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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., African swine fever is a highly contagious fatal infectious disease of pigs with a worldwide occurrence and economic importance. Two adult large white boars from two farms in Jos were diagnosed with African swine fever in 2019 through gross and histopathological evaluations, viral isolation and polymerase chain reaction. Sequences obtained were compared and deposited in the GenBank and were accessioned MN888693 and MN888694 and the phylogram showed that the ASFV belongs to genotype I.

The objectives of this paper are twofold: (i) to calibrate and validate the PILOTEN crop model for tomato (Solanumlycopersicum L.), and (ii) to use the model to examine the response of tomato yield to different irrigation and fertilization schedulingscenarios under varying climatic conditions in order to draw up management recommendations for smallholder farms. Thecalibration and validation were performed by using data collected from field experiments carried out in the north-east of SouthAfrica, on shallow Ferrosol and Luvisol soils. The scenarios dealt with: (i) the initial soil-water availability (fulfilling or not thesoil-water reserve at planting); (ii) the irrigation dose frequency (high versus low irrigation doses ranging from 10 to 40 mm perapplication); (iii) the fertilization dose frequency (full versus split fertilization application). The results indicated that only thefertilization scheduling may significantly affect tomato yields, while irrigation scheduling has a minor role. However, comparedto the local official irrigation guidelines, it is recommended to slightly increase the irrigation doses during the first 4 weeks ofgrowth for tomato grown between early September and mid-December, and to slightly decrease the doses in the second partof the growing cycles starting in February and May. It is also suggested to avoid applying the entire fertilizer dose after seedlingplanting for tomato grown between mid-February and late June; Ce travail a un double objectif: (i) calibrer et valider le modèle Piloten pour la tomate (Solanum lycopersicum L.) et (ii) utiliserle modèle pour analyser la réponse du rendement de la tomate à différents scénarios d’irrigation et de fertilisation, sousdifférentes conditions climatiques, afin de proposer des recommandations de conduites culturales aux petites exploitations.Le calage et la validation du modèle ont été effectués en utilisant des données recueillies par des expérimentations menées dansle nord-est de l’Afrique du Sud, sur des Ferrosols et des Luvisols peu profonds. Les scénarios ont traité: (i) de la disponibilitéinitiale en eau du sol (réserve en eau du sol reconstituée ou non à la plantation); (ii) du couple dose-fréquence d’irrigation(doses d’irrigation élevées à faibles allant de 10 à 40 mm par application); (iii) du couple dose-fréquence de fertilisation.Les résultats ont indiqué que seul le calendrier de la fertilisation peut affecter de manière significative les rendements de latomate, tandis que le calendrier d’irrigation joue un rôle mineur. En comparaison avec les recommandations locales de lagestion de l’irrigation et de la fertilisation de la tomate, il est recommandé d’augmenter légèrement les doses d’irrigation pendantles 4 premières semaines de croissance pour la tomate cultivée entre le début septembre et la mi-décembre, et de diminuerlégèrement les doses dans la deuxième partie du cycle de croissance commençant en février et en mai. Il est également suggéréd’éviter l’application de la totalité de la dose d’engrais après les semis de tomate cultivée entre mi-février et fin-juin

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.

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.

African swine fever (ASF) is a viral disease of suids that frequently leads to death. There are neither licensed vaccines nor treatments available, and even though humans are not susceptible to the disease, the serious socio-economic consequences associated with ASF have made it one of the most serious animal diseases of the last century. In this context, prevention and early detection play a key role in controlling the disease and avoiding losses in the pig value chain. Target biosecurity measures are a strong strategy against ASF virus (ASFV) incursions in farms nowadays, but to be efficient, these measures must be well-defined and easy to implement, both in commercial holdings and in the backyard sector. Furthermore, the backyard sector is of great importance in low-income settings, mainly for social and cultural practices that are highly specific to certain areas and communities. These contexts need to be addressed when authorities decide upon the provisions that should be applied in the case of infection or decide to combine them with strict preventive measures to mitigate the risk of virus spread. The need for a deeper understanding of the smallholder context is essential to prevent ASFV incursion and spread. Precise indications for pig breeding and risk estimation for ASFV introduction, spread and maintenance, taking into account the fact that these recommendations would be inapplicable in some contexts, are the keys for efficient target control measures. The aim of this work is to describe the 305 outbreaks that occurred in domestic pigs in Sardinia during the last epidemic season (2010–2018) in depth, providing essential features associated with intensive and backyard farms where the outbreaks occurred. In addition, the study estimates the average of secondary cases by kernel transmission network. Considering the current absence of ASF outbreaks in domestic pig farms in Sardinia since 2018, this work is a valid tool to specifically estimate the risk associated with different farm types and update our knowledge in this area.

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.

In November 2014 I suffered a major stroke and was unable to work. I recently began to ‘wake up’ and have been able to re-engage with literature and discussions on agricultural development and climate change. I am surprised how familiar I am with much of the debate, but disappointed that major issues receive little attention – such as the impacts of climate change on smallholder agriculture, conservation of biodiversity and the need for reform of global financial institutions. In this short ‘perspective’, I comment on issues where there has been little academic attention over the past seven years and new issues that have arisen such as animal-free foods and artificial intelligence. I conclude by suggesting where attention needs to be focused. I expect this to be my final contribution to the literature.

The objective of research was to determine the effect of different protein levels on the carcass and meat characteristics of male Bali cattle fattened in smallholder farms. The cattle used were 18 heads aged 2 to 2.5 years with initial body weight of 229.86±12.46 kg. The experimental cattle were randomly divided into 3 treatment groups i.e., T₀, T₁, and T₂. The T₀ group cattle were fed ration according to traditional practices of fattening cattle by farmers (100% forage). The T₁ group cattle were fed ration with 12% CP and 72% TDN. The T₂ group cattle were fed ration with 15% CP and 72% TDN. The variables measured were final body weight and the characteristics of the carcass (meat, fat, and ratio of meat to bone), and meat quality including the chemical composition of the meat (protein, fat, and collagen), meat tenderness, cooking loss, water holding capacity, and degree of acidity (pH). Data were analyzed by variance analysis. Results of research showed that the final body weight, and weight of carcass, meat, and fat of T₁ and T₂ groups were similar; conversely, those parameters in T₀ group cattle were lower (P

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.

Background Poultry farming is widely practiced by rural households in Vietnam and the vast majority of domestic birds are kept on small household farms. However, smallholder poultry production is constrained by several issues such as infectious diseases, including avian influenza viruses whose circulation remains a threat to public health. This observational study describes the demographic structure and dynamics of small-scale poultry farms of the Mekong river delta region. Method Fifty three farms were monitored over a 20-month period, with farm sizes, species, age, arrival/departure of poultry, and farm management practices recorded monthly. Results Median flock population sizes were 16 for chickens (IQR: 10–40), 32 for ducks (IQR: 18–101) and 11 for Muscovy ducks (IQR: 7–18); farm size distributions for the three species were heavily right-skewed. Muscovy ducks were kept for long periods and outdoors, while chickens and ducks were farmed indoors or in pens. Ducks had a markedly higher removal rate (broilers: 0.14/week; layer/breeders: 0.05/week) than chickens and Muscovy ducks (broilers: 0.07/week; layer/breeders: 0.01–0.02/week) and a higher degree of specialization resulting in a substantially shorter life span. The rate of mortality due to disease did not differ much among species, with birds being less likely to die from disease at older ages, but frequency of disease symptoms differed by species. Time series of disease-associated mortality were correlated with population size for Muscovy ducks (Kendall’s coefficient τ = 0.49, p-value

Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (

The research was aimed to determine the effect of season on feed quantity and quality, and growth performance of male Bali cattle fattened in smallholder farms. Observation from January to March for rainy season and June to August for dry season. Feed quantity and quality were observed covering all feedstuff used farmers in fattening. Cattle used for rainy season was 50 heads aged 1.5 – 4.0 years with initial body weight (BW) 188.03±31.85 kg. In dry season, observation was continued for the same cattle as many as 35 heads aged 2.0 – 4.5 years and initial BW was 261.80±38.46 kg. Data were analyzed by quantitative descriptiption for feed quantity and quality, while growth performance were analyzed by independent T-Test. Results of study showed the dominant type of feedstuff used during rainy and dry season were L.leucocephala (56.00 vs 33.14%), Zea mays fresh straw 18.44% (rainy season), natural grass (13.00 vs 22.25%), P.purpuroides (3.68 vs 3.99%) and G.sepium (1.36 vs 29.69%). Crude protein (CP) of L.leucocephala and G.sepium higher during the dry season, while S. grandiflora, natural grass, and P.purpuroides higher in rainy season. The DMD (%) of L. leucocephala and S. grandiflora in rainy and dry seasons were almost the same, but G.sepium had DMD higher in dry season. The OMD L.leucocephala and G. sepium higher during the dry season, while the S.grandiflora had higher during the rainy season. Natural grass and P.purpuroides had DMD and OMD higher during the rainy season. Dry matter intake (kg/head/day) of cattle during dry season was higher (P

Food security in Sub-Saharan Africa, particularly in semi-arid tropics (41% of the region; 6 months of dry season) is threatened by droughts, dry spells and infertile soils. In Zimbabwe, 74% of smallholder farming areas are located in semi-arid areas mostly in areas with soils of low fertility and water holding capacity. The dominant crop in these areas, maize (Zea mays L.), is susceptible to drought. Under smallholder farming in Zimbabwe, conventional tillage entails cutting and turning the soil with a mouldboard plough thereby burying weeds and crop residues. Seed is planted by hand into a furrow made by the plough, ensuring that crops germinate in relatively weed free seedbeds. Inter-row weed control is performed using the plough or ox-drawn cultivators and hand hoes. Conventional tillage has been criticised for failure to alleviate negative effects of long dry spells on crops and to combat soil loss caused by water erosion estimated at 50 to 80 t ha-1 yr-1. Therefore, conservation tillage has been explored for improving soil and water conservation and crop yields. Our objective was to determine the maize yield advantage of the introduced technology (conservation tillage) over conventional tillage (farmers’ practice) based on a review of experiments in semi-arid Zimbabwe. We use a broad definition of conservation tillage instead of the common definition of =30% cover after planting. Eight tillage experiments conducted between 1984 and 2008 were evaluated. Conventional tillage included ploughing using the mouldboard plough and digging using a hand hoe. Conservation tillage included tied ridging (furrow diking), mulch ripping, clean ripping and planting pits. Field-edge methods included bench terraces (fanya juus) and infiltration pits. Results showed small yield advantages of conservation tillage methods below 500 mm rainfall. For grain yields =2.5 t ha-1 and rainfall =500 mm, 1.0 m tied ridging produced 144 kg ha-1 and mulch ripping 344 kg ha-1 more than conventional tillage. Above 2.5 t ha-1 and for rainfall >500 mm, conventional tillage had =640 kg ha-1 yield advantage. Planting pits had similar performance to ripping and conventional tillage but faced digging labour constraints. Experiments and modelling are required to test conservation tillage seasonal rainfall thresholds. Constraints to adoption of conservation tillage by smallholder farmers necessitate best agronomic practices under conventional tillage while work on adoption of alternative tillage methods continues.

The aim of this paper was to investigate the key factors limiting maize ( Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity.A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers.

Global food production strongly depends on availability of nutrients. Assessment of future global phosphorus (P) fertilizer demand in interaction with nitrogen (N) and potassium (K) fertilizers under different levels of food demand requires a model-based approach. In this paper we tested use of the QUEFTS model (Quantitative Evaluation of Fertility of Tropical Soils) for assessing crop yields in response to N, P and K application in different environments. QUEFTS was initially developed to simulate interactions between N, P and K for tropical soils under maize crop. We performed an extensive model analysis of crop yields in relation to soil and fertilizer nutrients for six field data sets with maize, rice, and wheat crops grown in tropical and temperate regions. The model equations had to be adapted to broaden the model applicability beyond the original boundary conditions of pH, rain-fed cropping systems, optimum harvest index and temperature. Recalibration and modification resulted in a good agreement between model predicted and observed yields. Our results indicate that the adjustments increased the applicability of the model. However, for application in global studies QUEFTS is data demanding and, also, further testing (and probably improvement) is needed, since various processes (e.g. inputs of other nutrients than N, P and K, sub-soil properties and water supply) are ignored in the model, but may differ dramatically across the globe.

The carbon footprint (CFP) assessment of smallholders offers key information on the capacities and challenges for greenhouse gas (GHG) mitigation at farm scale. This allows prioritizing the practices that ensure both the food security of farmers and the low carbon impact associated to climate change. To tackle food security challenges and to maintain sustainable environment production, agroecological practices were planned for farmers in the Itasy region, Central Highlands of Madagascar. The project consisted of agroforestry and forestry systems, composting of organic matters, and system of rice intensification. The goals of this study were (i) to assess the CFP of farms in the Itasy region Central Madagascar, (ii) to assess the impact of agroecological practices adopted by farmers on farms CFP, and (iii) to compare the impact of Tier 1 and Tier 3 factors for carbon removal in woody biomass and in cropland soils on farms CFP. For these purposes, a survey of 192 representative farms was realized during the years 2012-2013. Agroecological practices integrated at farm scale reduced significantly farms CFP up to 364% in terms of land surface and up to 578% in terms of food production, suggesting an important GHG sequestration at farm scale. Main sources of GHG at farms scale were: nitrous oxide from soil management (25%), methane from rice cultivation (24%), livestock manure management (24%), and enteric fermentation (23%). Trees planted in agroforestry and forestry systems offered the highest GHG mitigation benefits. Tier 1 factors overestimated up to 7 times higher the farms CFP compared to Tier 3 factors. This study highlights that the integration of agro-ecological practices at farm scale offers significant GHG mitigation and carbon sequestration in Malagasy context, thus giving an alternative for climate change mitigation.

R esearch was aimed to determine nitrogen balance, microbial protein synthesis, and blood metabolites of male Bali cattle fattening fed ration with different protein level in smallholder farms North Central Timor, Province of East Timor Tenggara, Indonesia. The cattle used were 18 heads aged 2 to 2.5 years with initial body weight of 229.86±12.46 kg. The cattle were randomly divided into three treatment groups. The T 0 group was given feed the same as traditional fattening cattle practices by farmers , T 1 group fed ration contain ing 12% crude protein ( CP) and 72% total digestible nutrients (TDN ), and T 2 group fed ration contain ing 15% CP and 72% TDN . C attle were fed individually for 90 days and drinking water ad libitum . The data were analyzed by analysis of variance. Results of research indicated the nitrogen balance, and blood urea nitrogen between T 1 and T 2 were relatively similar, but those were higher (P

Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.