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Until today, anti-microbial drugs have been the therapy of choice to combat bacterial diseases. Resistance against antibiotics is of growing concern in man and animals. Stress, caused by demanding environmental conditions, can reduce immune protection in the host, influencing the onset and outcome of infectious diseases. Therefore psychoneuro-immunological intervention may prove to be a successful approach to diminish the impact of diseases and antibiotics use. This study was designed to investigate the effect of social and environmental enrichment on the impact of disease, referred to as \"disease susceptibility\", in pigs using a co-infection model of PRRSV and A. pleuropneumoniae. Twenty-eight pigs were raised in four pens under barren conditions and twenty-eight other pigs were raised in four pens under enriched conditions. In the enriched pens a combination of established social and environmental enrichment factors were introduced. Two pens of the barren (BH) and two pens of the enriched housed (EH) pigs were infected with PRRSV followed by A. pleuropneumoniae, the other two pens in each housing treatment served as control groups. We tested if differences in disease susceptibility in terms of pathological and clinical outcome were related to the different housing regimes and if this was reflected in differences in behavioural and immunological states of the animals. Enriched housed pigs showed a faster clearance of viral PRRSV RNA in blood serum (p = 0.014) and histologically 2.8 fold less interstitial pneumonia signs in the lungs (p = 0.014). More barren housed than enriched housed pigs developed lesions in the lungs (OR = 19.2, p = 0.048) and the lesions in the barren housed pigs showed a higher total pathologic tissue damage score (p<0.001) than those in enriched housed pigs. EH pigs showed less stress-related behaviour and differed immunologically and clinically from BH pigs. We conclude that enriched housing management reduces disease susceptibility to co-infection of PRRSV and A. pleuropneumoniae in pigs. Enrichment positively influences behavioural state, immunological response and clinical outcome in pigs.

Food production is highly dependent on regional yields of crops. Regional differences in grain yields could be due to fertilizer management and climate variability. Here, we analyze trends of grain yields in North China, Northeast China, East China, and Central and Southwest China from 1992 to 2012, using the Chinese statistical yearbooks. We estimate the major factors influencing yield by regression analysis. Results show that fertilizer indices were 40 % for Northeast China, 36 % for East China, 9 % for North China, and 6.8 % for Central and Southwest China. Soil indices were 67 % for Northeast China, 14 % for East China, 90 % for North China, and 6 % for Central and Southwest China. The indices of annual mean temperature were positive for Central and Southwest China (72 %) and East China (39 %), but negative for Northeast China (−15 %) and North China (−16 %). Soil factors explained 80 % of yield variation in North China. Annual mean temperature explained 80 % of yield variation in Central and Southwest China.

For integrated pest management (IPM) programs to be maximally effective, monitoring of the growth and decline of the pest populations is essential. Here, we present the validation results of a new automated monitoring device for the poultry red mite (Dermanyssus gallinae), a serious pest in laying hen facilities world-wide. This monitoring device (called an “automated mite counter”) was validated in experimental laying hen cages with live birds and a growing population of D. gallinae. This validation study resulted in 17 data points of ‘number of mites counted’ by the automated mite counter and the ‘number of mites present’ in the experimental laying hen cages. The study demonstrated that the automated mite counter was able to track the D. gallinae population effectively. A wider evaluation showed that this automated mite counter can become a useful tool in IPM of D. gallinae in laying hen facilities.

Rapid demographic ageing substantially affects socioeconomic development 1 – 4 and presents considerable challenges for food security and agricultural sustainability 5 – 8 , which have so far not been well understood. Here, by using data from more than 15,000 rural households with crops but no livestock across China, we show that rural population ageing reduced farm size by 4% through transferring cropland ownership and land abandonment (approximately 4 million hectares) in 2019, taking the population age structure in 1990 as a benchmark. These changes led to a reduction of agricultural inputs, including chemical fertilizers, manure and machinery, which decreased agricultural output and labour productivity by 5% and 4%, respectively, further lowering farmers’ income by 15%. Meanwhile, fertilizer loss increased by 3%, resulting in higher pollutant emissions to the environment. In new farming models, such as cooperative farming, farms tend to be larger and operated by younger farmers, who have a higher average education level, hence improving agricultural management. By encouraging the transition to new farming models, the negative consequences of ageing can be reversed. Agricultural input, farm size and farmer’s income would grow by approximately 14%, 20% and 26%, respectively, and fertilizer loss would reduce by 4% in 2100 compared with that in 2020. This suggests that management of rural ageing will contribute to a comprehensive transformation of smallholder farming to sustainable agriculture in China. Rural population ageing reduces the sustainability of smallholder farming in China, but the transition to a new farming model could reverse the negative effects of rural population ageing.

Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others’ learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.

This paper provides an analysis of U.S. farmland owners, operators, and workers by race, ethnicity, and gender. We first review the intersection between racialized and gendered capitalism and farmland ownership and farming in the United States. Then we analyze data from the 2014 Tenure and Ownership Agricultural Land survey, the 2012 Census of Agriculture, and the 2013–2014 National Agricultural Worker Survey to demonstrate that significant nation-wide disparities in farming by race, ethnicity and gender persist in the U.S. In 2012–2014, White people owned 98% and operated 94% of all farmland. They generated 98% of all farm-related income from land ownership and 97% of income from farm owner-operatorship. Meanwhile, People of Color farmers (African American or Black, Asian American, Native American, Hawaiian or other Pacific Islander, and Hispanic farmers) were more likely to be tenants rather than owners, owned less land, and generated less farm-related wealth per person than their White counterparts. Hispanic farmers were also disproportionately farm laborers. In addition to racial and ethnic disparities, there were disparities by gender. About 63% of non-operating landowners, 86% of farm operators, and 87% of tenant farmers were male, and female farmers tended to generate less income per farmer than men. This data provides evidence of ongoing racial, ethnic and gender disparities in agriculture in the United States. We conclude with a call to address the structural drivers of the disparities and with recommendations for better data collection.

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low- and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer’s plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study evaluates ground- and satellite-based approaches to estimating crop yields and yield responsiveness to inputs, using data on maize from Eastern Uganda. Using unique, simultaneous ground data on yields based on farmer reporting, sub-plot crop cutting, and full-plot harvests across hundreds of smallholder plots, we document large discrepancies among the ground-based measures, particularly among yields based on farmer-reporting versus sub-plot or full-plot crop cutting. Compared to yield measures based on either farmer-reporting or sub-plot crop cutting, satellite-based yield measures explain as much or more variation in yields based on (gold-standard) full-plot crop cuts. Further, estimates of the association between maize yield and various production factors (e.g., fertilizer, soil quality) are similar across crop cut- and satellite-based yield measures, with the use of the latter at times leading to more significant results due to larger sample sizes. Overall, the results suggest a substantial role for satellite-based yield estimation in measuring and understanding agricultural productivity in the developing world.

The increased adoption of fertilizer and improved seeds are two key aspects to raising the level of land productivity in Ethiopian agriculture. However, the adoption and diffusion of such technologies has been slow. We use data from Ethiopia between 1999–2009 to examine the role of learning from extension agents versus learning from neighbors for both improved seeds and fertilizer adoption. We combine farmers' spatial networks with panel data to identify these influences, and find that while the initial impact of extension agents was high, the effect wore off after some time, in contrast to learning from neighbors.

Despite the efforts to enhance adoption of innovative technologies (IT) by the Tunisian Government through national and/or international development projects, the potential rate of adoption of these technologies has remained low among farmers. This study aims at shedding some light on the potential factors that influence IT adoption in the Tunisian arid areas. Technological, economic, institutional factors and human specific factors (social) are selected to be the determinants of agricultural technology adoption. A quantitative approach employing a cross-sectional design was used to gather data. Stratified random sampling was employed and a total of 200 small-scale farmers (100 adopters and 100 non-adopters) were sampled. Data analysis and assessment was done through descriptive and statistical inferential analysis, and econometric modeling using the binary logistic regression model. Empirical findings show that economic and socio-demographic factors such as farmer education, size of cattle flocks and off-farm income were statistically significant and had positive influence on technology adoption while age and farmer experience had significant and negative effects on IT adoption. The findings confirm the important role of institutional factors (being a member of an association, benefiting from extension services and source of technology knowledge) in the adoption decision of IT, particularly when such variables were found to be significant and positives. In contrast, labor and credit services do not significantly influence adoption of IT. Based on these results, Government should focus on educating young farmers with large cattle flock size and off-farm income to enhance the adoption of IT for livestock holders. It should also intensify training programs for farmers and for extension agents with the collaboration of the project managers and the involvement of the profession and the private sector. Finally, the open innovation strategy including all stakeholders during idea generation could be considered as a better way to decrease technology development costs and improve IT adoption.

A common and driving assumption in agricultural research is that the introduction of research trials, new practices and innovative technologies will result in technology adoption, and will subsequently generate benefits for farmers and other stakeholders. In Lao PDR, the potential benefits of introduced technologies have not been fully realised by beneficiaries. We report on an analysis of a survey of 735 smallholder farmers in Southern Lao PDR who were questioned about factors that influenced their decisions to adopt new technologies. In this study, we have constructed measures or states of adoption which identify key elements of an adoption decision-making nexus. Analysis was conducted to statistically group explanatory factors of adoption. The key explanatory factors represented attributes of the farmer, the factors considered when undertaking production decisions and elements of the agricultural value chain that present as opportunities or constraints. We describe the combination of farmer’s personal attributes, perceptions of the value chain, and the introduction of new technologies by external actors as an “agricultural research value chain”, where agricultural research activities intervene to derive greater benefits for local farmers. A generalised linear model, via Poisson (multiple) regression analysis on the identified explanatory factors, was applied to explore how they influence adoption measures and we found several significant relationships.