Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R2 = 0.60, Linear with R2 = 0.54, and Extra Trees with R2 = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R2 of 0.75, and Bayesian Ridge with an R2 of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia.
This study combines three rounds of surveys with remote sensing to measure long-term impacts of a randomized irrigation program in the Dominican Republic. Specifically, Landsat 7 and Landsat 8 satellite images are used to measure the causal effects of the program on agricultural productivity, measured through vegetation indices (NDVI and OSAVI). To this end, 377 plots were analyzed (129 treated and 248 controls) for the period from 2011 to 2019. Following a Differencein-Differences (DD) and Event study methodology, the results confirmed that program beneficiaries have higher vegetation indices, and therefore experienced a higher productivity throughout the post-treatment period. Also, there is some evidence of spillover effects to neighboring farmers. Furthermore, the Event Study model shows that productivity impacts are obtained in the third year after the adoption takes place. These findings suggest that adoption of irrigation technologies can be a long and complex process that requires time to generate productivity impacts. In a more general sense, this study reveals the great potential that exists in combining field data with remote sensing information to assess long-term impacts of agricultural programs on agricultural productivity.
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