Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model

Bhatia, Virender Singh; Singh, Piara; Wani, S P; Chauhan, G. S.; Rao, A V R K; Mishra, A. K.; Srinivas, K.

doi:10.1016/j.agrformet.2008.03.004

Cited by 139 publications

(90 citation statements)

References 9 publications

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“…High and significant correlation between the seed yield and TIPAR further supports the validity, robustness and generality of the correlation between IPAR and crop growth. The average seed yields of 1.60 t ha -1 for NC and 2.87 t ha -1 for MLBD in both seasons compare well with 2.7 t ha -1 (Bhatia et al, 2008) and 1.7 to 2.3 t ha -1 (Tefera, 2011). It was higher than 2.49 t ha -1 (Adeniyan and Ayoola, 2006) and far above the 1.5 t ha -1 (Akande et al, 2007).…”

Section: Seed Yield Ipar and Ruementioning

confidence: 52%

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2016

IJAPR

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Increasing water productivity and radiation use efficiencies (RUE) of Soybeans are very important in order to improve its production at field level. In this study, we investigated the productivity of Soybeans under the conventional (NC) and six water conservation practices: Tied ridge, Mulch, Soil bund , Tied ridge plus soil bund , Tied ridge plus mulch (TRML) and Mulch plus Soil bund arranged in a randomised complete block design. The objective of the study was to determine the effects of soil water storage (SWS) on land and water productivity and radiation use efficiency ( .3% under water conservation practices. However, WPbiomass and seed yield were not significantly related to RUE. It is hereby concluded that water conservation practices increased the seed yield and water productivity for seed and biomass but did not significantly affect the RUE of the crop. These findings will provide useful information for the development of water and radiation driven models for the production of Soybeans under rainfed conditions.

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Section: Seed Yield Ipar and Ruementioning

confidence: 52%

Untitled

2016

IJAPR

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“…Simulation study by Bhatia et al, 2008, revealed climatic potential of 30 to 35 qt/ha and rainfed potential of 20 to 25 qt/ha. The study also reported that maximum simulated yield achieved for soybean under water nonlimiting condition was 54 qt/ha in Hosangabad district of Madhya Pradesh, 48.6 qt/ha in Amravati district of Maharashtra, and 46.2 qt/ha at Vidisha district of Madhya Pradesh.…”

Section: Varieties Released and Yield Potentialmentioning

confidence: 99%

Soybean improvement through research in India and socio-economic changes

Sharma

Dupare

Patel

2016

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Soybean research work in India which was intended towards yield improvement, biotic and abiotic stress management and food grade characteristics has resulted in 98 varieties notified with maximum yield potential of 4 t/ha which is an increase from 2.5 t/ha during the 1970s, resistant/tolerant to insects, pests and moisture stress as well as with higher nutritional characteristics. The yield index (yield of 1970-71=100) for soybean has increased to 318 since its introduction for commercial cultivation. The technology achievement index has increased from 100 in 1980-81 to 156.3 in 1999-2000 and 182 in 2010-11. The per hectare value of all crops output (at constant 2001-04 prices) increased 250 to 600 per cent in TE 2011-12 over TE 1971-72 in the districts with higher proportion of gross cropped area under soybean and also the per hectare value of output was higher in major soybean growing districts than the other districts with a few exceptions. Though, at present about 45 varieties exist in the seed-chain, however, only four varieties dominate the seed chain and account for about 95 per cent of the total soybean seed requirement in the country and 98 per cent of the seed availability. To bridge the continual increase in demand for edible oil and nutritional requirements, genetic improvement in soybean for attenuating major issues is required.

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“…The CSM-DSSAT models in the DSSAT (v4.0) software package have been tested against various single factors, such as crop yield, soil water, plant-available N, cultivar, planting date and temperature (Boote et al 1998;Alagarswamy et al 2000;Hunt et al 2001;Jones et al 2003;Soler et al 2007;Bhatia et al 2008;Langensiepen et al 2008;Stastna et al 2010;Wang et al 2010). …”

Section: Crop Growth Modelmentioning

confidence: 99%

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

et al. 2015

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. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49Á61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERESÁWheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha(1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.Key words: CERES-Wheat crop model, spring wheat, yield aggregation, regional application (Canada) Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. et Yang, J. 2015. Mise a`l'e´chelle re´gionale des mode`les du rendement des cultures : e´tude de cas avec le DSSAT pour le ble´de printemps cultive´dans les Prairies canadiennes. Can. J. Soil Sci. 95: 49Á61. On recourt souvent aux mode`les agricoles dynamiques a`l'e´chelle de la parcelle ou du champ. Toutefois, une mise a`l'e´chelle s'impose quand on utilise les mode`les articule´s sur les processus pour des applications re´gionales comme la pre´vision du rendement des cultures ou l'e´valuation de l'impact du changement climatique sur la productivite´des cultures dans une re´gion. Les mode`les agricoles dynamiques ont souvent besoin de donne´es de´taille´es sur le climat, le sol et les pratiques agronomiques. C'est pourquoi leur fiabilite´diminue a`l'e´chelon re´gional, l'incertitude des re´sultats de la simulation pouvant s'accroıˆtre avec les incertitudes associe´es aux donne´es saisies. Dans le cadre de cette e´tude, les auteurs ont mode´lise´le rendement du ble´de printemps au niveau de nombreux sols individuels en recourant au mode`le CERES du ble´dans le syste`me d'aide a`la prise de de´cisions pour le transfert des technologies agricoles (...

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Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model

Abstract: a g r i c u l t u r a l a n d f o r e s t m e t e o r o l o g y 1 4 8 ( 2 0 0 8 ) 1 2 5 2 -1 2 6 5 a r t i c l e i n f o

Cited by 139 publications

References 9 publications

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Soybean improvement through research in India and socio-economic changes

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

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