Agroecosystem Modeling

Dear Colleagues,

Agroecosystem is a human-induced ecosystem managed for the production of food, fuel and fiber. It covers 1/4th of the global land surface area. In a quantitative way, it is an area where almost 30% of the land is dedicated to croplands or intensively managed pastures. The complexity of this system is different across the globe. Presently, around 7.5 billion people live on planet Earth, and in future, this number might be in the range of 8.5 to 12 billion. Thus, to feed the billions of people around the world, we need to manage our agroecosystem. In the past, intensification/overexploitation of agroecosystems with irrigation, agronomic managements, improved crop varieties, agrochemicals and agricultural machinery resulted in enhanced food production. However, no climate-smart agricultural options were available in the past that resulted in the degradation of this whole ecosystem. Problems such as greenhouse gas emissions, smog, erosion, salinization, water pollution, eutrophication, loss in biodiversity, and insect and pest prevalence are predominantly due to inaccurate agroecosystem management. Therefore, if these problems are not to be addressed on an urgent basis at ground scale, they might jeopardize the development possibilities of future generations. The understanding of the mechanisms/processes responsible for the degradation of the agroecosystem could reverse these negative trends and can help to develop new strategies from gene to field scale. Models are a good tool to describe the response of agroecosystems under different sets of biotic and abiotic scenarios. At present, there are different process-based agroecosystem models available that can be used to solve “what if” questions in this era of climate change. These models are helpful in ideotype designing, phenotyping, understanding of Genotype (G) x Environment (E) x Management (M) interactions, crop physiological mechanisms, water and nutrient management, conservation and precision agriculture, insect, pest, and disease forecasting, soil organic carbon dynamics, socioeconomic analysis, and climate impact assessments. However, to get reliable information from all these models, we need to have a good-quality data set.

Dr. Ahmed Mukhtar
Guest Editor

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• agroecosytems modeling
• cropping systems
• climate change
• genotype (G) x environment (E) x management (M)
• conservation agriculture
• intercropping
• remote sensing