Search

The agricultural productivity and sustainability in Eastern Gangetic Plain Zones of India are threatened because of the inefficiency of current production practices, shortage of resources, and socioeconomic constraints. We hypothesized the potential impact of intensified cereal systems with mung bean as a third crop within the annual cropping cycle. We assessed economic, social, and environmental indicators for intensified and current cropping system management practiced by different farm types in the region using the FarmDESIGN model. Building on a farm typology constructed for the region in our past research, we used five types of farmers: part-time (PT), well-endowed (WE), small-scale (SS) crop and livestock mix, medium-scale (MS), and resource-poor farmers (RP) in this study. The performance indicators of the 229 original cropping systems cultivated within the 43 farms varied strongly in the eight performance indicators. This variability of cropping systems performance within the farm types resulted in the absence of significant differences between the types. Compared to the original cropping systems, the intensified cropping systems with mung bean not only performed high in dietary energy (DE) production and organic matter (OM) inputs into the soil but also had high application rates of biocides and minimized losses of nitrogen (N). The intervention systems were low in labor requirement and scored at an intermediate level for crop gross margin, water use, and greenhouse gas (GHG) emissions. The ranges of areas of maize- and rice-based systems that could be replaced by intensified systems were largest for the WE and RP farm types. This was reflected in large ranges of change in the performance indicators, but no significant differences in response were found between the farm types. The intensification of maize- and rice-based systems with the proposed intervention cropping systems involving mung bean would result in increased profitability, higher DE yield, and lower requirements for labor and water as the proportion of the farms being converted increases. However, the use of biocides would increase, while the intervention cropping systems would have no significant effect on OM input, GHG emissions, and soil N losses. [ABSTRACT FROM AUTHOR]

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow. Stress-tolerant crop varieties for cultivation in innovative cropping systems will be crucial under climate-changed environments. A survey of 588 experts predicted traits that will be essential for pearl millet, sorghum, maize, groundnut, cowpea, and common bean, particularly in sub-Saharan Africa. This review discusses the status of breeding and novel approaches to use genetic resources for the prioritized traits, and concludes that the greatest challenge to developing varieties to win the race between climate change and food security will be our foresight in defining, and boldness to prioritize breeding for future-essential traits. [ABSTRACT FROM AUTHOR]

Summary: Farmers around the world are increasingly vulnerable: climate variability is identified as the primary stressor, but unfavorable biophysical circumstances and disturbances in the socioeconomic domain (labor dynamics and price volatility) also affect farm management and production. To deal with these disturbances, adaptations are recognized as essential. Antifragility acknowledges that adaptations and volatility are inherent characteristics of complex systems and abandons the idea of returning to the pre-disturbance system state. Instead, antifragility recognizes that disturbances can trigger reorganization, enabling selection and removal of weaker system features and allowing the system to evolve toward a better state. In this study, we assessed the vulnerability of different types of smallholder farms in Bihar, India, and explored the scope for more antifragile farming systems that can 'bounce back better' after disturbances. Accumulation of stocks, creation of optionality (i.e., having multiple options for innovation) and strengthening of farmer autonomy were identified as criteria for antifragility. We had focus group discussions with in total 92 farmers and found that most expressed themselves to be vulnerable: they experienced challenges but had limited adaptive capacity to change their situation. They mostly made short-term decisions to cope with or mitigate urgent challenges but did not engage in strategic planning driven by longer-term objectives. Instead, they waited for governmental support to improve their livelihoods. Despite being confronted with similar challenges, four positive deviant farmers showed to be more antifragile: their diverse farming systems were abundant in stocks and optionality, and the farmers were distinguished in terms of their autonomy, competence, and connectedness to peers, the community, and markets. To support antifragility among regular farmers, adaptations at policy level may be required, for example, by shifting from a top-down toward a bottom-up adaptation and innovation regime where initiative and cooperation are encouraged. With a more autonomous orientation, farmers' intrinsic motivation is expected to increase, enabling transitions at the farm level. In this way, connected systems can be developed which are socioeconomically and biophysically adaptive. When practices, knowledge, and skills are continuously developed, an antifragile system with ample stocks and optionality may evolve over time. [ABSTRACT FROM AUTHOR]

The effect of the spatial resolution of digital terrain models (DTMs) on topography and soil erosion modelling is well documented for low resolutions. Nowadays, the availability of high spatial resolution DTMs from unmanned aerial vehicles (UAVs) opens new horizons for detailed assessment of soil erosion with hydrological models, but the effects of DTM resolution on model outputs at this scale have not been systematically tested. This study combines plot-scale soil erosion measurements, UAV-derived DTMs, and spatially explicit soil erosion modelling to select an appropriate spatial resolution based on allowable loss of information. During 39 precipitation events, sediment and soil samples were collected on five bounded and unbounded plots and four land covers (forest, fallow, maize, and eroded bare land). Additional soil samples were collected across a 220 ha watershed to generate soil maps. Precipitation was collected by two rain gauges and vegetation was mapped. A total of two UAV campaigns over the watershed resulted in a 0.60 m spatial-resolution DTM used for resampling to 1, 2, 4, 8, and 15 m and a multispectral orthomosaic to generate a land cover map. The OpenLISEM model was calibrated at plot level at 1 m resolution and then extended to the watershed level at the different DTM resolutions. Resampling the 1 m DTM to lower resolutions resulted in an overall reduction in slope. This reduction was driven by migration of pixels from higher to lower slope values; its magnitude was proportional to resolution. At the watershed outlet, 1 and 2 m resolution models exhibited the largest hydrograph and sedigraph peaks, total runoff, and soil loss; they proportionally decreased with resolution. Sedigraphs were more sensitive than hydrographs to spatial resolution, particularly at the highest resolutions. The highest-resolution models exhibited a wider range of predicted soil loss due to their larger number of pixels and steeper slopes. The proposed evaluation method was shown to be appropriate and transferable for soil erosion modelling studies, indicating that 4 m resolution (<5 % loss of slope information) was sufficient for describing soil erosion variability at the study site. [ABSTRACT FROM AUTHOR]

We present an assessment of the extent, diversity, and nutritional contribution of the milpa through a quantitative analysis of data from a survey conducted in 989 small scale farm households in the Western Highlands of Guatemala (WHG). The milpa is a traditional agricultural system in which maize is intercropped with other species, such as common beans, faba beans, squashes or potatoes. Our study shows that more than two-thirds of the 1,205 plots recorded were under the milpa system, with a great diversity of crop combinations. As shown with the 357 plots for which specific yields were available, milpa systems present higher total productivity than monocropped maize, expressed as total energy yield of the harvested crops in the respective system, and were also better at providing the recommended daily allowances of fourteen essential nutrients, based on a Potential Nutrient Adequacy (PNA) indicator. Maize-bean-potato, maize-potato, and maize-bean-faba intercrops had the highest PNAs, and monocropped maize, the lowest. These results support the implementation of milpa systems tailored to different agro-ecologies in order to improve nutrition in the WHG and a variety of similar regions. [ABSTRACT FROM AUTHOR]

The effect of spatial resolution of digital terrain models (DTM) on topography and soil erosion modelling is well documented for low resolutions. Nowadays, the availability of high spatial resolution DTM from unmanned aerial vehicles (UAV) opens new horizons for detailed assessment of soil erosion with hydrological models, but effects of DTM resolution on model outputs at this scale have not been systematically tested. This study combines plot scale soil erosion measurements, UAV-derived DTM, and spatially explicit soil erosion modelling to select an appropriate spatial resolution based on allowable loss of information. During 39 precipitation events, sediment and soil samples were collected on five bounded and unbounded plots and four land covers (forest, fallow, maize, and eroded bare land). Additional soil samples were collected across a 220 ha watershed to generate soil maps. Precipitation was collected by two rain gauges and vegetation was mapped. Two UAV campaigns over the watershed resulted in a 0.60 m spatial resolution DTM used for resampling to 1, 2, 4, 8, and 15 m; and a multispectral orthomosaic to generate a land cover map. The OpenLISEM model was calibrated at plot level at 1 m resolution and then extended to the watershed level at the different DTM resolutions. Resampling the 1 m DTM to lower resolutions resulted in an overall reduction of slope. This reduction was driven by migration of pixels from higher to lower slope values; its magnitude was proportional to resolution. At the watershed outlet, 1 and 2 m resolution models exhibited the largest hydrograph and sedigraph peaks, total runoff and soil loss; they proportionally decreased with resolution. Sedigraphs were more sensitive than hydrographs to spatial resolution, particularly at the highest resolutions. The highest resolution models exhibited a wider range of predicted soil loss due to their larger number of pixels and steeper slopes. The proposed evaluation method showed to be appropriate and transferable for soil erosion modelling studies, indicating that 4 m resolution (< 5 % loss of slope information) was sufficient for describing soil erosion variability at the study site. [ABSTRACT FROM AUTHOR]

Agro‐food systems are undergoing rapid innovation in the world and the system's continuum is promoted at different scales with one of the main outcomes to improve nutrition of consumers. Consumer knowledge through educational outreach is important to food and nutrition security and consumer demands guide breeding efforts. Maize is an important part of food systems. It is a staple food and together with rice and wheat, they provide 60% of the world's caloric intake. In addition to being a major contributor to global food and nutrition security, maize forms an important part of the culinary culture in many areas of Africa, the Americas, and Asia. Maize genetics are being exploited to improve human nutrition with the ultimate outcome of improving overall health. By impacting the health of maize consumers, market opportunities will be opened for maize producers with unique genotypes. Although maize is a great source of macronutrients, it is also a source of many micronutrients and phytochemicals purported to confer health benefits. The process of biofortification through traditional plant breeding has increased the protein, provitamin A carotenoid, and zinc contents of maize. The objective of this paper is to review the innovations developed and promoted to improve the nutritional profiles of maize and outcomes of the maize agro‐food system. [ABSTRACT FROM AUTHOR]

Food security is a major challenge in Guatemala, one of the poorest countries in the world. Food insecurity is concentrated in the Western Highlands of Guatemala (WHG) where indigenous communities have been the main victims of social, political and economic marginalization. In this study we characterize the diversity of farming households in the WHG, identify the main sources of food for different types of farm households and assess their food security status through a simple, yet robust, potential food availability indicator. Based on a large and rich dataset of nearly 5000 farm households, our results show the diversity of farming systems in the region, dominated by maize and coffee production, as well as the large differences in their potential food availability. In our model, 52% of farm households in the WHG did not have the means to attain sufficient energy from their agricultural activities. In general, diversified maize-based, coffee-based and specialized coffee farm households had larger proportions of potentially food secure households with 60%, 83% and 74% food secure households, respectively. This contrasted with farm households specialized in maize production and resource-constrained households where there were a greater proportion of households were food insecure. The analytical framework presented here, combining a typology of farm households and their livelihoods with the analysis of their food security status, provides a useful approach for better targeting development interventions towards combating hunger, poverty and malnutrition. [ABSTRACT FROM AUTHOR]

SUMMARY: Understanding farm diversity is essential to delineate recommendation domains for new technologies, but diversity is a subjective concept, and can be described differently depending on the way it is perceived. Historically, new technologies have been targeted primarily based on agro-ecological conditions, largely ignoring socioeconomic conditions. Based on 273 farm households' surveys in Ethiopia, we compare two approaches for the delineation of farm type recommendation domains for crop and livestock technologies: one based on expert knowledge and one based on statistical methods. The expert-based typology used a simple discriminant key for stakeholders in the field to define four farm types based on Tropical Livestock Unit, total cultivated surface and the ratio of these two indicators. This simple key took only a few minutes to make inferences about the potential of adoption of crop and livestock technologies. The PCA-HC analysis included a greater number of variables describing the farm (land use, household size, cattle, fertilizer, off-farm work, hiring labour, production). This analysis emphasized the multi-dimensional potential of such a statistical approach and, in principle, its usefulness to grasp the full complexity of farming systems to identify their needs in crop and livestock technologies. A sub-sampling approach was used to test the impact of data selection on the diversity represented in the statistical approach. Our results show that diversity structure is significantly impacted according to the choice of a sub-sample of 15 of the 20 variables available. This paper shows the complementarity of the two approaches and demonstrates the influence of data selection within large baseline data sets on the total diversity represented in the clusters identified. [ABSTRACT FROM AUTHOR]

We calculated a simple indicator of food availability using data from 93 sites in 17 countries across contrasted agroecologies in sub-Saharan Africa (>13,000 farm households) and analyzed the drivers of variations in food availability. Crop production was the major source of energy, contributing 60% of food availability. The off-farm income contribution to food availability ranged from 12% for households without enough food available (18% of the total sample) to 27% for the 58% of households with sufficient food available. Using only three explanatory variables (household size, number of livestock, and land area), we were able to predict correctly the agricultural determined status of food availability for 72% of the households, but the relationships were strongly influenced by the degree of market access. Our analyses suggest that targeting poverty through improving market access and off-farm opportunities is a better strategy to increase food security than focusing on agricultural production and closing yield gaps. This calls for multisectoral policy harmonization, incentives, and diversification of employment sources rather than a singular focus on agricultural development. Recognizing and understanding diversity among smallholder farm households in sub-Saharan Africa is key for the design of policies that aim to improve food security. [ABSTRACT FROM AUTHOR]