Modeling Herbaceous Biomass and Assessing Degradation Risk in the Caatinga Biome Using Monte Carlo Simulation

Jefta Ruama de Oliveira Figueiredo, José Morais Pereira Filho (), Jefferson Ferreira de Freitas Feitosa, Magno José Duarte Cândido, Sonel Gilles, Olaf Andreas Bakke, Samuel Rocha Maranhão, Ana Clara Rodrigues Cavalcante, Ricardo Loiola Edvan and Leilson Rocha Bezerra ()
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Jefta Ruama de Oliveira Figueiredo: Department of Animal Science, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil
José Morais Pereira Filho: Department of Animal Science, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil
Jefferson Ferreira de Freitas Feitosa: Soil Science Department, Federal University of Ceará, Fortaleza 60020181, Ceará, Brazil
Magno José Duarte Cândido: Department of Animal Science, Federal University of Ceará, Fortaleza 60020181, Ceará, Brazil
Sonel Gilles: Department of Animal Science, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil
Olaf Andreas Bakke: Department of Forestry Engineering, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil
Samuel Rocha Maranhão: Department of Animal Science, Federal University of Ceará, Fortaleza 60020181, Ceará, Brazil
Ana Clara Rodrigues Cavalcante: Brazilian Agricultural Research Corporation, Sheep and Goats, Sobral 70770901, Ceará, Brazil
Ricardo Loiola Edvan: Department of Animal Science, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil
Leilson Rocha Bezerra: Department of Animal Science, Federal University of Campina Grande, Patos 58708110, Paraíba, Brazil

Sustainability, 2025, vol. 17, issue 16, 1-25

Abstract: Simulating scenarios under climate change is essential to understanding vegetation dynamics, ensuring the survival of forage species, and minimizing uncertainties in project costs and timelines. This study aimed to simulate historical probabilities and develop a biomass production model using PHYGROW software (Texas A&M University, College Station, TX, USA), combined with Monte Carlo Simulation (MCS) in the @RISK program (Ithaca, NY, USA), to evaluate long-term biomass production in a native pasture area of the Caatinga biome. The results show strong agreement between software estimates and field data. For 2016, PHYGROW estimated 883 kg/ha, while field measurements reached 836.8 kg/ha; for 2017, 1117 kg/ha was estimated, while 992.15 kg/ha was observed. For 2018, the model estimated 1200 kg/ha compared with 1763.5 kg/ha in the field, and for 2019, 1230 kg/ha was estimated versus the 1294.3 kg/ha observed. The Monte Carlo simulations indicated that the Weibull distribution best fitted the synthetic series, with 90% adherence. Biomass production values ranged from 618 to 1427 kg/ha with a 90% probability. Only 5% of the simulations projected values below 600 kg/ha or above 1400 kg/ha. Moreover, there was a 95% risk of production issues if planning was based on biomass values above 1000 kg/ha. These findings highlight PHYGROW’s potential for pasture management under semi-arid conditions for predicting and avoiding degradation scenarios that could even lead to areas of desertification.

Keywords: environmental; sustainability; prediction; livestock; dynamic systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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