Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

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

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.

Climate change impact : Regional scenario

Climate change and global warming are going to be the major issues for the 21st century. Their impacts on agriculture, water availability and other natural resources are of serious concern. The paper briefly summarizes the existing information on global warming, past climatic anomalies and occurrence of extreme events vis-a-vis their impact on south Asia in general and Indian in particular. Use of GCM models in conjunction with crop simulation models for impact assessment in agriculture are briefly touched upon. The impact on hydrosphere in terms of water availability and on the forests in India are also discussed. A major shift in our policy makers paradigm is needed to make development sustainable in the face of climate change, global warming and sea level rise.

Simulating impact of climatic variability and extreme climatic events on crop production

Wide range of inter-annual climatic variability and frequent occurrence of extreme climatic events in Indian context is a great concern. There is a need to assess the impact of these events on agriculture production as well suggest the agri-management options for sustenance. The appropriate region specific agro-advisory needs to be established for the farmers and other stake holders. Crop simulation models are effective tools for assessing the crops’ response to these climate related events and for suggesting suitable adaptation procedures for ensuring higher agricultural production. Remote sensing and GIS are effective tools in this regard to prepare the regional based agro-advisories, by linking with the crop simulation models and relational database layers of bio-physical and socio-economic aspects. For effective agro-advisory services, there is a need to link the other biotic and abiotic stresses for accurate estimates and generating window of suitable agri-management options. Crop simulation models can effectively integrate these stresses for crop and soil processes understanding and ultimate yield formation. In this review article, we have discussed about the inter-annual/ seasonal climatic variability and occurrence of extreme climatic events in India and demonstrated the potential of crop models viz., INFOCROP, WTGROWS, DSSAT to assess the impact of these events (also including climate change) on growth and yield of crops and cropping systems and thereby suggesting appropriate adaptation strategies for sustenance. The potential of remote sensing for crop condition assessment and regional/national yield forecast has been demonstrated. Crop simulation tools coupled with remote sensing inputs through GIS can play an important role in evolving this unique operational platform of designing weather based agro-advisory services for India.

Impact of urbanization trends on production of key staple crops

Urbanization has appropriated millions of hectares of cropland, and this trend will persist as cities continue to expand. We estimate the impact of this conversion as the amount of land needed elsewhere to give the same yield potential as determined by differences in climate and soil properties. Robust spatial upscaling techniques, well-validated crop simulation models, and soil, climate, and cropping system databases are employed with a focus on populous countries with high rates of land conversion. We find that converted cropland is 30–40% more productive than new cropland, which means that projection of food production potential must account for expected cropland loss to urbanization. Policies that protect existing farmland from urbanization would help relieve pressure on expansion of agriculture into natural ecosystems.

Effectiveness of using representative subsets of global climate models in future crop yield projections

Representative subsets of global climate models (GCMs) are often used in climate change impact studies to account for uncertainty in ensemble climate projections. However, the effectiveness of such subsets has seldom been assessed for the estimations of either the mean or the spread of the full ensembles. We assessed two different approaches that were employed to select 5 GCMs from a 20-member ensemble of GCMs from the CMIP5 ensemble for projecting canola and spring wheat yields across Canada under RCP 4.5 and 8.5 emission scenarios in the periods 2040–2069 and 2070–2099, based on crop simulation models. Averages and spreads of the simulated crop yields using the 5-GCM subsets selected by T&P and KKZ approaches were compared with the full 20-GCM ensemble. Our results showed that the 5-GCM subsets selected by the two approaches could produce full-ensemble means with a relative absolute error of 2.9–4.7% for canola and 1.5–2.2% for spring wheat, and covers 61.8–91.1% and 66.1–80.8% of the full-ensemble spread for canola and spring wheat, respectively. Our results also demonstrated that both approaches were very likely to outperform a subset of randomly selected 5 GCMs in terms of a smaller error and a larger range.

Spatial and Temporal Evaluation of Satellite Rainfall Estimates Over Vietnam

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

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

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.

Model Validation of Aqua Crop for Brinjal (Solanum melongena) Crop at Madakasira Region, Anantapur District, Andhra Pradesh

Crop simulation models plays a vital role for estimating the effects of soil, water, nutrients on grain and biomass yields and water productivity of different crops. Among the various crop simulation models, Aqua Crop model was adopted for the predicting the crop water requirement in the Madakasira region, Anantapur district, Andhra Pradesh. The Brinjal crop was selected for the study and was irrigated through two different methods i.e., drip and flood irrigation. The model generated the crop yield and crop water requirement for the drip and flood irrigation of Brinjal crop was compared with the actual field results of crop yield and crop water requirement. The simulated crop yield and crop water requirement for the Brinjal crop under flood irrigation was 5.23 t/ha and 326 mm. The actual crop yield and crop water requirement for the Brinjal crop under flood irrigation was 4.2 t/ha and 335 mm. The simulated crop yield and crop water requirement for the Brinjal crop under Drip irrigation was 5.76 t/ha and 318.3 mm. The actual crop yield and crop water requirement for the Brinjal crop under drip irrigation was 4.8 t/ha and 290 mm. From the results, it was clear that the model simulated the actual conditions of the crop. The benefit cost ratio was done for the experimental field data which clearly shows that the crop yield under drip irrigation has achieved the higher cost benefit ratio. Therefore, Aqua Crop model was suitable for simulating the crop conditions under any circumstances.