Using Climate and Crop Simulation Models for Assessing Climate Change Impacts on Agronomic Practices and Productivity

2014 ◽  
pp. 201-219 ◽  
Author(s):  
Winfred B. Mbungu ◽  
Henry F. Mahoo ◽  
Siza D. Tumbo ◽  
Frederick C. Kahimba ◽  
Filbert B. Rwehumbiza ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Simulation Models ◽  
Agronomic Practices ◽  
Crop Simulation ◽  
Crop Simulation Models

scholarly journals Crop Simulation Models: A Tool for Future Agricultural Research and Climate Change

2021 ◽  
pp. 146-154
Author(s):  
Asma Fayaz ◽  
Y. Rajit Kumar ◽  
Bilal Ahmad Lone ◽  
Sandeep Kumar ◽  
Z. A. Dar ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Prediction Accuracy ◽  
Developmental Stages ◽  
Agricultural Research ◽  
Simulation Models ◽  
Decision Making Process ◽  
Crop Simulation ◽  
Crop Simulation Models ◽  
Traditional Research

A crop simulation model is a computerized program which is used to describe the process of growth and developmental stages of crop in relation to weather data, crop conditions and soil conditions to solve the real-world problems. Crop simulation models plays an important role in decision making process as these models can save time and resources. The prediction accuracy of simulation models is one of the most vital components in decision making process. Our review shows the prediction accuracy and efficiency of the simulation models like DSSAT and APSIM. We have compared the prediction accuracy of these models on various growth and development stages of crops along with yield prediction. Both the models have performed well while predicting various growth and developmental stages of crops. The present scenario of traditional research is site-specific, Resource consuming and time consuming. Hence the information obtained through traditional research by qualitative analysis has many limitations, Because of changing climate and weather parameters there is a need for computerized based statistical tool which can provide decision support system for more than 10-15 years. By this we strongly believe that Crop simulation models can be a vital tool in future agricultural research and climate change mitigation strategies.

scholarly journals Importância dos modelos de simulação de culturas diante os impactos das alterações climáticas sobre a produção agrícola - Revisão

2021 ◽  
Vol 14 (6) ◽  
pp. 3648
Author(s):  
Antonio Gebson Pinheiro ◽  
Luciana Sandra Bastos de Souza ◽  
Alexandre Maniçoba da Rosa Ferraz Jardim ◽  
George do Nascimento Araújo Júnior ◽  
Cleber Pereira Alves ◽  
...  
Keyword(s):  
Climate Change ◽  
Simulation Models ◽  
Climatic Effect ◽  
Yield Gap ◽  
Agricultural Income ◽  
Crop Simulation ◽  
Agricultural Vulnerability ◽  
Crop Simulation Models ◽  
Productive Potential ◽  
Impacts Of Climate Change

O efeito climático é o principal responsável pelas oscilações no rendimento produtivo. Logo, é esperado que as mudanças do clima promovam alterações na agricultura, comprometam a sustentabilidade e a segurança alimentar, especialmente, em áreas semiáridas. O entendimento da amplitude desses fatores e suas consequências no rendimento agrícola mediante os diferentes cenários climáticos, regionais e tecnológicos são fundamentais nas tomadas de decisões. Para as análises desses diversos cenários, os modelos de simulação de culturas se caracterizam como ferramentas funcionais e com eficientes performances na estimativa dos níveis de produtividades, desde que devidamente calibrados e validados com dados consistentes e representativos. Dentre os modelos de simulação podemos destacar: AquaCrop - FAO, ZAE - FAO, CROPGRO e Apsim como aqueles de maiores aplicabilidades nas culturas agrícolas, sendo utilizados de maneira recorrente em diversos estudos para fins do conhecimento das lacunas de produtividade agrícola, ou “Yield Gap”. Esta revisão analisou os impactos das alterações climáticas na agricultura e o levantamento de informações dos principais modelos de simulação de culturas. Mediante síntese das informações levantadas, pode-se evidenciar o eminente impacto das alterações climáticas sobre o cenário agrícola futuro, proporcionando maior vulnerabilidade agrícola. Logo, destaca-se a importância do uso de modelos de simulação de culturas para conhecimento das lacunas de produtividade e potencial produtivo. Contudo, é evidente a necessidade de pesquisas mais detalhadas sobre a aplicabilidade dos modelos em cenários agrícolas diversos e situações climáticas amplas.Palavras-chave: modelos de simulação; sazonalidade climática; práticas resilientes; “yield gap”. Importance of crop simulation models in view of the impacts of climate change on agricultural production – Review A B S T R A C TThe climatic effect is the main responsible for the fluctuations in the productive yield. Therefore, it is expected that climate change will promote changes in agriculture, compromise sustainability and food security, especially in semi-arid areas. Understanding the breadth of these factors and their consequences on agricultural income through different climatic, regional and technological scenarios are fundamental in decision-making. For the analysis of these different scenarios, the crop simulation models are characterized as functional tools and with efficient performances in the estimation of the productivity levels, as long as they are properly calibrated and validated with consistent and representative data. Among the simulation models we can highlight: AquaCrop - FAO, ZAE - FAO, CROPGRO and Apsim as those with the greatest applicability in agricultural crops, being used in a recurring manner in several studies for the purpose of understanding agricultural productivity gaps, or “Yield Gap”. This review analyzed the impacts of climate change on agriculture and the gathering of information on the main crop simulation models. By synthesizing the information collected, it is possible to highlight the imminent impact of climate change on the future agricultural scenario, providing greater agricultural vulnerability. Therefore, the importance of using crop simulation models to understand the gaps in productivity and productive potential is highlighted. However, there is a clear need for more detailed research on the applicability of models in diverse agricultural scenarios and broad climatic situations.Keywords: simulation models; climatic seasonality; resilient practices; yield gap.

Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

2021 ◽  
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Thomas P. Higginbottom ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
...  
Keyword(s):  
Smallholder Farmers ◽  
Simulation Models ◽  
Extreme Weather ◽  
Index Insurance ◽  
Yield Estimation ◽  
Crop Phenology ◽  
Yield Data ◽  
Basis Risk ◽  
Crop Simulation ◽  
Crop Simulation Models

<p>Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers. </p><p>In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets – a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insurance</p><p>We apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.</p>

Crop Simulation Models in Agronomic Systems

1986 ◽  
pp. 141-208 ◽  
Author(s):  
F.D. Whisler ◽  
B. Acock ◽  
D.N. Baker ◽  
R.E. Fye ◽  
H.F. Hodges ◽  
...  
Keyword(s):  
Simulation Models ◽  
Crop Simulation ◽  
Crop Simulation Models

Crop simulation models for yield prediction.

2009 ◽  
pp. 576-601 ◽  
Author(s):  
M. R. Anwar ◽  
G. O'Leary ◽  
J. Brand ◽  
R. J. Redden
Keyword(s):  
Simulation Models ◽  
Yield Prediction ◽  
Crop Simulation ◽  
Crop Simulation Models
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