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 haź (≥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. INTRODUCTIONCrop simulation models integrate the scientific knowledge frommany disciplines including agricultural and allied sciences. Models can accurately evaluate individual and/or joint
A field experiment was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Faridkot and Bathinda during rainy (kharif) season 2017 to evaluate the performance of Bt and non Bt cotton (Gossypium hirsutum L.) cultivars under different sowing environments. The experiment was laid out in split plot design with 3 sowing dates (April 20, May 10 and May 30) in main plots and 4 American cotton cultivars [2 Bt cultivars (NCS 855 BGII and RCH 650 BGII) and 2 non Bt cultivars (F 2228 and F 1861)] in sub-plots. Results of the pooled data indicated that early sown (April 20) crop accumulated more dry matter production, higher crop growth rate (CGR) as well as relative growth rate (RGR) followed by crop sown on May 10 and May 30. Maximum CGR (14.35–15.48 g/m2/day) was obtained during 90–120 DAS (days after sowing) while RGR was highest during 60–90 DAS. Among tested cultivars, F 1861 exhibited better CGR and RGR values and hence, accumulated higher dry matter (1303.0 g/m2) followed by F 2228 (1276.9 g/m2), NCS 855 BGII (1261.1 g/m2) and RCH 650 BGII (1206.7 g/m2). Dry matter accumulation in fruiting bodies has started around 90 DAS and accounted for 30–35% of total above ground biomass. Bt cultivar NCS 855 BGII, accumulated higher dry matter in fruiting bodies (458.1 g/m2), though at par with RCH 650 BGII (432.2 g/m2) but, significantly higher than F 1861 (403.3 g/m2) and F 2228 (401.9 g/m2). Dry matter accumulation towards fruiting bodies in Bt cultivars was ~9% higher than non Bt cultivars which may be prime reason for better yield performance of Bt cotton.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.