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Crop protection information, such as how to control emergent and outbreak crop diseases and pests, as well as the latest research, regulations, and quality control measures for pesticides and fertilizers, is important to farmers. Rural smallholder farmers in Tanzania have traditionally relied on government agricultural officers who visit them in their villages to provide this crop protection information. However, these officers are few and cannot reach all the farmers on time. This means that farmers fail to make critical farming decisions on time, which can lead to low crop productivity. In this study, we aim to provide farmers with reliable and instant crop protection information by developing a system based on the Short Message Service (SMS) and the Web. This system automatically replies to farmers’ requests for the latest crop protection information in the Swahili language through SMS on a mobile phone or a Web system. The findings reveal that our proposed system can provide farmers with crop protection information at lower cost (500 times cheaper) than the existing Tigo Kilimo system. Furthermore, our proposed system’s deep learning model is effective in understanding and processing Swahili natural language SMS queries for crop protection information with an accuracy of 96.43%. This crop protection information will help farmers make better critical farming decisions on time and improve crop productivity.

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers’ appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker–trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers’ traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

The impacts of changing climate on agriculture have consequences on livelihoods and food security. Smallholder farmers, who have heterogeneous farming systems and limited resources, compounded with multiple risks, are greatly affected. There has been limited research showing how vulnerability assessments have evolved in the smallholder agricultural sector of Africa overtime. This study systematically reviewed recent publications on vulnerability studies, especially among smallholder agricultural systems, to provide an overview of current developments in theory and practice of vulnerability in Africa over the last decade. The findings indicate an increase in vulnerability assessments undertaken across Sub Saharan Africa. Despite progress made in the application of enhanced conceptual frameworks and methods, at least four important gaps exist in the assessment process namely, inadequate engagement of local perspectives and knowledge, lack of clarity in the operationalisation of vulnerability, lack of comprehensiveness of measurement criteria employed and relevance of assessment in decision support. Notwithstanding these challenges, there exist opportunities to geographically improve assessments across Africa. In order to produce knowledge to traverse projected changes in climate systems for agricultural economies and to ensure sustainable smallholder livelihoods, we suggest that future research efforts should be oriented towards providing more information to enlighten science, policy and practice for informed decision-making and evidenced based policies. This requires evaluation of adaptation capacity as a critical aspect of vulnerability assessment to provide guidance and inform effective decision-making on allocation of scarce resources (prioritization); understand trade-offs management and implementation to build understanding among stakeholders that guide possible pathways to reduce vulnerability.

In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making.

The biodiverse, predominantly crop-livestock mixed-farming in India is key to ensuring resilience to climate change and sustainability of smallholder farming agroecologies. Farmers traditionally grow diverse crops as polyculture, and agriculture is mainly organic/biodynamic with spirituality in food systems deeply ingrained. Job-driven out-migration of rural youths, the family labor force, and globalization of contemporary food choices under corporate industrial agriculture both adversely affect sustainability of traditional farming landscapes and compromise the nutrition and health of rural farming communities. Besides documenting information on general agri-food system policy inputs, our paper presents the results of an exploratory study of four crucial community-level initiatives conducted in four distinct agroecological landscapes of India, aimed at bringing sustainability to traditional farming and food systems. The driving force for fundamental change in agri-food system, and in society, is the question of sustainability. The organic and local food movements are but specific phases of the larger, more fundamental sustainable agri-food movement. While it is very critical to increase farmer livelihood, it is even more important to increase overall rural economy. It was found that four important interventions viz. linking organic agriculture to community-supported agriculture (CSA) initiatives; linking small-holder farming to school meal (MDM) programmes; enhanced market access and value chain development for local agricultural produce; and creation of employment opportunities at community level for rural youths and reducing over-dependence of rural population on agriculture as source of income can make traditional farming more profitable and sustainable. The transition to more sustainable methods of farming by selling the farm produce “locally” helps both consumers and farmers alike and is considered a future strength of smallholder Indian agriculture.

ContextThe global trend of landscape simplification for industrial agriculture is known to cause losses in biodiversity and ecosystem service diversity. Despite these problems being widely known, status quo trajectories driven by global economic growth and changing diets continue to lead to further landscape simplification.ObjectivesIn this perspective article, we argue that landscape simplification has negative consequences for a range of relational values, affecting the social-ecological relationships between people and nature, as well as the social relationships among people. A focus on relational values has been proposed to overcome the divide between intrinsic and instrumental values that people gain from nature.ResultsWe use a landscape sustainability science framing to examine the interconnections between ecological and social changes taking place in rural landscapes. We propose that increasingly rapid and extreme landscape simplification erodes human-nature connectedness, social relations, and the sense of agency of inhabitants—potentially to the point of severe erosion of relational values in extreme cases. We illustrate these hypothesized changes through four case studies from across the globe. Leaving the links between ecological, social-ecological and social dimensions of landscape change unattended could exacerbate disconnection from nature.ConclusionA relational values perspective can shed new light on managing and restoring landscapes. Landscape sustainability science is ideally placed as an integrative space that can connect relevant insights from landscape ecology and work on relational values. We see local agency as a likely key ingredient to landscape sustainability that should be actively fostered in conservation and restoration projects.

Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Sustainable Agricultural Practices (SAPs) are the most promising pathways to enhance the productivity and resilience of agricultural production of smallholder farming systems while conserving the natural resources. This study was undertaken to identify the barriers affecting sustainable agricultural productivity of smallholder farmers in the eastern Free State, South Africa. Data were collected from 359 smallholder farmers using questionnaires and the validity of the collected data was confirmed through focus group discussions with key informants. Descriptive statistics and a binary logistic regression model were used to analyze data. Results indicated that traditional SAPs such as intercropping, mulching and crop rotation were more likely to be adopted by farmers with access to land yet without access to credit (and had low levels of education, although this finding was not significant). In contrast, new SAPs such as cover cropping, minimum-tillage, tied ridging and planting pits were more knowledge (education), capital and labor intensive. Therefore, extension strategies should take these differences into consideration when promoting both the adoption of traditional SAPs and new SAPs. Targeting resource-constrained farmers (in terms of access to credit and education) through raising awareness and building capacity is essential to ensure the adoption of traditional SAPs. In turn, promoting the adoption of new SAPs not only needs awareness raising and capacity building but also must fundamentally address resource constraints of South African smallholder farmers such as knowledge, capital and labor. It is recommended that government should provide resources and infrastructure to improve the quality and outreach of extension services through field demonstration trials and training.

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.