An Outlook on Harnessing Technological Innovative Competence in Sustainably Transforming African Agriculture.

Agricultural value chains worldwide provide essential support to livelihoods, ecosystem services, and the growing bioeconomy. The coronavirus disease 2019 (COVID-19) pandemic slowed down or reversed decades of agricultural growth and exposed the vulnerabilities of farmers and the food insecure in Africa, thus reiterating the need to build resilience, agility, and adaptability for a sustainable agriculture. Existing social, political, environmental, and economic challenges demonstrate that a path to faster sustainable growth is increased productivity through improved input quality, of which technical inputs are a part. This work presents a perspective calling for African innovative competence in technological and methodological applications and solutions as part of the most critical area of a holistic organization for social progress. It finds that while performances of previous agricultural transformation efforts offer insights for future directions, novel pathways fitting to the diversity of situations and contexts on the continent are needed. These may include vertical agriculture in land-constrained regions to grow high-value products, ocean or sea farming in coastal regions, development of multiple-harvesting crops, and self-replicating plants. Developing standards that integrate current scientific methodologies and technologies with indigenous knowledge for agricultural growth and disaster management will bring the complementary benefits of both worlds into optimal development.

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems.

Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system's internal environment with the highest occurring in May. In May, an average change of -0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

Could Japonica Rice Be an Alternative Variety for Increased Global Food Security and Climate Change Mitigation?

The growing importance of rice globally over the past three decades is evident in its strategic place in many countries' food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice's physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.