The performance of the CROPGRO model for bean (Phaseolus vulgaris L.) yield simulation

Oliveira, Evandro Chaves de; Costa, José Maria Nogueira da; Júnior, Trazilbo José de Paula; Ferreira, Williams Pinto Marques; Justino, Flávio; Neves, Leonardo de Oliveira

doi:10.4025/actasciagron.v34i3.13424

Cited by 20 publications

(17 citation statements)

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“…The flowering was estimated as 54 days after sowing (DAS), the onset of the first pod as 61 DAS and the physiological maturity as 96 DAS. Other studies had already proved the ability of this model in simulating the phenology of other cultivars of beans, corroborating our findings (MEIRELES et al, 2002;OLIVEIRA et al, 2012). (Figure 3).…”

Section: Model Calibrationsupporting

confidence: 81%

“…Table 3 demonstrates that the model simulated adequately crop yield, with maximum variation of 5.0%, reflecting the accuracy occurred in model calibration. OLIVEIRA et al (2012), calibrating the same model for different bean cultivars, found variations of up to 15.94% between simulated and field-observed yields. …”

Section: Model Calibrationmentioning

confidence: 99%

“…In addition, these studies are limited to answer promptly further questions generated, mainly involving weather and its interaction with crop production factors. In such a context, computer models are alternative tools to predict crop yields as function of weather conditions and crop and soil management practices instantly (OLIVEIRA et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Specific crop coefficients (provided in a program file) and genetic coefficients of cultivars (calibrated for the genotype of interest) enable simulations of further responses of a given cultivar under varied environmental conditions. In Brazil, the DSSAT software has been used to evaluate growth and production of crops such as common beans (OLIVEIRA et al, 2012;DALLACORT et al, 2011), cowpea beans (LIMA FILHO et al, 2013), soybeans (RODRIGUES et al, 2012;CHEVARRIA et al, 2013), corn (ALVES et al, 2011), sugarcane (NASSIF et al, 2012MARIN et al, 2013;GOMES et al, 2014. ), and perennial forages (PEDREIRA et al, 2011;LARA et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Calibration and Testing of Cs-Cropgro Model for Common Beans

et al. 2016

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Simulation models of crop growth enable estimating crop yield and water balance components with low cost and high precision. This study aimed to calibrate and evaluate the performance of the CS-CROPGRO model for common beans under the conditions of Jaboticabal city (SP), Brazil. The model was calibrated and tested with data previously collected from two experiments conducted in 2002 and 2003. In these experiments, treatments corresponded to two irrigation scheduling methods (tensiometers and class A pan evaporation) and two cropping systems (conventional and no-till). For calibration, data from the experiment carried out in 2002 were used. For testing, we used data from the experiment conducted in 2003. By using the genetic coefficients calibrated for cultivar IAC-Carioca, the model could correctly simulate phenology, leaf area, dry matter and grain yield during both, calibration and testing, under no-till and conventional systems, combined with tensiometer and class A pan irrigation scheduling methods. Conversely, the model did not show the same performance for soil moisture simulations.

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Section: Model Calibrationsupporting

confidence: 81%

Section: Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Calibration and Testing of Cs-Cropgro Model for Common Beans

et al. 2016

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“…COSTA et al (2015), analisando a resposta do milho à diferentes lâminas de irrigação, em Mossoró-RN, encontraram lâmina de 410 mm à 788 mm. OLIVEIRA et al (2012) comentam quanto à importância da necessidade de irrigação suplementar, mesmo em períodos em que as chuvas são mais frequentes, uma vez que há irregularidades na sua distribuição. Na Figura 5 está representada a redução esperada na produtividade potencial da água para o cultivo do milho sem o uso da irrigação suplementar, para as datas estudadas.…”

Section: Resultsunclassified

Estimativa Das Necessidades Hídricas Do Milho Cultivado Nas Condições Edafoclimáticas De São Mateus – Es

Sales¹,

Louzada²,

Oliveira³

et al. 2016

Encicl. Biosf.

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RESUMOA água é um recurso que limita muito a produtividade do milho na região norte do Espírito Santo. O objetivo do trabalho foi estimar a precipitação efetiva, evapotranspiração da cultura, lâmina de irrigação e quebra de produtividade da água no cultivo do milho na região de São Mateus -ES, para diferentes épocas de semeadura. Para identificar o requerimento de água para o milho, foi utilizado o programa CROPWAT 8.0, que calcula o balanço hídrico da cultura a partir de dados climáticos, de solo e da espécie cultivada. Os resultados mostraram que o CROPWAT recomendou o uso de irrigação nas nove datas de semeadura investigadas. No entanto, a antecipação da data de semeadura para 01/09, 15/09 e 1/10, possibilitará menores lâminas de irrigação suplementar e menor quebra de produtividade de água no cultivo do milho na região. PALAVRAS-CHAVE: Clima, solo, evapotranspiração, quebra de produtividade ESTIMATED NEEDS WATER MAIZE IN THE EDAFOCLIMATIC CONDITIONS OF SÃO MATEUS -ESABSTRACT Water is a resource that greatly limits the productivity of maize in the northern region of the Espírito Santo. The objective was to estimate the effective precipitation, evapotranspiration, water depth and water productivity drop in corn cultivation in the region of São Mateus -ES to different sowing times. For identify the water requirement for corn was used CROPWAT 8.0 software, which calculates the water balance of culture from climatic, soil and cultivated species. The results showed that the CROPWAT recommended the use of irrigation while the nine investigated sowing dates. However, early sowing date for 01/09, 15/09 and 1/10 enable smaller blades of supplemental irrigation and break water productivity in maize cultivation in the region.

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Assessing high-impact spots of climate change: spatial yield simulations with Decision Support System for Agrotechnology Transfer (DSSAT) model

Eitzinger

Läderach²,

Rodríguez

et al. 2016

Mitig Adapt Strateg Glob Change

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Drybeans (Phaseolus vulgaris L.) are an important subsistence crop in Central America. Future climate change may threaten drybean production and jeopardize smallholder farmers’ food security. We estimated yield changes in drybeans due to changing climate in these countries using downscaled data from global circulation models (GCMs) in El Salvador, Guatemala, Honduras, and Nicaragua. We generated daily weather data, which we used in the Decision Support System for Agrotechnology Transfer (DSSAT) drybean submodel. We compared different cultivars, soils, and fertilizer options in three planting seasons. We analyzed the simulated yields to spatially classify high-impact spots of climate change across the four countries. The results show a corridor of reduced yields from Lake Nicaragua to central Honduras (10–38 % decrease). Yields increased in the Guatemalan highlands, towards the Atlantic coast, and in southern Nicaragua (10–41 % increase). Some farmers will be able to adapt to climate change, but others will have to change crops, which will require external support. Research institutions will need to devise technologies that allow farmers to adapt and provide policy makers with feasible strategies to implement them.Electronic supplementary materialThe online version of this article (doi:10.1007/s11027-015-9696-2) contains supplementary material, which is available to authorized users.

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The performance of the CROPGRO model for bean (Phaseolus vulgaris L.) yield simulation

Cited by 20 publications

References 20 publications

Calibration and Testing of Cs-Cropgro Model for Common Beans

Calibration and Testing of Cs-Cropgro Model for Common Beans

Estimativa Das Necessidades Hídricas Do Milho Cultivado Nas Condições Edafoclimáticas De São Mateus – Es

Assessing high-impact spots of climate change: spatial yield simulations with Decision Support System for Agrotechnology Transfer (DSSAT) model

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