scholarly journals Use of crop simulation models to evaluate limited irrigation management options for corn in a semiarid environment

2008 ◽  
Vol 44 (7) ◽  
Author(s):  
S. A. Saseendran ◽  
L. R. Ahuja ◽  
D. C. Nielsen ◽  
T. J. Trout ◽  
L. Ma
Keyword(s):  
Irrigation Management ◽  
Simulation Models ◽  
Management Options ◽  
Semiarid Environment ◽  
Crop Simulation ◽  
Crop Simulation Models

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

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 113-130
Author(s):  
K. K. SINGH ◽  
NAVEEN KALRA
Keyword(s):  
Remote Sensing ◽  
Climatic Variability ◽  
Simulation Models ◽  
Management Options ◽  
Advisory Services ◽  
Extreme Climatic Events ◽  
Crop Simulation ◽  
Crop Simulation Models ◽  
Wide Range ◽  
The Impact

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.

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

START: A data preparation tool for crop simulation models using web-based soil databases

2018 ◽  
Vol 154 ◽  
pp. 256-264 ◽  
Author(s):  
Kwang Soo Kim ◽  
Byung Hyun Yoo ◽  
Vakhtang Shelia ◽  
Cheryl H. Porter ◽  
Gerrit Hoogenboom
Keyword(s):  
Simulation Models ◽  
Data Preparation ◽  
Web Based ◽  
Crop Simulation ◽  
Crop Simulation Models
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