Sugarcane yield estimation for climatic conditions in the state of Goiás

Caetano, Jordana Moura; Casaroli, Derblai

doi:10.1590/0034-737x201764030011

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…It allows for estimating the potential crop yield if water and nutritional needs are satisfied, disregarding losses due to pests or diseases. The actual yield is calculated by penalizing the potential yield by water deficit [60]. Over the years, the model has undergone several improvements [59,61,62].…”

Section: Fao Agroecological Zone Model (Fao-azm)mentioning

confidence: 99%

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

de França e Silva,

Chaves,

Luciano

et al. 2024

Remote Sensing

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The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data.

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Section: Fao Agroecological Zone Model (Fao-azm)mentioning

confidence: 99%

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

de França e Silva,

Chaves,

Luciano

et al. 2024

Remote Sensing

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“…Because the main climatic elements that determine the productivity of agricultural crops are air temperature, solar radiation, photoperiod, real insolation, distribution, duration and frequency of precipitations and crop management. Which can predict its effect on plant growth and development, and thus make the most assertive decisions in agricultural planning (Alvares et at., 2013;Cardozo & Sentelhas, 2013;Angels et al, 2017;Caetano & Casaroli 2017;Dias & Sentelhas 2018, Cassaroli et al, 2019, Anjos et al, 2020b.…”

Section: Introductionmentioning

confidence: 99%

Productivity and penalty in sugarcane from three meteorological databases in Jataí-GO

Anjos

Casaroli²,

Júnior³

et al. 2023

Sci. Elec. Arch.

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The agroecological zone model proposed by FAO (ZAE/FAO) is an important tool in agricultural planning and can be used in Jataí, which has been gaining space in the production of sugarcane. The objective of this study was to compare the observed meteorological databases (BDMEP) with the estimated ones (Xavier and NASA POWER), in the estimation of potential productivity (PP) and attainable (PA) of sugarcane and its penalties for management (PMJ) and water deficit (PDF), using the ZAE/FAO model in the municipality of Jataí - GO. To this end, the BDMEP, Xavier and NASA POWER database, 33 years old (1984-2017), were used. Using BDMEP, Xavier and NASA POWER as input in the ZAE model, we observed an average PP of 259±11, 275±10 and 267±11 Mg ha-1 as, respectively. PA reaches 200 Mg ha-1 when using BDMEP in ZAE, with 50% of the crop year with PA above 180 Mg ha-1. There was an average penalty of 114.01±6.9 for management and 85.67±4.08 Mg ha-1 in stem yield. The meteorological variables of Xavier's database can be used in the ZAE model in the prediction of productivity penalty for water deficit and sugarcane management, with 63% and 88% adjustment to BDMEP, respectively. NASA POWER can be used in the simulation of penalty for failures in sugarcane management, with up to 87% approximation of those observed. Both sources when used in the prediction model assist in agricultural planning.

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“…Mathematical models that simulate growth and yield of agricultural crops are important tools for decision making (Caetano and Casaroli 2017;Ferreira et al 2019;Battisti et al 2020;Caetano et al 2021). The use of estimated weather data has been widely used, improving its degree of accuracy more and more (Jha et al 2019; Dubrovsky et al 2020).…”

Section: Introductionmentioning

confidence: 99%

The impact of gridded weather database on soil water availability in rice crop modeling

Menezes

Casaroli

Heinemann³

et al. 2022

Theor Appl Climatol

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In recent years, there has been an increase in studies suggesting that gridded weather database (GWD) is a suitable source for simulating crop yield. Brazil has low geospatial coverage by measured weather database (MWD). Based on that, this study aimed to compare two different GWD sources, Daily Gridded (DG) and NASA/POWER (NP), on the simulated yield of upland rice (UR) against the MWD input. The GWD and MWD were obtained for seven locations across UR Brazilian region, considering a period ranging from 1984 to 2016. GWD and MWD were used to estimate rice potential (Yp) and attainable yield (Ya), in clay soil and sandy soil, using ORYZA (v3) model. DG had the best performance for all variables. GWD-based yields had a reasonable performance. However, DG had a slightly better performance than NP in all conditions, DG-based yields showed RMSE values of 0.57, 0.71 and 0.52 for Yp and Ya in clay and sandy soil, whereas NP showed RMSE values of 0.86, 0.91 and 0.64. DG also showed higher R² and d values for yields assessed. Both GWD overestimated Ya, these overestimations in DG-based yield were 3.54, 9.61, and 21.35% for Yp and Ya in clay and sandy soil respectively, in NP-based yield were 13.67, 18.45, 29.11%, showing that for both GWD-based yield increased as the soil type texture as well as water storage decreased. As a consequence, we do not recommend the use of precipitation data in daily timestep crop modeling.

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Sugarcane yield estimation for climatic conditions in the state of Goiás

Cited by 16 publications

References 19 publications

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

Productivity and penalty in sugarcane from three meteorological databases in Jataí-GO

The impact of gridded weather database on soil water availability in rice crop modeling

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