A Review of Crop Growth Simulation Models as Tools for Agricultural Meteorology

Rauff, Kazeem O.; Bello, Rasaq

doi:10.4236/as.2015.69105

Cited by 44 publications

(30 citation statements)

References 14 publications

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“…These models are expressed as regression equations (with one or a few parameters) and are based on direct descriptions of the observed data. They do not provide information about the mechanisms that give rise to response [13]. As a result, they are less data intensive than dynamic crop models.…”

Section: Types Of Crop Growth Modelsmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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Section: Types Of Crop Growth Modelsmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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“…The APSIM-and-CERESMaize models have been used effectively to simulate maize growth, development, and yield (Rauff and Bello, 2015). The performance of APSIM-and-CERES-maize models is limited by the quality of input data such as daily weather data, soil profile characterization data, surface residues, crop management, initial soil condition and plant growth analysis.…”

Section: Crop Simulation Modelsmentioning

confidence: 99%

“…Therefore, system analysis and simulations provide critical roles in developing this understanding of options. A proper understanding of climate change and its impact on crop yield would assist scientists, planners and policy makers to sensitize and guide farmers to make informed discussions as it relates to aspects of proper select of crops, and cultivar, dates of planting, application of irrigation water and scheduling to reduce the risks (Rauff and Bello, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The application of GCMs, crop simulation-andstatistical downscaling models to understand climate change impact on crop growth and yield offers a direct link between climate models, crop models, economic models, agrometeorology and concerns of society (Rauff and Bello, 2015). Additionally, integrated use of GCMs, statistical downscaling techniques, and crop simulation models provide an approach that applied scientific vigor in assessing the impact of climate change on agricultural production and world food security compared to other surveys.…”

Section: Introductionmentioning

confidence: 99%

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Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

Chisanga¹,

Phiri²,

Chinene³

2017

Sci. Res. Essays

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Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

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“…New agricultural research is needed to provide additional information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate change around the world. Therefore many researchers have over the years shown interest in finding ways to estimate the yield of crops before harvest [1]. In this context plant growth and development models should be elaborated to supply a basis for planning and managing crop production [2].…”

Section: Introductionmentioning

confidence: 99%