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Rwanda has been a notable player in sustainable development in the last decades, particularly in light of its agricultural practices. This paper offers a multifaceted viewpoint on the challenges faced by Rwanda's specialty coffee sector. Our holistic approach to studying the situation and possibilities included fieldwork, site visits, informal interviews with various stakeholders, from farmers to governmental officials, and an extensive literature study to support our findings. We highlight the challenges of optimizing the domestic supply chain and the effects of such developments on rural livelihoods. Poverty and lacking resources require a nuanced view toward sustainability, as ecological and economic sustainability do not align. The paper presents how certification programs can function as indicators of sustainability and stresses the need for policy evaluations. We also discuss the unique challenges faced by smallholder farmers in Rwanda and how corporate interests may interfere with long‐term sustainable development. As Rwanda continues to develop, this paper argues that the resilience and ingenuity of its people are crucial in the ongoing development of sustainable agricultural systems. [ABSTRACT FROM AUTHOR]

Abstract Antimicrobial resistance (AMR) as a result of antimicrobial overuse and misuse in agriculture is a growing concern, especially in the predominant but poorly regulated backyard or smallholder swine farms of the Philippines. A city-scale surveillance of antimicrobial resistance genes (ARGs) was conducted through selected backyard swine farms to obtain a comprehensive understanding of the AMR situation in the backyard sector of Davao City, Philippines. The ARGs encoding resistance to four antimicrobial classes (β-lactams: bla CTX−M, bla OXA, bla SHV, bla TEM, mecA; quinolones: qnrS and qnr-A2; macrolides: ermA and ermB; tetracycline: tetM) were quantified in swine feces via real-time PCR, and point maps were generated to visualize the ARG geographic distribution. The ARGs bla- TEM, ermB, qnrS, and tetM were detected in all farms at relatively intermediate to high levels, consistently representing the four most used antimicrobial classes in Philippine farms. All the investigated ARGs were present in some local barangay units, indicating potential emergence of AMR hotspots. A 0.5 quantile regression analysis identified specific backyard farm attributes associated with ARG occurrence and diversity, which may influence the ecological spread of the ARGs. These findings provide support for evidence-based guidelines to reinforce a One Health framework for the Philippine National Action Plan to Combat AMR.

Antimicrobial resistance (AMR) as a result of antimicrobial overuse and misuse in agriculture is a growing concern, especially in the predominant but poorly regulated backyard or smallholder swine farms of the Philippines. A city-scale surveillance of antimicrobial resistance genes (ARGs) was conducted through selected backyard swine farms to obtain a comprehensive understanding of the AMR situation in the backyard sector of Davao City, Philippines. The ARGs encoding resistance to four antimicrobial classes (β-lactams: blaCTX−M, blaOXA, blaSHV, blaTEM, mecA; quinolones: qnrS and qnr-A2; macrolides: ermA and ermB; tetracycline: tetM) were quantified in swine feces via real-time PCR, and point maps were generated to visualize the ARG geographic distribution. The ARGs bla-TEM, ermB, qnrS, and tetM were detected in all farms at relatively intermediate to high levels, consistently representing the four most used antimicrobial classes in Philippine farms. All the investigated ARGs were present in some local barangay units, indicating potential emergence of AMR hotspots. A 0.5 quantile regression analysis identified specific backyard farm attributes associated with ARG occurrence and diversity, which may influence the ecological spread of the ARGs. These findings provide support for evidence-based guidelines to reinforce a One Health framework for the Philippine National Action Plan to Combat AMR. [ABSTRACT FROM AUTHOR]

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze. [ABSTRACT FROM AUTHOR]

Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

A growing entrepreneurial industry known as "agritourism," or farm tourism, offers rural communities new economic prospects while upholding agricultural customs. This study explores how stakeholder cooperation among smallholder farms in particular Keralan districts is contributing to the growth of farm tourism entrepreneurship. It highlights the crucial elements--financial assistance, governmental regulations, community engagement, and infrastructure development--that affect farm tourism's viability. The study also looks at the difficulties that farmers encounter, such as restricted access to resources, regulatory obstacles, and poor stakeholder communication. Descriptive analysis, correlation, and regression models are used in the study, which shows that government and financial support are essential for increasing farm income and drawing tourists. The results underscore the significance of cultivating robust collaborations among farmers, and governmental organizations. [ABSTRACT FROM AUTHOR]

The livestock-dairy sector in Sub-Saharan Africa, particularly in Rwanda, is experiencing rapid growth due to population expansion, urbanisation, and changing food preferences. The unmet local production demands are causing soil and water pollution, competition for biomass, land, and water, but also grassland degradation, biodiversity loss, and increased GHGs emissions. Rwanda has the lowest productivity in the region, largely due to inadequate and poor-quality livestock feed resources. To increase animal productivity, promoting forage species with higher nutritional value and better adaptation to drought-prone and poor-fertility soils could be beneficial. Using a mixed-methods approach, the study explores Brachiaria forage adoption and profitability and analyses policy objectives and measures to overcome adoption barriers and promote the transition from subsistence to market-oriented systems. Results show that Brachiaria, although advantageous from an economic point of view, is characterised by very low adoption rates. Furthermore, access to extension programmes is limited and often not supported by adequate incentives. To overcome such barriers, policy interventions should be harmonised and information and knowledge management prioritised, public and private extension and advisory services (EASs) programmes coordinated, agricultural input subsidies increased, and institutional coordination promoted to enhance climate-smart animal feeding. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) are progressively being employed in an array of agricultural activities, as they come equipped with a controllable platform necessary for near real-time data acquisition. Because of these impressive advancements in geospatial technologies, the collection of important data to achieve various agricultural applications, such as crop discrimination, is increasingly being done. While they prove to be cost-effective, autonomous, and flexible in agricultural applications, a key bottleneck in such research is the lack of relevant information relating to UAV types and sensor characteristics, as well as data processing and analytical methods that are applicable for crop discrimination. As such, the study conducted a systematic review of trends, technical challenges, and opportunities. The study used PRISMA Guidelines to select, assess, and systematically review 83 articles from Web of Science, Scopus, Google Scholar, and PubMed. The findings of this review showed that rotary-wing UASs equipped with multispectral and RGB cameras were the most frequently used for crop classification. 90% of the reviewed articles noted that the deployment of these advanced technologies was predominantly in monoculture systems normally found in developed countries. Unfortunately, the potential of UASs for crop mapping and monitoring on heterogeneous smallholder farms normally practice mixed cropping systems is progressing slowly, because of technical challenges, operational costs, differing cropping systems, complex classification algorithms, environmental factors, and restrictive policies and regulations, especially in developing countries. Subsequently, research should prioritise developing simple algorithms that can accurately extract crop statistics from high-spatial resolution RGB imagery collected by UAS platforms. [ABSTRACT FROM AUTHOR]

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Abstract Background Arcobacter species are considered emerging foodborne pathogens that can potentially cause serious infections in animals and humans. This cross-sectional study determined the frequency of potentially pathogenic Arcobacter spp. in both commercial and smallholder farm animals in Ghana and Tanzania. A total of 1585 and 1047 (poultry and livestock) samples were collected in Ghana and Tanzania, respectively. Selective enrichment media, along with oxidase and Gram testing, were employed for isolation of suspected Arcobacter spp. and confirmation was done using MALDI-TOF MS. Antibiotic susceptibility was assessed through disk diffusion method and ECOFFs were generated, for interpretation, based on resulting inhibition zone diameters. Results The overall Arcobacter frequency was higher in Ghana (7.0%, n = 111) than in Tanzania (2.0%, n = 21). The frequency of Arcobacter in commercial farms in Ghana was 10.3% (n/N = 83/805), while in Tanzania, it was 2.8% (n/N = 12/430). Arcobacter was detected in only 3.6% (n/N = 28/780) of the samples from smallholder farms in Ghana and 1.5% (n/N = 9/617) of the samples from Tanzania. For commercial farms, in Ghana, the presence of Arcobacter was more abundant in pigs (45.1%, n/N = 37/82), followed by ducks (38.5%, n/N = 10/26) and quails (35.7%, n/N = 10/28). According to MALDI-TOF-based species identification, Arcobacter butzleri (91.6%, n/N = 121/132), Arcobacter lanthieri (6.1%, n/N = 8/132), and Arcobacter cryaerophilus (2.3%, n/N = 3/132) were the only three Arcobacter species detected at both study sites. Almost all of the Arcobacter from Ghana (98.2%, n/N = 109/111) were isolated during the rainy season. The inhibition zone diameters recorded for penicillin, ampicillin, and chloramphenicol allowed no determination of an epidemiological cut-off value. However, the results indicated a general resistance to these three antimicrobials. Multidrug resistance was noted in 57.1% (n/N = 12/21) of the Arcobacter isolates from Tanzania and 45.0% (n/N = 50/111) of those from Ghana. The type of farm (commercial or smallholder) and source of the sample (poultry or livestock) were found to be associated with multi-drug resistance. Conclusions The high levels of MDR Arcobacter detected from farms in both countries call for urgent attention and comprehensive strategies to mitigate the spread of antimicrobial resistance in these pathogens.

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.

In the Strategic Plan for the CAP 2023-2027, the goal of special importance is the strengthening ecosystem services. Eco-schemes are a new type of direct support intended for farmers taking additional actions to protect the environment and climate. The study undertakes an institutional and environmental assessment of the proposal to withdraw from implementing some of the assumptions included in the eco-schemes. As a result, the criteria for their availability turned out to be inadequate for smallholder farms. Farmers implementing eco-schemes are exposed to additional expenditures or reduced incomes due to the extensification of production. However, they may receive applicable financial compensation on the annual basis. The current, rather complicated system authorizing payments for 2023, meant that relatively not many farmers made such efforts. At the same time, this results in receiving approximately 50% of support from the direct payments comparing to the previous year. In this paper there has been suggested the need for modification of the eco-schemes with the special emphasis on smallholder farms. It was based on two pillars: technological and environmental, with a proposal of requirements that may be relatively easy to meet in practice.

Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lower-cost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.

Accurate and reliable crop type identification from satellite images provides a foundation for crop yield predictions which paves the way to help ensure food security. Most of the work done in the field of crop type mapping using remote sensing is restricted to the developed countries having large field parcels, while a little effort has been directed towards doing so for developing countries, where this task becomes more challenging due to the small size of field parcels, irregular shapes of the fields, and an acute shortage of labelled datasets for training supervised machine learning models. In this research, we try to fill this gap in the literature by exploring the feasibility of performing the semantic segmentation of agricultural fields from satellite images by proposing an encoder–decoder-based semantic segmentation architecture, CropNet, with a ResNet network as the encoder backbone and the use of attention modules in the decoder to allow the model to focus on more important portions of the feature maps and the feature fusion to concatenate the feature maps from all the decoder nodes getting a more precise prediction by bringing the spatial location information from the previous layers. The architecture outperformed the state of the art by 0.51% and 1.3%, on overall accuracy and macro-F1 score, respectively, after being trained on the "2019 Zindi's Farm Pin Crop Detection" dataset of Sentinel-2 images. The model achieved a field-wise overall classification accuracy of 78.06% and macro-F1 score of 67.3% and a pixel-wise segmentation mean Intersection over Union (mIoU) of 62.22% which is an improvement of 2.56% over the state-of-the-art methods, thereby demonstrating that our model is computationally efficient for the job of semantic segmentation of crop types from the satellite images in the difficult scenario of smallholder farms. [ABSTRACT FROM AUTHOR]

Soils on many smallholder farms in Southern Africa are severely depleted in plant nutrients, in particular phosphorus (P), following years of maize monocropping with little or no fertilizer input. Past studies suggest that pigeon pea (Cajanus cajan (L.) Millsp.) may increase plant-available P. Pigeon pea is not a common crop in much of Southern Africa, and the effect of locally grown pigeon pea varieties on plant-available P is unknown. We assessed the changes in plant-available P after growing pigeon pea varieties MPPV-2, MPPV-3, and Babati White in Zambia, viz. Lixisols of Choma and Mkushi, Acrisols of Chipata and Kasama, and Arenosols of Kaoma. The selected soils were not fertilized. Baseline soils (0–20 cm), sampled after long-term maize monocropping and soils from the same fields after growing pigeon pea were collected from field trials in Kaoma, Chipata, Choma, and Mkushi and analyzed for plant-available P. Further, a greenhouse study was conducted with soils from Kasama, Choma, Kaoma, and Chipata, under which soil P was determined before and after growing pigeon pea, soybean (Dina), and maize (SC 419) without fertilizer addition. Pigeon pea under field studies had no significant (p > 0.05) effect on plant-available P in Choma, Kaoma, and Chipata. In Mkushi, pigeon pea cropping resulted in a 47.5% significant decline (p ≤ 0.05) in plant-available P, amounting to a loss of 11.2 kg ha−1. The greenhouse study showed a significant decline (p ≤ 0.001) in plant-available P after seven weeks of maize growth, while there was no significant (p > 0.05) effect on plant-available P after soybean and pigeon pea cropping. The latter was primarily due to the significantly higher P uptake associated with larger biomass production of maize after seven weeks in the greenhouse. During the initial seven weeks, pigeon pea biomass had significantly higher P concentrations than maize. Thus, P deficiency symptoms were exhibited in maize, while pigeon pea appeared healthy. However, mobilized P, calculated as the sum of plant P and soil P after cropping minus soil P before planting, was significantly lower (p ≤ 0.01) in pigeon pea compared to soybean and maize. Synthesizing field and greenhouse experiments suggests that there is a low net decline of plant-available P from soils after pigeon pea cropping. Therefore, rotation with these pigeon pea varieties could be beneficial to resource-poor farmers due to low P removal and its ability to grow in P-deficient soil. [ABSTRACT FROM AUTHOR]

Background: Arcobacter species are considered emerging foodborne pathogens that can potentially cause serious infections in animals and humans. This cross-sectional study determined the frequency of potentially pathogenic Arcobacter spp. in both commercial and smallholder farm animals in Ghana and Tanzania. A total of 1585 and 1047 (poultry and livestock) samples were collected in Ghana and Tanzania, respectively. Selective enrichment media, along with oxidase and Gram testing, were employed for isolation of suspected Arcobacter spp. and confirmation was done using MALDI-TOF MS. Antibiotic susceptibility was assessed through disk diffusion method and ECOFFs were generated, for interpretation, based on resulting inhibition zone diameters. Results: The overall Arcobacter frequency was higher in Ghana (7.0%, n = 111) than in Tanzania (2.0%, n = 21). The frequency of Arcobacter in commercial farms in Ghana was 10.3% (n/N = 83/805), while in Tanzania, it was 2.8% (n/N = 12/430). Arcobacter was detected in only 3.6% (n/N = 28/780) of the samples from smallholder farms in Ghana and 1.5% (n/N = 9/617) of the samples from Tanzania. For commercial farms, in Ghana, the presence of Arcobacter was more abundant in pigs (45.1%, n/N = 37/82), followed by ducks (38.5%, n/N = 10/26) and quails (35.7%, n/N = 10/28). According to MALDI-TOF-based species identification, Arcobacter butzleri (91.6%, n/N = 121/132), Arcobacter lanthieri (6.1%, n/N = 8/132), and Arcobacter cryaerophilus (2.3%, n/N = 3/132) were the only three Arcobacter species detected at both study sites. Almost all of the Arcobacter from Ghana (98.2%, n/N = 109/111) were isolated during the rainy season. The inhibition zone diameters recorded for penicillin, ampicillin, and chloramphenicol allowed no determination of an epidemiological cut-off value. However, the results indicated a general resistance to these three antimicrobials. Multidrug resistance was noted in 57.1% (n/N = 12/21) of the Arcobacter isolates from Tanzania and 45.0% (n/N = 50/111) of those from Ghana. The type of farm (commercial or smallholder) and source of the sample (poultry or livestock) were found to be associated with multi-drug resistance. Conclusions: The high levels of MDR Arcobacter detected from farms in both countries call for urgent attention and comprehensive strategies to mitigate the spread of antimicrobial resistance in these pathogens. [ABSTRACT FROM AUTHOR]

China is currently experiencing a severe issue of excessive fertilization. Variable rate fertilization (VRF) technology is key to solving this issue in precision agriculture, and one way to implement VRF is through management zone (MZ) delineation. This study is aimed at evaluating the feasibility and potential benefits of VRF based on site-specific MZs in smallholder farm fields. This study determined the amounts of basal and top-dressing fertilizers in different spatial units, based on soil nutrient MZs and crop growth MZs, respectively. The potential agronomic, economic, and environmental advantages of spatial variable rate fertilization were further assessed by comparing the farmer's treatment, the expert's treatment, and the variable rate fertilization treatment based on management zones (VR-MZ). The results showed that VR-MZ reduced the use of nitrogen (N), phosphorus (P), and potassium (K) fertilizers by 22.90–43.95%, 59.11–100%, and 8.21–100%, respectively, and it also increased the use efficiency of N, P, and K by 12.27–28.71, 89.64–176.85, and 5.48–266.89 kg/kg, respectively, without yield loss. The net incomes of VR-MZ were 15.5–449.61 USD ha−1 higher than that of traditional spatially uniform rate fertilization. Meanwhile, less nitrous oxide emission (23.50–45.81%), ammonia volatilization (19.38–51.60%), and nitrate ion leaching amounts (28.77–53.98%) were found in VR-MZ compared to those in uniform fertilization. The results suggest that the VR-MZ has great potential for saving fertilizers, significantly increasing farmers' net income, reducing environmental pollution, and promoting the sustainable use of resources. This study provides a theoretical basis and technical support for exploring a VRF suitable for village-scale farming. [ABSTRACT FROM AUTHOR]

The livestock-dairy sector in Sub-Saharan Africa, particularly in Rwanda, is experiencing rapid growth due to population expansion, urbanisation, and changing food preferences. The unmet local production demands are causing soil and water pollution, competition for biomass, land, and water, but also grassland degradation, biodiversity loss, and increased GHGs emissions. Rwanda has the lowest productivity in the region, largely due to inadequate and poor-quality livestock feed resources. To increase animal productivity, promoting forage species with higher nutritional value and better adaptation to drought-prone and poor-fertility soils could be beneficial. Using a mixed-methods approach, the study explores Brachiaria forage adoption and profitability and analyses policy objectives and measures to overcome adoption barriers and promote the transition from subsistence to market-oriented systems. Results show that Brachiaria, although advantageous from an economic point of view, is characterised by very low adoption rates. Furthermore, access to extension programmes is limited and often not supported by adequate incentives. To overcome such barriers, policy interventions should be harmonised and information and knowledge management prioritised, public and private extension and advisory services (EASs) programmes coordinated, agricultural input subsidies increased, and institutional coordination promoted to enhance climate-smart animal feeding.

The contribution of smallholder farming systems to the National greenhouse gas (GHG) budget is missing in most developing countries, including Kenya. Data on the contribution of smallholder cropping systems to the GHG balance is essential for realising Sustainable Development Goal 13 on climate action, i.e., on nationally determined contributions (NDCs) and in compliance with the Paris Agreement. Do smallholder farming systems act as nature-based solutions for greenhouse gas emissions reduction? This study evaluated GHG emissions from cropping systems under on-farm smallholder farming conditions. We had five cropping systems on two smallholder farms: sole maize, maize-bean intercrop, coffee, banana, and agroforestry. Gas samples were collected using three static chambers per cropping system. The gas samples were analysed using gas chromatography (GC) fitted with a 63Ni-electron capture detector (ECD) for N2O and flame ionisation detector (FID) for CH4 and CO2 using N as carrier gas. Cumulative annual fluxes of (CH4, N2O, and CO2) varied significantly in farms one and two across the cropping systems. The cumulative soil GHG fluxes ranged from -1.34kg CH4C ha−1 yr−1 under agroforestry to -0.77kg CH4C ha−1 yr−1 under banana for CH4, 0.30kg N2ON ha−1 yr−1 to 1.23kg N2ON ha−1 yr−1 for N2O and 5949kg CO2C ha−1 yr−1 to 12,954kg CO2C ha−1 yr−1 for CO2. The maize grain yields ranged from 0 to 3.38 Mg ha−1. The N2O yields scaled emissions ranged from 0.10 to 0.26g kg−1 maize and 0.68 to 1.30g kg−1 beans. Smallholder farmers in Upper Eastern Kenya contribute a limited amount of soil GHG emissions and thus could act as a nature-based solution for lowering agricultural emissions.

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Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately $5 \times 5$ km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6. [ABSTRACT FROM AUTHOR]

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R2 = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R2 = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.

Protracted political conflicts disrupt people's lives, including their ability to feed themselves. Urban planning, operating within the ambit of the state, impacts food systems in conflict cities. We examine the confluence of planning and political misgovernance on food sovereignty in conflict cities. We do so by documenting the experiences of urban growers who cultivate, eat, and distribute indigenous greens (haakh) in the city of Srinagar in the Himalayan belt of Jammu and Kashmir, the site of a protracted conflict. Experiences of growers were analyzed within the context of the city's complex urban planning landscape. Empirical methods included qualitative interviews of urban growers (n = 40) and review of land use plans and policies. We found that haakh production ensured access to affordable, nutritious, and culturally celebrated greens for haakh growing households. That said, intersecting burdens from undemocratic governance and militarism (from India), weak forms of local planning (within Srinagar), and climate change threaten urban growers' work, and imperils food sovereignty. Completing a study in a conflict region was extraordinarily challenging. The study's generalizability is limited by its short duration and small sample size—the inductive findings set the stage for future research. Conflict cities are a reminder that urban planning is anything but technical. Planning curricula must prepare future planners for the politics of planning. Planners in conflict cities are in liminal positions—between the state and the public. To the best of their ability, planners in conflict cities such as Srinagar have to protect smallholder growers' control of their food system, especially over land and water. The monitoring, recording, and suspension of contested or undemocratic land conversions, land grabs, or land transfers without full consent of indigenous and local peoples ought to be a local and international policy priority. [ABSTRACT FROM AUTHOR]

Soils on many smallholder farms in Southern Africa are severely depleted in plant nutrients, in particular phosphorus (P), following years of maize monocropping with little or no fertilizer input. Past studies suggest that pigeon pea (Cajanus cajan (L.) Millsp.) may increase plant-available P. Pigeon pea is not a common crop in much of Southern Africa, and the effect of locally grown pigeon pea varieties on plant-available P is unknown. We assessed the changes in plant-available P after growing pigeon pea varieties MPPV-2, MPPV-3, and Babati White in Zambia, viz. Lixisols of Choma and Mkushi, Acrisols of Chipata and Kasama, and Arenosols of Kaoma. The selected soils were not fertilized. Baseline soils (0–20 cm), sampled after long-term maize monocropping and soils from the same fields after growing pigeon pea were collected from field trials in Kaoma, Chipata, Choma, and Mkushi and analyzed for plant-available P. Further, a greenhouse study was conducted with soils from Kasama, Choma, Kaoma, and Chipata, under which soil P was determined before and after growing pigeon pea, soybean (Dina), and maize (SC 419) without fertilizer addition. Pigeon pea under field studies had no significant (p > 0.05) effect on plant-available P in Choma, Kaoma, and Chipata. In Mkushi, pigeon pea cropping resulted in a 47.5% significant decline (p ≤ 0.05) in plant-available P, amounting to a loss of 11.2 kg ha−1. The greenhouse study showed a significant decline (p ≤ 0.001) in plant-available P after seven weeks of maize growth, while there was no significant (p > 0.05) effect on plant-available P after soybean and pigeon pea cropping. The latter was primarily due to the significantly higher P uptake associated with larger biomass production of maize after seven weeks in the greenhouse. During the initial seven weeks, pigeon pea biomass had significantly higher P concentrations than maize. Thus, P deficiency symptoms were exhibited in maize, while pigeon pea appeared healthy. However, mobilized P, calculated as the sum of plant P and soil P after cropping minus soil P before planting, was significantly lower (p ≤ 0.01) in pigeon pea compared to soybean and maize. Synthesizing field and greenhouse experiments suggests that there is a low net decline of plant-available P from soils after pigeon pea cropping. Therefore, rotation with these pigeon pea varieties could be beneficial to resource-poor farmers due to low P removal and its ability to grow in P-deficient soil.

China is currently experiencing a severe issue of excessive fertilization. Variable rate fertilization (VRF) technology is key to solving this issue in precision agriculture, and one way to implement VRF is through management zone (MZ) delineation. This study is aimed at evaluating the feasibility and potential benefits of VRF based on site-specific MZs in smallholder farm fields. This study determined the amounts of basal and top-dressing fertilizers in different spatial units, based on soil nutrient MZs and crop growth MZs, respectively. The potential agronomic, economic, and environmental advantages of spatial variable rate fertilization were further assessed by comparing the farmer’s treatment, the expert’s treatment, and the variable rate fertilization treatment based on management zones (VR-MZ). The results showed that VR-MZ reduced the use of nitrogen (N), phosphorus (P), and potassium (K) fertilizers by 22.90–43.95%, 59.11–100%, and 8.21–100%, respectively, and it also increased the use efficiency of N, P, and K by 12.27–28.71, 89.64–176.85, and 5.48–266.89 kg/kg, respectively, without yield loss. The net incomes of VR-MZ were 15.5–449.61 USD ha−1 higher than that of traditional spatially uniform rate fertilization. Meanwhile, less nitrous oxide emission (23.50–45.81%), ammonia volatilization (19.38–51.60%), and nitrate ion leaching amounts (28.77–53.98%) were found in VR-MZ compared to those in uniform fertilization. The results suggest that the VR-MZ has great potential for saving fertilizers, significantly increasing farmers’ net income, reducing environmental pollution, and promoting the sustainable use of resources. This study provides a theoretical basis and technical support for exploring a VRF suitable for village-scale farming.

Few land-allocation models consider the impact of off-farm income on tropical deforestation. We provide a concept to integrate off-farm income in a mechanistic multiple-objective land-allocation model, while distinguishing between farms with and without re-allocation of on-farm labor to obtain off-farm income. On farms with re-allocation of labor we found that off-farm income reduced farmers' financial dependency on deforestation-related agricultural income leading to less tropical deforestation. The influence of off-farm income covered two aspects: availability of additional income and re-allocation of on-farm labor to off-farm activities. The labor effect tended to reduce deforestation slightly more than the income effect. On farms without re-allocation of on-farm labor we showed how farmers can use off-farm income to purchase additional labor to accelerate deforestation. Our study highlights the importance of considering off-farm income in land-use models to better understand, model and possibly curb tropical deforestation. [ABSTRACT FROM AUTHOR]

Worldwide, farm animals, in particular poultry, are an important reservoir for Campylobacter spp. However, information on Campylobacter colonization in farm animals in Africa is scarce. Hence, this cross-sectional study determined antibiotic-resistant Campylobacter from both commercial and smallholder farm animals in the Asante Akim North Municipality of Ghana. Fecal samples from poultry and livestock kept by commercial and smallholder farms were collected and analyzed using standard microbiological methods. The overall Campylobacter frequency was 20.3% (n/N = 322/1,585), and frequencies detected were similarly high in isolates from commercial (21.0%, n/N = 169/805) and smallholder (19.6%, n/N = 153/780) farms. Species isolated were C. coli (67.7%, n/N = 218/322) and C. jejuni (32.3%, n/N = 104/322). However, the frequency of C. coli was 2.1 (95% CI: 1.8-2.5) times higher than what was found for C. jejuni. Campylobacter frequencies in the rainy season was 22.2% (n/N = 258/1,160) and 15.1% (n/N = 64/425) in the dry season (prevalence ratio = 1.48, 95% CI: 1.2-1.9). About 1.7% (n/N = 6/322) of the Campylobacter isolates, all from smallholder farms, were susceptible to all antibiotics tested. Multidrug resistance was observed for 4.7% (n/N = 15/322) of the Campylobacter isolates, of which 93.3% (n/N = 14/15) occurred in isolates from commercial farms. This study highlights the need for the implementation of control programs, in commercial farming but also at the smallholder farm level, to formulate clear guidelines aimed at decreasing Campylobacter contamination of meat products and reducing the use of antibiotics in the farming sector. [ABSTRACT FROM AUTHOR]

ABSTRACTFew land-allocation models consider the impact of off-farm income on tropical deforestation. We provide a concept to integrate off-farm income in a mechanistic multiple-objective land-allocation model, while distinguishing between farms with and without re-allocation of on-farm labor to obtain off-farm income. On farms with re-allocation of labor we found that off-farm income reduced farmers’ financial dependency on deforestation-related agricultural income leading to less tropical deforestation. The influence of off-farm income covered two aspects: availability of additional income and re-allocation of on-farm labor to off-farm activities. The labor effect tended to reduce deforestation slightly more than the income effect. On farms without re-allocation of on-farm labor we showed how farmers can use off-farm income to purchase additional labor to accelerate deforestation. Our study highlights the importance of considering off-farm income in land-use models to better understand, model and possibly curb tropical deforestation.

Worldwide, farm animals, in particular poultry, are an important reservoir for Campylobacter spp. However, information on Campylobacter colonization in farm animals in Africa is scarce. Hence, this cross-sectional study determined antibiotic-resistant Campylobacter from both commercial and smallholder farm animals in the Asante Akim North Municipality of Ghana. Fecal samples from poultry and livestock kept by commercial and smallholder farms were collected and analyzed using standard microbiological methods. The overall Campylobacter frequency was 20.3% (n/N = 322/1,585), and frequencies detected were similarly high in isolates from commercial (21.0%, n/N = 169/805) and smallholder (19.6%, n/N = 153/780) farms. Species isolated were C. coli (67.7%, n/N = 218/322) and C. jejuni (32.3%, n/N = 104/322). However, the frequency of C. coli was 2.1 (95% CI: 1.8–2.5) times higher than what was found for C. jejuni. Campylobacter frequencies in the rainy season was 22.2% (n/N = 258/1,160) and 15.1% (n/N = 64/425) in the dry season (prevalence ratio = 1.48, 95% CI: 1.2–1.9). About 1.7% (n/N = 6/322) of the Campylobacter isolates, all from smallholder farms, were susceptible to all antibiotics tested. Multidrug resistance was observed for 4.7% (n/N = 15/322) of the Campylobacter isolates, of which 93.3% (n/N = 14/15) occurred in isolates from commercial farms. This study highlights the need for the implementation of control programs, in commercial farming but also at the smallholder farm level, to formulate clear guidelines aimed at decreasing Campylobacter contamination of meat products and reducing the use of antibiotics in the farming sector.

Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA. [ABSTRACT FROM AUTHOR]

Agroecology is an approach that seeks to improve the integration of food systems through environmentally sustainable production systems. This paper explores the key practices considered to define agroecological farming in Bikita District of Masvingo in Zimbabwe. It reviewed the literature on agroecology and presented a criterion that informed analysis to distinguish between agroecological and non-agroecological farmers. Agroecological farmers mainly comprised resource-constrained farmers who sought to tap on their natural environment, local knowledge, and social networks to move away from conventional agricultural practices to achieve similar food and nutrition security and livelihood objectives as their non-agroecological counterparts. They differed from their non-agroecological counterparts mainly with respect to the diversity of crops grown, agronomic techniques used, and primary sources of income. Major challenges for farming households practising agroecological approaches includes lack of capacity for sustained adherence to agroecological principles (e.g., in the context of otherwise conventional systems; for all intends and purposes, existing extension messages and support mechanisms do not favor agroecological farming). These farmers, therefore, practice agroecological farming against institutions and policies that do not recognize agroecological farming. There is therefore currently 'no level playing field' for farmers and promoters of agroecological practices in Bikita as in many areas in Zimbabwe. [ABSTRACT FROM AUTHOR]

Abstract African swine fever (ASF) is a highly contagious fatal infectious disease of pigs and wild suids. The disease has a worldwide occurrence and significant impact on pig production. Two adult intensively raised large white boars from two farms in Jos with a history of sudden death were diagnosed of ASF between July and August 2019. Post‐mortem examination of carcasses grossly showed splenomegaly, haemorrhagic lymphadenitis and hepatomegaly with severe congestion. The kidneys were enlarged and had generalized petechiae and blood clot in the pelvis. The heart was moderately enlarged. Microscopic examination of the spleen and lymph nodes revealed severe lymphocytic depletion, haemorrhage and severe haemosiderosis. The liver was severely congested with focal coagulative necrosis of the hepatocytes. The kidneys were severely congested and showed renal tubular necrosis with few tubular protein casts. Tissue samples were confirmed to be positive for African swine fever virus (ASFV) by polymerase chain reaction (PCR) assay, and phylogenetic analysis revealed that the isolate belonged to genotype I.

• DRS models may be used for testing soil samples beyond their calibration zones. • Combining local spectral libraries and spiking improve the accuracy of DRS approach. • DRS and wet chemistry approaches produce similar soil test crop response ratings. Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T 2018) and 607 farms during 2021 (T 2021) covering seven districts of the Bundelkhand region of central India. The T 2018 samples covered 7 development blocks; the T 2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T 2018 dataset and tested against the T 2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T 2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T 2018 dataset was spiked with 20% samples from the T 2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms. [ABSTRACT FROM AUTHOR]

The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems. [ABSTRACT FROM AUTHOR]

Organic matter inputs positively affect soil fertility and quality but management effects on the soil and plant microbiome are less understood. Therefore, we studied the response of microbial colonization of the East African highland banana cultivar "Mpologoma" (AAA genome) under different mulch and manure treatments on three representative smallholder farms in Uganda. In general, the gammaproteobacterial community appeared stable with no significant response to organic matter inputs after 24 months of treatment. Significant differences (p < 0.05) in the plant-associated carpo-, phyllo-, and rhizosphere microbial community composition and diversity were found among individual sampled farms, independent of added soil inputs. Across farms, banana fruit harbored a richer and more balanced gammaproteobacterial community than the rhizo- and endospheres. Gammaproteobacterial beta diversity was shaped by the microenvironment (44%) as well as the sampling site (4%). Global effects of treatments in the rhizosphere analyzed using linear discriminant analysis effect size showed significantly enriched genera, such as Enterobacter , under manure and mulch treatments. As shown in previous works, bunch size and total yield were highly increased with manure and mulch, however, our results highlight general short-term microbial stability of Ugandan banana cropping systems with increases in the gammaproteobacterial community. [ABSTRACT FROM AUTHOR]

Conservation agriculture (CA) is recognized as a promising crop management strategy for sustainable agricultural intensification. The objective of this study was to evaluate CA cropping systems for rainfed maize as an alternative to the traditional tillage-based cropping systems (CT) in the context of family farms, using a multi-criteria model that represents the point of view of farmers. Farmers considered several aspects for evaluating the cropping systems, thatwere systematized in the model through five criteria (with sub-criteria): (a) costs; (b) yield; (c) labour; (d) human health and environment; and (e) production risks. CA did not differ from CT for the ‘costs’ criterion but was superior for the ‘yield’ and ‘labour’ criteria. In contrast, CT obtained better ratings for the criteria ‘human health and environment’ and ‘production risks’. Considering all criteria, CA was better appraised than CT. However, a new local policy measure that subsidizes the hiring of mechanized tillage services overturns this outcome, indicating the importance of exogenous factors. Overall, the participatory processes in building the model allowed us to better understand the reasons of adoption or non-adoption of CA by small-scale farmers in the tropics.

Organic matter inputs positively affect soil fertility and quality but management effects on the soil and plant microbiome are less understood. Therefore, we studied the response of microbial colonization of the East African highland banana cultivar “Mpologoma” (AAA genome) under different mulch and manure treatments on three representative smallholder farms in Uganda. In general, the gammaproteobacterial community appeared stable with no significant response to organic matter inputs after 24 months of treatment. Significant differences (p < 0.05) in the plant-associated carpo-, phyllo-, and rhizosphere microbial community composition and diversity were found among individual sampled farms, independent of added soil inputs. Across farms, banana fruit harbored a richer and more balanced gammaproteobacterial community than the rhizo- and endospheres. Gammaproteobacterial beta diversity was shaped by the microenvironment (44%) as well as the sampling site (4%). Global effects of treatments in the rhizosphere analyzed using linear discriminant analysis effect size showed significantly enriched genera, such as Enterobacter, under manure and mulch treatments. As shown in previous works, bunch size and total yield were highly increased with manure and mulch, however, our results highlight general short-term microbial stability of Ugandan banana cropping systems with increases in the gammaproteobacterial community.

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The present study investigated the seroprevalence of Leptospira hardjo antibodies and their relationship to the burden of reproductive disorders associated with smallholder ruminant production systems in Maiduguri, northeastern Nigeria. We randomly collected 376 blood samples from 11 cattle (n=188) and 10 goats (n.=188) farms in Maiduguri from April to September 2019. The farmers completed structured questionnaires to furnish information regarding herd characteristics and reproductive histories of farm animals. A serum IgG/IgM antibody-capture ELISA test kit with a sensitivity of 96.91%, specificity of 90.40%, positive and negative predictive values of 88.68% and 97.41%, respectively, was used for the detection of Leptospira hardjo antibodies from blood serum. The overall seroprevalence of Leptospira hardjo was 4.26% (95% CI: 2.17-8.17) in cattle and 2.13% (95% CI: 0.83-5.34) in goats, respectively. Leptospira hardjo antibodies were detected in 4 (50%) out of the 8 cattle herds and 3 out of the 7 goat flocks with a history of reproductive disorders. At the present rate of detection of Leptospira hardjo antibodies in ruminants with histories of reproductive disorders, the current burden of the disease and its consequences on the reproductive efficiencies could be highly underestimated in the smallholder ruminant production system in the northeastern part of Nigeria. [ABSTRACT FROM AUTHOR]

Leguminous plants are known to require phosphorus fertilizers and inoculation with nitrogen fixing rhizobia for optimum yield but other nutrients may also be lacking. In this study, the most limiting nutrients for legume growth were determined in soils where the crops had not responded to P and rhizobial inoculation in field trials, using the double pot technique. Soils were collected from 17 farmers' fields in West Kenya, Northern Nigeria, Eastern and Southern Rwanda, South-west and North-west Sierra Leone. Plant growth and mean biomass were measured on soils to which a full nutrient solution, containing phosphorus (P), potassium (K), magnesium (Mg), sulfur (S) and micronutrients (MN) were added, and which were compared to a control (no nutrient added), and individual omissions of each nutrient. The relationship between soil properties and nutrient deficiencies was explored. Nutrient limitations were found to differ between soils, both within and across countries. Generally, each soil was potentially deficient in at least one nutrient, with K, P, Mg, MN and S emerging as most limiting in 88, 65, 59, 18, and 12% of tested soils, respectively. While K was the most limiting nutrient in soils from Kenya and Rwanda, P was most limiting in soils from Nigeria. P and K were equally limiting in soils from Sierra Leone. Mg was found limiting in two soils from Kenya and three soils from Rwanda and one soil each in Nigeria and Sierra Leone. Micronutrients were found to be limiting in one soil from Nigeria and one soil from Rwanda. Estimates of nutrient deficiency using growth and mean biomass were found to be correlated with each other although the latter proved to be a more sensitive measure of deficiency. With few exceptions, the relation between soil parameters and nutrient deficiencies was weak and there were no significant relations between deficiency of specific nutrients and the soil content of these elements. Although our results cannot be translated directly to the field, they confirm that individual and multiple nutrient deficiencies were common in these “non-responsive” soils and may have contributed to reported low yields. This highlights the need for balanced nutrition in legume production in SSA.

It is often claimed that cocoa producers are poor, but the extent of their poverty is rarely defined. We analyzed six data sets derived from household questionnaires of 385–88,896 cocoa producers in Côte d'Ivoire and Ghana. Across all data sets, many households (30–58%) earn a gross income below the World Bank extreme poverty line and the majority (73–90%) do not earn a Living Income. Households with less income per person per day generally achieve lower cocoa yields, consist of more household members, have a smaller land size available, and rely more on cocoa income than households with higher incomes. When comparing the effects of increasing prices and yields on gross income, yield increases lead to larger benefits especially for the poorest households. Doubling the cocoa price would leave 15–25% of households with a gross income below the extreme poverty line and 53–65% below the Living Income benchmark. At yields of 600 kg/ha, against current yields around 300 kg/ha, these percentages are reduced to 7–11 and 48–62%, respectively, while at yields of 1,500 kg/ha only 1–2% of households remain below the extreme poverty line and 13–20% below the Living Income benchmark. If we assume that the production costs of achieving a yield of 1,500 kg/ha are 30% of revenue, still only 2–4% of households earn a net income below the extreme poverty line and 25–32% below the Living Income benchmark. Whilst sustainable intensification of cocoa production is undoubtedly a strong approach to increase cocoa yields and farmer incomes, achieving this does not come without pitfalls. The poorer households face multiple barriers to invest in cocoa production. A better understanding of cocoa producing households and the resources available to them, as well as the opportunity for alternative income generation, is required to tailor options to increase their income. The utility and interpretability of future household surveys would be drastically improved if definitions and variables addressed were approached in a standardized way.