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Abstract The sub-Saharan Africa region continues to experience food insecurity, a consequence of the less productive agricultural sector that has dragged to adapt to the effects of climate change. As the region’s population continues to grow, there is a need to modernize the region’s agricultural sector to meet the increasing food demand. Although extreme atmospheric conditions cannot be entirely mitigated, however, the integration of technologies such as the Internet of things (IoT) and machine learning (ML) can increase the quantity and quality of production from the crop fields. These technologies have the potential to empower agricultural management systems to handle both climatic and farm data in an orchestrated manner, and inform the formulation of effective strategies. This study presents the design and development of a system for predicting crop yields that integrates IoT and ML. The system combines historic and current weather and crop yield data to predict seasonal crop yields. The weather parameters including, rainfall, temperature, humidity and soil moisture are collected by IoT sensors and transmitted to the cloud for crop yield forecasting. The system is used to analyze seasonal yields of Irish-Potato and Maize in Musanze District of Rwanda. Using data over different agricultural seasons, the system achieved favorable predictive accuracy with mean absolute percentage error (MAPE) values of 0.339, 0.309, and 0.177 for two seasons of Irish potatoes and one season of maize, respectively. Such predictive yield systems can reduce food insecurity risks and enhance harvest efficiency by enabling early awareness of crop production, fostering effective strategies shared among decision-makers and stakeholders. While maize and Irish potatoes were the initial case studies, expansion to include other crops and more variables is envisioned.

Climate change is causing significant damage to crop production in the central plateau zone of Rwanda, particularly affecting sorghum, food, and the incomes of smallholder farmers. Understanding farmers’ perceptions and the factors impacting their responses is crucial for improving sorghum production policies and programs. Therefore, a study was conducted to assess sorghum farmers’ perceptions of climate change and the factors determining their adaptation strategies. A multistage sampling method and a cluster random selection were utilized to select 345 respondents from five districts of the study area. The data were analyzed using descriptive statistics and a multivariate probit model. The results showed that 98.8% of farmers were aware of climate change, with deforestation being the main anthropogenic activity causing it. Consequently, 95.7% and 84.3% of farmers experienced grain yield reductions, and over 20 sorghum varieties disappeared. To address these impacts, farmers adopted five adaptation strategies: early maturing sorghum varieties (67%), adjusting planting dates (50.1%), drought-tolerant varieties (46.7%), soil conservation practices (38.3%), and crop diversification (32.8%). The multivariate probit model results showed the age and literacy level of the household head, access to extension services, access to information, access to credit, farming experience, and land size as the important factors influencing at least one of the climate change adaptation strategies. The study concluded that sorghum farmers are aware of the impacts of climate change and are acting to address its negative effects. The results suggest that the government and stakeholders should support farmers in strengthening their adaptation strategies for sustainable sorghum production.

Hot pepper (Capsicum spp.) is an important cash crop in Rwanda however, its productivity can only increase after addressing factors that limit its production. This study aimed at revealing the farmers knowledge and perceptions of virus diseases and their management in Rwanda. A survey was conducted between February and March 2018 in the main hot pepper growing areas covering low, mid and high-altitude agro-ecological zones (AEZs). Household data were collected using a structured questionnaire from 101 respondents and analysed using descriptive statistics. Majority of farmers (86.1%) indicated that pests and diseases were the main constraints to hot pepper production. Viral diseases were perceived by 71.9% of the farmers as the most serious diseases while 51.4% and 12.9% of them reported that aphids and whiteflies were the major insect pests of hot pepper, respectively. Only 17.8% and 25.7% of the farmers attributed the cause of the viral diseases to insect vectors and the use of infected seeds, respectively. The main method used to control viral diseases was application of synthetic pesticides. About two-thirds of the farmers lacked in knowledge of viral disease symptoms, spread and management across all AEZs. Majority of the farmers (80.2%) did not have access to extension service or training but relied mainly on farmer-interactions for information. Therefore, interventions such as farm-level training to raise the farmers awareness of diseases, especially viral diseases and integrated disease management are needed. This study provides baseline information for the development of sustainable integrated pest management (IPM) strategy for hot pepper viral diseases in Rwanda. [Fundam Appl Agric 2020; 5(3.000): 319-329]

Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.

This study was carried out to determine the effect of vine and fruit pruning on watermelon (Citrullus lanatus) yield. Five pruning methods: P1=no pruning at all, P2=pruning to four vines with two fruits per vine, P3=pruning to four vines with one fruit per vine, P4=pruning to three vines with two fruits per vine and P5=pruning to three vines with one fruit per vine were evaluated on two watermelon cultivars: ‘Sugar baby’ and ‘Julie F1’ under a factorial randomized complete block design with three replications. Investigations were carried out in the seasons 2017A (short rains) and 2017B (long rains) at Karama and Rubona experimental sites belonging to Rwanda Agriculture and Animal Resources Development Board. The obtained results indicated a significant difference among the different cultivars and pruning methods tested during both seasons and at two sites. Generally, all studied parameters recorded higher values during season 2017B than in season 2017A at Rubona site. A similar trend was recorded at Karama site except that the fruit yield per plant and per hectare for plants which were pruned to three vines with one fruit reduced during season 2017B as compared to season 2017A. The highest number of fruits per plant, fruit weight, fruit yield per plant and per hectare was recorded in ‘Julie F1’ compared to ‘Sugar baby’ at both sites and during both seasons. Higher fruit weight was obtained when both cultivars were pruned to three or four vines with one fruit per vine. Higher number of fruits per plant and higher fruit yield per plant was observed under pruning to four vines with two fruits per vine at Rubona site; while at Karama site, higher fruit yield per plant was recorded under pruning to three vines with one fruit or two fruits per vines and pruning to four vines with two fruits per vine. A similar trend was observed in fruit yield per hectare. Based on results of the current study, cultivation of the hybrid ‘Julie F1’ and pruning to three vines with one fruit per vine is recommended for optimum watermelon yield with big-sized fruits.

Hot pepper is an important crop in Rwanda but viral diseases and pests are major constraints to its production. Field experiments were conducted to evaluate the resistance of 18 hot pepper genotypes (4 commercials, 5 introduced and 9 local) to natural infection by viruses and aphid infestation, in two agro­ecological zones of Rwanda. Fourteen genotypes were further evaluated for resistance to Cucumber mosaic virus (CMV) under screenhouse conditions. Disease incidence and severity were recorded in all experiments while population of aphids was assessed in the field. Diseased leaf samples from each genotype in the field were analysed using polymerase chain reaction to detect the presence of viruses, while samples from the screenhouse were analysed using serological assay. Results showed significant (p