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This study delves into the relationship between human cultures and plant genetic diversity, centering on the expanded concept of landesque capital. Traditionally, this term has encapsulated human-made environmental modifications, such as anthropogenic soils or terraces, that can yield long-term benefits. We extend this notion to embrace the genetic diversity and dynamics of crops and crop wild relative (CWR) gene pools. The investigation focuses on the Napo Runa of Ecuador and their cultural practices, including selection, seed saving, and transplantation, to highlight potential models of selection pressures affecting crop-CWR complexes and any cultural practices connected to either. In doing so, we aim to underscore the interconnected and multispecies nature of the relationships among genetic diversity and cultural diversity. Changes in the genetic makeup of one plant, induced by human interaction, can affect its cultivated counterparts through hybridization or future breeding programs, thereby influencing agricultural resilience, nutritional value, and the very cultural practices dependent on these crops or any related to them. Consequently, we argue for a focused examination of plant-based forms of landesque capital or any gene pool-based form, including animals or microbial life. This approach highlights the importance of interconnecting biocultural diversity between cultivated plants and their “wild” relatives and recognizing the intricate interdependencies between cultural practices and gene pools in the face of environmental challenges.
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Abstract This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/abd8fc
