Operationalizing crop model data assimilation for improved on-farm situational awareness

Knowling, Matthew J.; White, Jeremy T.; Grigg, Dylan; Collins, Cassandra; Westra, Seth; Walker, Rob R.; Pellegrino, Anne; Ostendorf, Bertram; Bennett, Bree; Alzraiee, Ayman

doi:10.1016/j.agrformet.2023.109502

Cited by 5 publications

(1 citation statement)

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“…Due to this, these models need a large amount of input data (usually in a daily frequency) that can be difficult to parameterize for large scales and where there is broad agricultural variability, e.g., types of soil, crop practices, or varieties [7,8]. However, because mechanistic models allow different processes that influence crop development to be simulated, it is possible to assess the crop in a variety of ways, e.g., in relation to soil moisture, leaf area index, biomass, and yield [9].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%