Farmers' perceptions on mechanical weeders for rice production in sub-Saharan Africa

Competition from weeds is one of the major biophysical constraints to rice (Oryza spp.) production in sub-Saharan Africa. Smallholder rice farmers require efficient, affordable and labour-saving weed management technologies. Mechanical weeders have shown to fit this profile. Several mechanical weeder types exist but little is known about locally specific differences in performance and farmer preference between these types. Three to six different weeder types were evaluated at 10 different sites across seven countries – i.e., Benin, Burkina Faso, Côte d'Ivoire, Ghana, Nigeria, Rwanda and Togo. A total of 310 farmers (173 male, 137 female) tested the weeders, scored them for their preference, and compared them with their own weed management practices. In a follow-up study, 186 farmers from Benin and Nigeria received the ring hoe, which was the most preferred in these two countries, to use it during the entire crop growing season. Farmers were surveyed on their experiences. The probability of the ring hoe having the highest score among the tested weeders was 71%. The probability of farmers’ preference of the ring hoe over their usual practices – i.e., herbicide, traditional hoe and hand weeding – was 52, 95 and 91%, respectively. The preference of this weeder was not related to gender, years of experience with rice cultivation, rice field size, weed infestation level, water status or soil texture. In the follow-up study, 80% of farmers who used the ring hoe indicated that weeding time was reduced by at least 31%. Of the farmers testing the ring hoe in the follow-up study, 35% used it also for other crops such as vegetables, maize, sorghum, cassava and millet. These results suggest that the ring hoe offers a gender-neutral solution for reducing labour for weeding in rice as well as other crops and that it is compatible with a wide range of environments. The implications of our findings and challenges for out-scaling of mechanical weeders are discussed

Instant estimation of rice yield using ground-based RGB images and its potential applicability to UAV

Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep learning-based approach for instantaneously estimating rice yield using RGB images. During ripening stage and at harvest, over 22,000 digital images were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9m at 4,820 harvesting plots having the yield of 0.1 to 16.1 t ha-1 across six countries in Africa and Japan. A convolutional neural network (CNN) applied to these data at harvest predicted 68% variation in yield with a relative root mean square error (rRMSE) of 0.22. Even when the resolution of images was reduced (from 0.2 to 3.2cm pixel-1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high throughput phenotyping, and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production

Deep learning enables instant and versatile estimation of rice yield using ground-based RGB images

"AIの目"によるイネ収穫量の簡単・迅速推定. 京都大学プレスリリース. 2023-07-21.Rice (Oryza sativa L.) is one of the most important cereals, which provides 20% of the world’s food energy. However, its productivity is poorly assessed especially in the global South. Here, we provide a first study to perform a deep-learning-based approach for instantaneously estimating rice yield using red-green-blue images. During ripening stage and at harvest, over 22, 000 digital images were captured vertically downward over the rice canopy from a distance of 0.8 to 0.9 m at 4, 820 harvesting plots having the yield of 0.1 to 16.1 t·ha⁻¹ across 6 countries in Africa and Japan. A convolutional neural network applied to these data at harvest predicted 68% variation in yield with a relative root mean square error of 0.22. The developed model successfully detected genotypic difference and impact of agronomic interventions on yield in the independent dataset. The model also demonstrated robustness against the images acquired at different shooting angles up to 30° from right angle, diverse light environments, and shooting date during late ripening stage. Even when the resolution of images was reduced (from 0.2 to 3.2 cm·pixel−1 of ground sampling distance), the model could predict 57% variation in yield, implying that this approach can be scaled by the use of unmanned aerial vehicles. Our work offers low-cost, hands-on, and rapid approach for high-throughput phenotyping and can lead to impact assessment of productivity-enhancing interventions, detection of fields where these are needed to sustainably increase crop production, and yield forecast at several weeks before harvesting

Status quo of chemical weed control in rice in sub-Saharan Africa

If future rice production is to contribute to food security for the increasing population of sub-Saharan Africa (SSA), effective strategies are needed to control weeds, the crop’s fiercest competitors for resources. To gain better insights into farmers’ access to, and use of, herbicides as part of weed control strategies, surveys were conducted in key rice production locations across SSA. Farm surveys were held among 1965 farmers across 20 countries to collect data on rice yields, farmer’s weed management practices, herbicide use, frequencies of interventions and information sources regarding herbicides. Markets were surveyed across 17 countries to collect data on herbicide availability, brand names and local prices (converted to US$ha−1). Herbicides are used by 34$17 ha−1). They are also the most popular herbicides among farmers. For advice on herbicide application methods, farmers primarily rely on their peers, and only a few receive advice from extension services (<23%) or inform themselves by reading the product label (<16%). Herbicide application timings are therefore often (38%) sub-optimal. Herbicide technologies can contribute to reduced production losses in rice in SSA. However, through negative effects on crop, environment and human health, incorrect herbicide use may unintentionally counteract efforts to increase food security. Moving away from this status quo will require strict implementation and monitoring of national pesticide regulations and investment in research and development to innovate and diversify the currently followed weed management strategies, agricultural service provision and communications with farmers

Training of young service providers and extension agents in RiceAdvice

As part of the AICCRA project activities, the Syngenta Foundation and AfricaRice organized a training session for young service providers and extension agents on the use of RiceAdvice and KoboCollect. The training took place on July 21 and 22, 2022, in Ségou, in the conference room of the Gabriel Cisse Center, and was attended by 43 participants from the Center for Mechanized Agriculture of Socouma, Koumase, Gneleni, Senekasabati, Dunkafa, Wassa, and Togou. The workshop provided young service providers and extension agents with enhanced knowledge and skills in advisory services, data collection, and processing for climate-informed and locally relevant decision-making

Strategic phosphorus (P) application to the nursery bed increases seedling growth and yield of transplanted rice at low P supply

Sustainable phosphorus (P) management in rice production systems requires efficient P application strategies to improve P fertilizer use efficiency combined with the use of P-efficient genotypes that should ideally have reduced grain P concentrations to lower P removal from the system. In this study, the strategic application of a micro-dose of P to the nursery bed was evaluated as a way to improve seedling vigor and P fertilizer use efficiency and overcome potentially reduced seedling vigor associated with low seed P concentration in transplanted rice. In a first experiment established in two fields differing in soil P supply, two rice genotypes (Mudgo and Santhi Sufaid) were grown with and without P application to a P-deficient nursery bed. In a second experiment established in the same fields with the same genotypes, P application to the nursery bed was evaluated in combination with two seed sources differing in seed P concentration. In both experiments, P application to the nursery bed doubled seedling shoot biomass of both genotypes at transplanting time with a 5-fold increase in seedling shoot P content in the first experiment. In the first experiment, P application to the nursery bed increased grain yield by 10–14% in the field with higher soil P supply and 30–40% in the field with lower soil P supply. In the second experiment, P application to the nursery bed increased grain yield by 19–20% in the field with lower P supply only, whereas no effect of P application to the nursery bed on yield was observed at higher soil P supply. We conclude that strategic application of micro-doses of P to the nursery bed can substantially enhance seedling vigor and grain yield of transplanted rice at low P supply