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Pastoral and agro-pastoral (PAP) systems in East Africa face a range of challenges including increased climate variability. Various measures have been proposed to improve the resilience of pastoral/agro-pastoral (PAP) systems to drought. However, identifying the most effective measure for a given system and location is complicated, and tools are required to appraise measures on a consistent basis. This paper develops a model of a PAP system and uses it to assess the effects of four measures (Index-based livestock insurance, IBLI; Commercial destocking with an early warning system, EWS; Rangeland restoration, RR; Fodder planting, FP) on the resilience of the PAP system. It also quantifies the greenhouse gas (GHG) effects of the measures, thereby identifying potential trade-offs and synergies between the policy objectives of resilience and climate smart agriculture (CSA). A dynamic model of the Borena pastoral cattle system was developed to undertake the analysis. At its core is a herd model that calculates the changes in cattle population over time. Feed availability and drought occurrence affect fertility and mortality rates, which in turn determine the population and (meat and milk) production. A suite of indicators covering the three dimensions of CSA (increasing productivity, enhancing resilience and reducing GHG emissions) were developed, and used to compare the situation with and without measures. Destocking with an early warning system provides the biggest increases (relative to the no measure situation) in production and profit, due to the way it changes the herd size and structure. It maintains a larger herd than any of the other measures, and a greater proportion of the herd are adult females. Fodder planting and rangeland restoration provide moderate increases in production and profit. Index-based livestock insurance provides a moderate increase in protein production, but has no effect on profit, as it is designed to reduce risk rather than increase productivity or profit, at least in the short term. All of the measures increase the total emissions relative to the no measure scenario. In terms of the three dimensions of climate-smart agriculture, IBLI leads to some improvements in productivity and resilience but leads to large increases in total emissions, and modest increases in emissions intensity (EI). EWS leads to large increases in productivity and resilience. However, it also leads to large increases in total emissions and a mixed effect on EI. FP and RR improve productivity and increase total emissions, while having little effect on EI or resilience. This paper illustrates the way in which systems dynamic model can be used to appraise measures designed to improve resilience. The result identify potential synergies and tensions between the goals of resilience and climate smart agriculture, and raises the question of whether fully climate-smart goals are viable in these systems. [Display omitted] • A model of a pastoral cattle system in Ethiopia was developed and used to investigate resilience. • Four measures were analysed: livestock insurance; managed destocking; rangeland restoration; fodder planting. • Destocking provides the biggest increase in production and profit, due to the way it changes the herd size and structure. • Fodder planting and rangeland restoration increase production and profit. Insurance increases production but not profit. • All of the measures increase the total GHG emissions but lead to little change in emissions intensity. [ABSTRACT FROM AUTHOR]

Rangeland productivity is strongly conditioned by the amount and temporal distribution of precipitation. Thus, the worsening of droughts with climate change could be a serious threat to their existence. This paper presents a modelling study aimed at evaluating the sensitivity of a valuable type of commercial rangelands, namely Spanish dehesas, to increases in the frequency and intensity of droughts driven by climate change. The assessment consisted in a multi-way ANOVA carried out on the basis of 5400 simulations of a multidisciplinary integrated model. It included two blocking factors linked to climate change scenarios, namely Representative Concentration Pathway and downscaling method, and two treatment factors, namely return period and severity of droughts. The levels of all factors were included as part of the simulation scenarios. The response variables constituted a summary of model's behaviour throughout one simulation. They were average profits per farmer and average stocking rate, both calculated over the entire simulation period, and remaining soil depth at the end of the simulation. The effects of the treatment factors on the response variables were small for all blocks, thereby suggesting that the sensitivity, and thus the vulnerability, of Spanish dehesas to the worsening of droughts would be low under climate change. Farmers were defined as conservative in all model simulations, that is, they minimized changes in the size of their herds and bought supplementary feed to meet shortfalls in livestock feed unless it was excessively expensive. Thus, we conclude that this group strategy could explain the adaptive capacity of Spanish dehesas to droughts. This paper shows that multidisciplinary integrated models are valuable learning tools to acquire insights into the relationships between climate, ecologic and socio-economic factors. Although there is a recurrent call for holistic studies, they are still rare in the rangeland literature. Hopefully, this paper will motivate some researchers to consider this approach. Unlabelled Image • Climate change heralds the worsening of droughts threatening rangeland sustainability. • Modelling assessment of the sensitivity of a dehesa rangelands to increases in the frequency and intensity of droughts. • The sensitivity, and thus the vulnerability, of dehesas to the worsening of droughts would be low. • A widespread conservative strategy would provide Mediterranean commercial rangelands with adaptive capacity to droughts. [ABSTRACT FROM AUTHOR]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]

Significant seasonal weather changes can create uncertainty in cattle markets due to resulting decreased cattle performance, increased animal mortality rates, and elevated production costs. Significant fluctuations in the number of beef cow inventories are a result of market conditions and weather changes. In addition, changes in industry structure, forage availability, and cost of transporting animals versus forage have caused geographic movement of the cow-calf sector in the United States. Cows are usually pasture-raised year-round, thus fulfilling nearly two-thirds of their forage requirements. Changes in weather, however, can directly affect forage quantity and quality, potentially directly impacting animal growth through feed intake. Because weather impacts on cow-calf production in the United States has received minimal research attention, this paper provides new empirical evidence of weather effects on location and production of the cow-calf industry. An econometric model of location and production is developed to examine the impact of temperature and total precipitation changes for each season, and economic factors on the spatial structure and geographical location of cow-calf production in 25 major cow-calf producing states in the U.S. The results show that seasonal temperatures and total seasonal precipitation significantly impact county-level beef cow inventories and operational locations. The results also reveal significant spatial patterns relative to positive correlations of beef cow inventories across counties. Changes in production resulting from weather changes ultimately affect operational profits. Knowledge regarding the economic impacts of increases in seasonal average temperature and precipitation variability on cow-calf production is needed by producers to develop risk-mitigating practices and production planning. Significant spatial patterns are also important for sector-specific development and concentration because understanding the importance of the spatial location of cow-calf operations may help policymakers formulate effective policies to facilitate sector development. [Display omitted] • Seasonal weather changes create complexity for cow-calf producers trying to mitigate economic losses • The current study examines weather impacts on the cow-calf production, including spatial interactions • Growing season temperature has the highest impact on beef cow inventories through influences of pasture growth • Positive spillover effects among cow-calf operations in neighboring counties • An understanding of seasonal weather impacts is important for better production planning [ABSTRACT FROM AUTHOR]

Family-run cow-calf farms based on native grasslands exhibit low economic and social sustainability, as reflected in low family incomes and high workloads. Experimental results have shown that pasture–herd interaction management could improve native grasslands and animal productivity. This paper analyzes the extent to which the sustainability of family-run livestock farms based on native grasslands could be enhanced by a systemic redesign informed by ecological intensification practices. The research questions address the initial state of farm sustainability, key bottlenecks to improving farm sustainability, and changes in sustainability criteria achieved over three years of farm redesign. The study was executed as part of a multi-level co-innovation project in Uruguay in which a team of scientist-practitioners and seven farm families participated in farm characterization, diagnosis, and redesign. The farm characterization took the form of indicators to describe the farms' management and bio-physical subsystems. Redesign plans were negotiated between the research team and the farmers. Frequent monitoring and evaluation cycles enabled finetuning across the years of implementation. Improvements were observed in the economic indicators gross margin (+55%), return to labor (+71%), and family income (+53%) and in the social indicator workload (−22%), and the environmental indicators bird diversity and ecosystem integrity index were maintained or increased slightly. These changes were explained by the uptake of coherent sets of ecological intensification practices causing changes in forage height (+30%), forage allowance (+69%), pregnancy (+22), weight of weaning calf per mating cow (+32%), and presence of tussocks (+65%). Ecological intensification principles resulted in synergistic positive effects between productivity–biodiversity tradeoffs and the scope for enhanced farm resilience and stability. Cow-calf family-run farms can be transformed to produce positive environmental and social effects and viable economic results. The implementation of projects in a co-innovation context may be taken as a guide to scaling up and scaling out the ecological intensification of livestock production on native grasslands, contributing to an extension system at the national level with the aim of improving cow-calf systems sustainability. [Display omitted] • Seven commercial farms were redesigned over three years of co-innovation. • Ecological intensification improved economic, environmental, and social indicators. • Spatio-temporal grazing management resulted in enhanced meat productivity. • Farmers highlighted the different way of working with an advisor. • Changes in performance of the participant farms exceeded national trends. [ABSTRACT FROM AUTHOR]