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ARS Home » Southeast Area » Fayetteville, Arkansas » Poultry Production and Product Safety Research » Research » Publications at this Location » Publication #409467
Research Project: Developing Best Management Practices for Poultry Litter to Improve Agronomic Value and Reduce Air, Soil and Water Pollution

Location: Poultry Production and Product Safety Research

Title: Boundary line and machine learning models to improve nutrient management in Guatemalan maize, bean, and coffee systems

Author
item SMITH, HARRISON - University Of Arkansas
item Ashworth, Amanda
item NALLEY, LANIER - University Of Arkansas
item SCHMIDT, AXEL - Catholic Relief Services
item TURMEL, MARIE - Catholic Relief Services
item Owens, Phillip

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/15/2023
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Accurate and economically sound soil fertility recommendations are critical for ensuring profitable food production for smallholder farmers. However, such recommendations are lacking in many areas due to insufficient soil and crop response data. This study was conducted between 2016 and 2022 using boundary line analysis to evaluate crop response to soil fertility on 644 farms in Guatemala. We identify optimal soil property conditions for maize (Zea mays L.), common bean (Phaseolus vulgaris L.), and coffee (Coffea arabica L.) production in Guatemala and use crop price information to develop economically sound fertilization recommendations. We also analyzed the drivers of yield outcomes and assessed their relative importance with machine learning (ML) models. Results demonstrate that a majority of countrylevel data currently have sub-optimal soil nutrient levels and by optimizing nutrients, yields and profits could be improved in 64%, 51%, and 69% of maize, common bean, and coffee crops, respectively. In addition, ML underscored the central role of climate in shaping yield outcomes. Variable importance rankings from random forest indicated climate an d water balance variables increased predictive accuracymore than soil parameters, highlighting the critical need for climate-smart adaptations in the region. The pairing of boundary line with ML analysis provided unique and complementary information about factors that drive yield of major crops in Guatemala. This is an effective approach for developing fertility recommendations in Guatemala and could be replicated in other countries where critical nutrient recommendations are currently lacking for closing yield gaps and identifying yield response to climatic stochasticity.