GRASSLAND BIOPHYSICAL PARAMETERS ESTIMATION USING REMOTE SENSING PRODUCTS TO SUPPORT PASTURE INSURANCE
Bibliographic record
Abstract
As a source of food and employment, livestock farming is an important activity for many societies around the world. In a global context, livestock farming has significant differences due to cultural and environmental aspects. A common goal of livestock producers is for the herd to gain weight and to do so it is necessary to have food available for the animals throughout the year. Unfortunately, pasture growth is susceptible to risks such as overgrazing and climate variation, which makes productivity highly variable over time, affecting ecosystem sustainability and bringing economic losses to producers. To mitigate economic losses, one of the alternatives for producers is to resort to agricultural insurance programs. Within the agricultural insurance market, the index-based approach, which considers remotely obtained variables such as precipitation data and vegetation indices, has gained notoriety for having greater geographical coverage, lower operating costs, and faster premium payments. In this context, the main objective of this research is to investigate and identify inputs that can increase the accuracy of remote monitoring of pastures. The inputs/products derived from remote sensing addressed in this research can contribute to characterizing the canopy of a given area of interest, validate gridded precipitation data that can replace weather stations, and estimate biomass production. The results of this research show that remote sensing is effective for estimating biophysical parameters of native grasslands and identifying differences between vegetation conditions in different ecoregions of the Canadian Prairies. Despite having identified indices such as the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) as indicators of vegetative growth, and the plant senescence reflectance index (PSRI) as an indicator of senescence, the leaf area index (LAI) proved to be an interesting parameter to be used for monitoring native grasslands because it presented significant correlation with other biophysical parameters. Good results were obtained for differentiating grassland/forage types at a more detailed level, especially in the Moist-Mixed and in the Mixed Ecoregions. Evidence was also gathered that gridded data can be important to estimate precipitation and identify atmospheric events that may affect plant development, especially in regions with few meteorological stations or with gaps in the time series. It was concluded that the dry matter productivity model (DMP), despite the short data history, is a better biomass production estimator than the NDVI and the enhanced vegetation index (EVI2), especially when combined with other parameters such as annual average NDVI and the annual average temperature. The results obtained in this research showed solid evidence that remote sensing can improve the accuracy of pasture monitoring and be a valuable support tool for the agricultural insurance market, especially for the index-based approach, making the relationship between insurance companies and rural producers clearer and fairer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".