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Agriculture faces several challenges including climate change and biodiversity loss while, at the same time, the demand for food, feed, biofuels, and fiber is increasing. Sustainable intensification aims to increase productivity and input-use efficiency while enhancing the resilience of agricultural systems to adverse environmental conditions through improved management and technology. Recent advances in sensing, machine learning, modeling, and robotics offer opportunities for novel smart digital technologies to enable sustainable intensification. However, developing smart digital technologies and putting them into agricultural practice, requires closing major research gaps, related in particular to (1) the utilization of multi-scale multi-sensor monitoring in space and time, (2) using artificial intelligence for linking process and data-driven methods, (3) improving decision making and intervention in plant production, and finally (4) modeling conditions and consequences of farmers acceptance. Closing these gaps requires an interdisciplinary approach. Here, we present a research agenda and steps forward to steer research efforts, highlighting research priorities, and identifying required interdisciplinary research collaboration. Following this agenda will leverage the full potential of smart digital technologies for sustainable crop production. [Display omitted] • Research gaps hinder smart digital technologies to enhance sustainable agriculture. • We present an interdisciplinary research agenda required to close these gaps. • Examples illustrate why closing these research gaps is important for sustainability. • The proposed research agenda helps steering research efforts and collaboration. [ABSTRACT FROM AUTHOR]

Irrigation water management is crucial for agricultural production and livelihood security in Morocco as in many other parts of the world. For the implementation of an effective water management, knowledge about farmers’ demand for irrigation water is crucial to assess reactions to water pricing policy, to establish a cost-benefit analysis of water supply investments or to determine the optimal water allocation between different users. Previously used econometric methods providing this information often have prohibitive data requirements. In this paper, the Contingent Valuation Method (CVM) is adjusted to derive a demand function for irrigation water along farmers’ willingness to pay for one additional unit of surface water or groundwater. An application in the Middle Drâa Valley in Morocco shows that the method provides reasonable results in an environment with limited data availability. For analysing the censored survey data, the Least Absolute Deviation estimator was found to be a more suitable alternative to the Tobit model as errors are heteroscedastic and non-normally distributed. The adjusted CVM to derive demand functions is especially attractive for water scarce countries under limited data availability. [Copyright &y& Elsevier]

Income comparisons between farm and non‐farm households play a crucial role in many aspects of farm policy. Using household income data from tax returns of all Norwegian taxpayers in the period 2006–2015 we study these income differences. We find that the unconditional mean income is higher for farm households, but with important differences depending on the comparison group considered. We also find that the income difference is reduced when we control for differences in the personal characteristics of the different non‐farm comparison sub‐groups. This finding implies that income comparison using unconditional means, as frequently done in agricultural policy making, is potentially misleading. We also show that the income effect of personal characteristics is not the same for different comparison sub‐groups, as has been assumed in previous studies of income disparities. Differences in personal characteristics, and the income effect of those characteristics, therefore need to be accounted for if income comparisons between farmers and non‐farmers are to inform farm support policies. [ABSTRACT FROM AUTHOR]

Fruit tree–based agroforestry has been promoted as an alternative farming practice in upland Northwest Vietnam to replace monocultures of staple crops. Although many studies have focused on evaluating the performance of agroforestry systems at the plot level, research on how farmers perceive and evaluate agroforestry considering whole-farm contexts is limited. We explored the perceptions and reasoned management decisions of agroforestry farmers to uncover challenges that hinder the wider use of agroforestry, and we assessed farmers' strategies for effective management of adoption challenges. We combined the Q methodology and the systems thinking approach. With the Q methodology, we explored prevalent discourses among the members of the farming community on the impact of agroforestry. Systems thinking elucidated a system-wide understanding of farmers' adaptive decision-making processes. By combining the two approaches, we uncovered the dynamics that shape farmers' perceptions and the rationale behind their management of the adoption process. Through the Q method, we identified three distinct discourses among participants. Two of these discourses are in favor of agroforestry, highlighting its beneficial impacts on livelihoods and the environment, e.g., through diversification of household income and through soil erosion control. We also generated a collective development pathway outlining how farmers navigated and adapted agroforestry practices to overcome adoption challenges through a whole-system approach to farm resource management. We identified structural barriers, such as unstable farm-gate prices, that may need high-level interventions. Our study adds a new dimension to the assessment of agroforestry through farmers' perspectives and contributes to the existing body of research on knowledge systems in agroforestry. Considering farmers' views and their ways of reasoning during innovation processes may allow tailoring appropriate innovations by accounting for unique farm situations and local farming systems. Such locally generated knowledge will have relevance for real-world contexts and therefore be useful for guiding actions. [ABSTRACT FROM AUTHOR]

Technological change co‐determines agri‐environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm‐level models that are rich in technology details and environmental indicators, integrated with agent‐based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade‐offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi‐directional Long Short Term Memory. [ABSTRACT FROM AUTHOR]

This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot's ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs. [ABSTRACT FROM AUTHOR]

Resilience has either been assessed on system or individual scale so far. Ignoring the other scale may potentially change the interpretation of resilience in socio-ecological systems (SES). Thus, this paper argues that the co-evolution of both resiliencies must be studied to capture multi-scale complexity. We attempt to close this gap by assessing resilience at both scales of a village community in Thaba Nchu, South Africa. Villagers use a commonly managed rangeland for beef cattle production. An agent based model of household interaction coupled with a biophysical model of the rangeland measures the resiliencies of the SES towards a shock, a stress and a policy intervention. Currently, the SES remains in a stable attractor in terms of SES resilience. Household resilience, however, degrades in a process of structural change. A drought scenario shows improved SES resilience but structural change at household level accelerated. An increase in the number absentee herders increases the likelihood for SES collapse by eroding social embededdness. Finally, an introduced basic income grant demonstrates that the SES is able to cope with an increased number of appropriators. However, interaction of the policy intervention with an exogenous stress translates into an increased probability of SES decoupling. [ABSTRACT FROM AUTHOR]

We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations. • We analyse the permanence of land use practices induced by a PES program in Costa Rica • We use empirical long-term time series data combining household surveys and ecosystem assessments on farm level • Land use changes were not reverted, nor expanded, after payments have ended • We found a convergence pattern of adoption behaviour among farmers • Next to payments, social ties and group membership correlate with long term adoption [ABSTRACT FROM AUTHOR]