with improved simulations of ET, biomass, yield and the surface energy balance components. The study revealed that high evaporation losses occurred during early crop development stages from exposed soils. This greatly affected the amount of soil moisture available for the crops. Soil water storage and depletion insights linked to water stress during the different crop development stages were also revealed which were analysed against existing agricultural practises, water sharing arrangements and irrigation amounts. This provided vital information for evaluating the performance of the rainfed/irrigated agricultural system in place while also providing information on crop water requirements and advice on optimum irrigation amounts and schedules. From this study, it’s clear that modern remote sensing products are needed for agricultural system modelling for improved modelling and understanding. There is still need to better incorporate management practices into these models to better understand and quantify their impacts on water management and yield.
Stream and Session
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