Africa has been hesitant to adopt agricultural biotechnology, lagging behind global trends over the past decade. One exception is Burkina Faso, a West African country that commercially released 125,000 ha of Bt cotton in 2009. Bt cotton may serve as a working example of how African countries can enhance sustainability using modern, science-driven technology to increase production levels while reducing input use and energy consumption. This paper reports the potential impact that Bt cotton can have on sustainability in Burkina Faso’s cotton sector based by summarizing empirical evidence from previously published studies. Based on the summary of published data collected from six years of field trials and producer surveys, Bt cotton increased cotton yields by an average of 21.3% and raised income by $106.14 per ha. Using an energy balance model, the introduction of Bt cotton would also result in a 6.6% saving in energy use. The significant increase in productivity and economic returns could be the catalyst for Burkina Faso, and other African countries, to emerge from the decade or so of stagnation and regain their competitive stance in world cotton markets while providing environmental and social benefits.

Wheat breeding continues to be an important component of agricultural innovations in the Great Plains region of the US. This paper reviews Oklahoma’s wheat breeding program over the past several decades with an emphasis on the economic aspects and planning of future breeding strategies. Over the past century, Oklahoma’s wheat yields have increased but the productivity gains have been offset in part due to increased yield variability. The shift to improved wheat varieties has resulted in increased susceptibility to plant disease and pest pressure. While a few varieties introduced over the past few decades have dominated the wheat seed market, recent trends indicate producers are adopting a more diverse range of wheat varieties. Producers’ concerns have expanded beyond demands for high yielding varieties to include more market oriented needs to increase protein content and test weight. This paper identifies several key policy instruments that Oklahoma stakeholders and policy makers should consider when planning future breeding strategies. A critical need is to assist Oklahoma producers in adapting to climate change by developing drought and heat resistant wheat varieties. Marketing new varieties would be improved and revenue increased if more innovative pricing structures were adopted to better align producers’ willingness to pay with seed development costs, which could then be re-invested in breeding. Research budgets, which have remained stagnant in real terms, will need to be increased to adequately address producers’ needs into the 21st century.

This study explores the efficacy of advanced machine learning models, including various Long Short-Term Memory (LSTM) architectures and traditional time series approaches, for forecasting cotton futures prices. This analysis is motivated by the importance of accurate price forecasting to aid U.S. cotton producers in hedging and marketing decisions, particularly in the Texas Gulf region. The models evaluated included ARIMA, basic feedforward neural networks, basic LSTM, bidirectional LSTM, stacked LSTM, CNN LSTM, and 2D convolutional LSTM models. The forecasts were generated for five-, ten-, and fifteen-day periods using historical data spanning 2009 to 2023. The results demonstrated that advanced LSTM architectures outperformed other models across all forecast horizons, particularly during periods of significant price volatility, due to their enhanced ability to capture complex temporal and spatial dependencies. The findings suggest that incorporating advanced LSTM architectures can significantly improve forecasting accuracy, providing a robust tool for producers and market analysts to better navigate price risks. Future research could explore integrating additional contextual variables to enhance model performance further.