Simulação do risco de deficit hídrico em regiões de expansão do cultivo de cana-de-açúcar no Brasil

Vianna, Murilo dos Santos; Sentelhas, Paulo César

doi:10.1590/s0100-204x2014000400001

Cited by 25 publications

(16 citation statements)

References 19 publications

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“…More than 60% of the variability in crop yield can be explained by the edaphoclimatic factors (Ray et al, 2015). Due to the complexity in understanding the factors of the soil-plantatmosphere system, crop growth models are effective tools to evaluate the effect of agricultural management practices on the growth and yields of crops of interest (Singels et al, 2014;Vianna & Sentelhas, 2014;.…”

Section: Introductionmentioning

confidence: 99%

Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Coelho

Dalri

Filho

et al. 2020

Rev. bras. eng. agríc. ambient.

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Model calibration is a fundamental factor to obtain high accuracy in the estimation of crop growth and yield. This study aimed to parameterize the genetic and ecotype coefficients of the DSSAT/Canegro model for five sugarcane cultivars kept under three water managements, besides evaluating the accuracy of the model in predicting sugarcane stalk yield, sugar yield and height. Experimental field data were obtained from two years (2016 and 2017) of cultivation at FCAV/Universidade Estadual Paulista, Jaboticabal, SP, Brazil. The cultivars were maintained under supplementary irrigation, deficit irrigation and no irrigation. Data of the supplementary irrigation treatment (without stress) were used for the parameterization of each cultivar. Model accuracy was assessed by Pearson correlation (r), root mean squared error (RMSE), mean bias error (MBE), index of agreement (d) and confidence coefficient (c). The DSSAT/Canegro model is highly accurate in predicting stalk and sugar yields of sugarcane grown under water regimes, presenting itself as a viable alternative in sugarcane yield simulation. For better performance of the DSSAT/Canegro model, it is necessary to parameterize the variables related to the ecotype of the cultivars, besides the specific coefficients of the cultivars.

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Section: Introductionmentioning

confidence: 99%

Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Coelho

Dalri

Filho

et al. 2020

Rev. bras. eng. agríc. ambient.

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“…The water deficit risk for sugarcane cultivation varies with each soil type in terms of its water retention capacity. In this case, sandy soils are those that present a greater susceptibility, independent of the place and planting date (Vianna & Sentelhas, 2014). Acric and Alic soils and soils with low water retention are typically observed in the Cerrado biome, which limits crop growth, resulting in increased susceptibility to drought.…”

Section: Introductionmentioning

confidence: 99%

Drought tolerance of the sugar cane varieties during the initial development

Silverio¹,

Silva²,

Bonfim-Silva³

et al. 2017

Aust J Crop Sci

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This study was conducted to identify the water-deficit tolerant sugarcane varieties (Saccharum officinarum L.) by cultivating them under mild-to-severe water deficit conditions. The experiments were performed in a greenhouse, employing the randomized block design in a 7 x 5 factorial scheme corresponding to seven sugarcane varieties (RB867515, RB92579, RB855536, RB72454, RB073036, RB073028, and RB073040) and five types of soil water stress (12, 24, 36, 48, and 60 kPa). Water stress was maintained throughout the experiment period. The experimental units included 10 dm 3 pots, with three replications, accounting for 105 pots in total. Height, diameter, and stalk dry mass were the variables analyzed and evaluations were done 90 days post transplantation. Finally, the RB073028, RB867515, and RB72454 varieties were identified as being tolerant to severe water stress. The varieties of RB073036 and RB073040 could tolerate moderate water deficit and the RB92579 and RB855536 varieties showed water deficit sensitivity.

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“…Mathematic models of crop yields can evaluate the regional impact of climate variability risks associated with agricultural production (TSUKAHARA et al, 2008;ANGULO et al, 2013;MONTEIRO;VIANNA;VIVAN et al, 2015). Although increasingly more precise, the application of these models has been criticized due to the scarcity of climate information with high spatial resolution enabling high accuracy and coverage (ANGULO et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

Ferreira

Sibaldelli

Morais

et al. 2017

SCA

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Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data. Ferreira, R. C. et al escassos estudos que avaliem a eficácia e precisão da utilização de dados climáticos estimados ou interpolados a partir de estações distantes da área agrícola de interesse. Assim, este estudo teve como objetivo demonstrar os impactos da variabilidade espacial dos principais elementos meteorológicos sobre a estimativa regional da produtividade de grãos de soja. Utilizaram-se dados observados em três estações meteorológicas em diferentes locais de Londrina, comparados por meio de análise de regressão. Calculou-se o balanço hídrico decendial para soja e estimaram-se as perdas de produtividade de grãos pelo método Zona Agroecológica. As temperaturas nos diferentes locais apresentaram semelhanças, enquanto a umidade relativa do ar e, principalmente, precipitação pluvial foram mais discrepantes. Recomenda-se muita cautela no uso e na escolha de uma única estação meteorológica para representar um município ou região, como acontece em várias regiões de importância agrícola e ambiental no Brasil. Modelos e resultados de estimativas de safras que não consideram tal...

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Simulação do risco de deficit hídrico em regiões de expansão do cultivo de cana-de-açúcar no Brasil

Cited by 25 publications

References 19 publications

Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Drought tolerance of the sugar cane varieties during the initial development

Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

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