De Jesús Soto, Alejandra Marie

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    Meta-analysis for crop fertility studies in Puerto Rico using linear mixed models and nonlinear mixed models
    (2024-05-09) De Jesús Soto, Alejandra Marie; Macchiavelli, Raúl E.; College of Arts and Sciences - Sciences; Santana Morant, Dámaris; Lorenzo González, Edgardo; Department of Mathematics; Villanueva Vega, Marién
    A popular technique for increasing crop yields worldwide is nitrogen fertilization. However, excessive nitrogen fertilizer causes enormous emissions of greenhouse gases, which contribute to global warming and climate change. In addition, unused nitrogen contaminate water causing problems to aquatic life. Having nutrient management in terms of fertilizer recommendations is key to having a sustainable high yielding crop, without damaging the plant, environment, and overall soil productivity. This project’s application consists in estimating the total amount of fertilizer, nitrogen, needed by a crop using statistical modeling. The relationship between variables for crop and soil processes are often better captured by nonlinear models because they provide a series of advantages. In this project, the crop nutrient requirement (CNR) is used as a non typical effect size for fertilizer recommendations. This metric depends on the fertilizer rate and the crop’s relative yield. Using these two metrics, crop yield response curves can be obtained from different agricultural studies. In order to get a combined CNR estimate this project will consider using two different meta-analysis methodologies: Linear Mixed Models (LMMs), and Non-Linear Mixed Models (NLMMs). Field fertility research conducted with crops from the Solanaceae family and forage crops documenting yield response to nitrogen fertilizers were used for this project. The LMM methodology consists on a two step approach: (1) the exponential model was used in order to get CNR estimates for all studies, and (2) these results were used as observation to fit an LMM and obtain the general CNR estimate. On the other hand, the second approach consisted in fitting an exponential NLMM to the raw data in order to obtain a different combined CNR estimate. Results show that 95% confidence intervals for the CNR combined estimates after meta-analysis were narrower than the individual confidence intervals from each study.
  • Thumbnail Image
    Publication
    Meta-analysis for crop fertility studies in Puerto Rico using linear mixed models and nonlinear mixed models
    (2024-05-09) De Jesús Soto, Alejandra Marie; Macchiavelli, Raúl E.; College of Arts and Sciences - Sciences; Santana-Morant, Dámaris; Lorenzo-González , Edgardo; Department of Mathematics; Villanueva Vega, Marién
    A popular technique for increasing crop yields worldwide is nitrogen fertilization. However, excessive nitrogen fertilizer causes enormous emissions of greenhouse gases, which contribute to global warming and climate change. In addition, unused nitrogen contaminate water causing problems to aquatic life. Having nutrient management in terms of fertilizer recommendations is key to having a sustainable high yielding crop, without damaging the plant, environment, and overall soil productivity. This project’s application consists in estimating the total amount of fertilizer, nitrogen, needed by a crop using statistical modeling. The relationship between variables for crop and soil processes are often better captured by nonlinear models because they provide a series of advantages. In this project, the crop nutrient requirement (CNR) is used as a non typical effect size for fertilizer recommendations. This metric depends on the fertilizer rate and the crop’s relative yield. Using these two metrics, crop yield response curves can be obtained from different agricultural studies. In order to get a combined CNR estimate this project will consider using two different meta-analysis methodologies: Linear Mixed Models (LMMs), and Non-Linear Mixed Models (NLMMs). Field fertility research conducted with crops from the Solanaceae family and forage crops documenting yield response to nitrogen fertilizers were used for this project. The LMM methodology consists on a two step approach: (1) the exponential model was used in order to get CNR estimates for all studies, and (2) these results were used as observation to fit an LMM and obtain the general CNR estimate. On the other hand, the second approach consisted in fitting an exponential NLMM to the raw data in order to obtain a different combined CNR estimate. Results show that 95% confidence intervals for the CNR combined estimates after meta-analysis were narrower than the individual confidence intervals from each study.