Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Evaluation of DSSAT-CROPGRO-cotton model to simulate phenology, growth, and seed cotton yield in northwestern India
Agronomy Journal ( IF 2.0 ) Pub Date : 2021-06-29 , DOI: 10.1002/agj2.20788 Sudhir Kumar Mishra 1 , Varinderjit Kaur 2 , Kulvir Singh 3
Agronomy Journal ( IF 2.0 ) Pub Date : 2021-06-29 , DOI: 10.1002/agj2.20788 Sudhir Kumar Mishra 1 , Varinderjit Kaur 2 , Kulvir Singh 3
Affiliation
Crop simulation models are widely used in developed countries to understand the impact of uncertain weather situations on crop production and consequent strategies formulation but their application in developing countries like India is limited and thus needs to be strengthened. The main objective of this study was to evaluate the DSSAT-CROPGRO-cotton (Gossypium hirsutum L.) model for assessing impact of varying weather on productivity dynamics of cotton in northwestern India. To ascertain this, field experiments were carried out under a 3 yr study at two distinct agro-climatic zones/locations for testing and validation of CROPGRO-cotton model (version 4.7) for phenological development and seed cotton yield of four diverse cotton cultivars (F2228, F1861, NCS855BGII, and RCH650BGII) grown under three sowing dates (20 April, 10 May, and 30 May). The results elucidated that phenological events like anthesis and physiological maturity were fairly predicted with high d-index value ≥0.83 and ≥0.89 and low root mean square error (RMSE), that is, ≤2.27 and ≤4.98 d, respectively. Furthermore, aboveground biomass and seed cotton yield also exhibited a RMSE of ≤706 kg ha–¹ (≥0.72, d-index) and ≤126 kg ha–1 (≥0.97, d-index), respectively, indicative of high accuracy. These findings revealed that DSSAT-CROPGRO-cotton model could be exploited for simulating cotton growth, phenology, and yield in northwestern India besides its application in similar arid crop environments across the globe. However, further testing with extensive and long-term datasets is required to improve the utility of model.
更新日期：2021-06-29
京公网安备 11010802027423号