Predicting aflatoxin content in peanuts using ambient temperature, soil temperature and soil moisture content during pod development

Chalwe, Hendrix; Lungu, Obed I.; Mweetwa, Alice M.; Phiri, Elijah; Yengwe, Jones; Njoroge, Samuel; Brandenburg, Rick; Jordan, David L.

doi:10.5897/ajps2018.1742

Cited by 3 publications

References 16 publications

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Developing statistical models of aflatoxin risk in peanuts using historical weather data

Kim,

Getachew,

Tillman

et al. 2024

Agronomy Journal

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Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a significant public health risk. Aflatoxin is detected postharvest after inspection of loads associated with grading at peanut buying points, leaving growers and shellers in a precarious position. Stricter limits on aflatoxin contamination could restrict the United States access to international markets. Predicting aflatoxin risk remains challenging, but improved tools could help inform postharvest storage segregation decisions and alert industry stakeholders to seasonal threats. This study aimed to develop and evaluate multiple statistical models that estimate the regional status of peanut aflatoxin contamination based on preharvest weather conditions. Our approach expanded on an existing peanut aflatoxin model for which a new geographic area and time period were tested. Weather variables served as independent variables to predict the risk of aflatoxin as the proportion of samples with greater than 20 ppb and 4 ppb aflatoxin (PGT20 [the proportion of samples with greater than 20 ppb aflatoxin] and PGT4 [the proportion of samples with greater than 4 ppb aflatoxin], respectively) across 10 counties in Georgia for 2018–2022. Best‐performing models were developed through multiple linear stepwise regression explaining more than 72% and 41% of the variability in PGT20 and PGT4, respectively. Model performance further varied whether it was a year of low or high aflatoxin incidence, with temperature observed as a key influencing factor across best‐performing models. This study established an adaptive approach to monitoring and managing aflatoxin risk through statistical predictive modeling, with output targeting farmers, industry, regulators, and public health officials. Future model development will aim to improve interpretation and confidence with in‐season aflatoxin prediction and efficacy testing of this approach across space and time.

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Developing statistical models of aflatoxin risk in peanuts using historical weather data

Kim,

Getachew,

Tillman

et al. 2024

Agronomy Journal

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Study on Spatial Auto-Regression Within Soil Physical-Chemical Indicators in Typical Karst Demonstration Zone

Yin

Chen

Jiang³

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

Keller

Russo

Rembold

et al. 2022

WMJ

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This review presents the current state of aflatoxin risk prediction models and their potential for value actors throughout the food chain in sub-Saharan Africa, with a specific focus on improving smallholder farmer management practices. Several empirical and mechanistic models have been developed either in academic research or by private sector aggregators and processors in high-income countries including Australia, the USA, and Southern Europe, but these models have been only minimally applied in sub-Saharan Africa, where there is significant potential and increasing need due to climate variability. Predictions can be made based on historic occurrence data using either a mechanistic microbiological framework for aflatoxin accumulation or an empirical model based on statistical correlations with climate conditions and local agronomic factors. Model results can then be distributed to smallholders through private, public, or mobile extension services, used by policymakers for strategy or policy, or utilised by private sector institutions for management decisions. Specific agricultural advice can be given during the three most critical points in the phenological cycle: preseason insight including sowing timing and crop varieties, preharvest advice about management and harvest timing, and postharvest optimal practices including storage, drying, and market information. Model development for sub-Saharan Africa is limited by a dearth of georeferenced aflatoxin occurrence data and real-time high resolution climate data; the wide diversity of farm typologies each with significant information and technology gaps; a prevalence of informal market structures and lack of economic incentives systems; and general lack of awareness around aflatoxins and best management practices to mitigate risk. Given advancements towards solving these challenges, predictive aflatoxin models can be integrated into decision support platforms to focus on optimisation of value for smallholders by minimising yield and nutritional losses, which can propagate value throughout the production and postharvest phases.

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Predicting aflatoxin content in peanuts using ambient temperature, soil temperature and soil moisture content during pod development

Cited by 3 publications

References 16 publications

Developing statistical models of aflatoxin risk in peanuts using historical weather data

Developing statistical models of aflatoxin risk in peanuts using historical weather data

Study on Spatial Auto-Regression Within Soil Physical-Chemical Indicators in Typical Karst Demonstration Zone

The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

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