From genes to policy: mission-oriented governance of plant-breeding research and technologies.

Mission-oriented governance of research focuses on inspirational, yet attainable goals and targets the sustainable development goals through innovation pathways. We disentangle its implications for plant breeding research and thus impacting the sustainability transformation of agricultural systems, as it requires improved crop varieties and management practices. Speedy success in plant breeding is vital to lower the use of chemical fertilizers and pesticides, increase crop resilience to climate stresses and reduce postharvest losses. A key question is how this success may come about? So far plant breeding research has ignored wider social systems feedbacks, but governance also failed to deliver a set of systemic breeding goals providing directionality and organization to research policy of the same. To address these challenges, we propose a heuristic illustrating the core elements needed for governing plant breeding research: Genetics, Environment, Management and Social system (GxExMxS) are the core elements for defining directions for future breeding. We illustrate this based on historic cases in context of current developments in plant phenotyping technologies and derive implications for governing research infrastructures and breeding programs. As part of mission-oriented governance we deem long-term investments into human resources and experimental set-ups for agricultural systems necessary to ensure a symbiotic relationship for private and public breeding actors and recommend fostering collaboration between social and natural sciences for working towards transdisciplinary collaboration.

How much can farmers pay for weeding robots? A Monte Carlo simulation study.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-023-10015-x.

COVID-19 in rural Africa: Food access disruptions, food insecurity and coping strategies in Kenya, Namibia, and Tanzania.

This study assesses the extent of COVID-19-related food insecurity in Kenya, Tanzania, and Namibia. Using the Household Food Insecurity Access Scale, we measure food insecurity in various dimensions and document several food access disruptions associated with the COVID-19 pandemic between April and July 2020. Furthermore, we assess the association of COVID-19 countermeasures with the adoption of various strategies in line with the coping strategies index. We rely on a unique phone survey that followed households who participated in an earlier field-based survey. First, through Ordinary Least-Squares and Probit regressions, we show a strong and statistically significant association between COVID-19 countermeasures and food access disruptions and food insecurity in each of the three countries. We then use a multivariate probit regression model to understand the use of the various coping strategies, including reducing food intake, increasing food search, and relying more on less nutritious foods. We provide evidence on the complementarities and trade-offs in using these coping strategies. COVID-19 and related lockdown measures coincided with a deleterious increase in food insecurity in rural Africa.

Action on Invasive Species: Control Strategies of Parthenium hysterophorus L. on Smallholder Farms in Kenya.

Parthenium hysterophorus L. (Asteraceae) is an invasive alien weed with detrimental effects on agricultural production, biodiversity, human and animal health, threating rural livelihoods in Asia and Africa. The problem emerged recently in the Kenyan Rift Valley, where it began to affect the landholdings of both agro-pastoralists and crop farmers. These vulnerable smallholders depend heavily on natural resources for their livelihoods. In this study, we assessed the severity of parthenium invasion and farmers' management responses using a sample of 530 agro-pastoralists in Baringo County, Kenya, in 2019. We hypothesise that the implementation of existing management strategies depends on the state of parthenium invasion and household socio-economic characteristics. The prevalence and severity of parthenium invasion differed greatly among field plots. To control weeds, farmers resort to either hand weeding, the use of synthetic herbicides, or intensive tillage, sometimes in combination with mulching. A multivariate probit regression model shows that households' characteristics determine the type of control strategies used as well as their complementarity and substitutability. Hand weeding is the most common option, adopted by almost 40% of farmers. The use of agrochemicals or soil-based control strategies appears to be related to knowledge and information characteristics such as access to extension services, membership in organisations and the educational level of household heads. While hand weeding and the use of synthetic herbicides depict significant substitutability, the latter strategy is limited to a few larger farms with market-oriented production. As parthenium invasion continues, policies need to improve farmer awareness and access to knowledge to enable pro-poor and environmentally sustainable control of parthenium on smallholder farms.

Integration of various dimensions in food-based dietary guidelines via mathematical approaches: report of a DGE/FENS Workshop in Bonn, Germany, 23-24 September 2019.

In the past, food-based dietary guidelines (FBDGs) were derived nearly exclusively by using systematic reviews on diet-health relationships and translating dietary reference values for nutrient intake into foods. This approach neglects many other implications that dietary recommendations have on society, the economy and environment. In view of pressing challenges, such as climate change and the rising burden of diet-related diseases, the simultaneous integration of evidence-based findings from different dimensions into FBDGs is required. Consequently, mathematical methods and data processing are evolving as powerful tools in nutritional sciences. The possibilities and reasons for the derivation of FBDGs via mathematical approaches were the subject of a joint workshop hosted by the German Nutrition Society (DGE) and the Federation of European Nutrition Societies (FENS) in September 2019 in Bonn, Germany. European scientists were invited to discuss and exchange on the topics of mathematical optimisation for the development of FBDGs and different approaches to integrate various dimensions into FBDGs. We concluded that mathematical optimisation is a suitable tool to formulate FBDGs finding trade-offs between conflicting goals and taking several dimensions into account. We identified a lack of evidence for the extent to which constraints and weights for different dimensions are set and the challenge to compile diverse data that suit the demands of optimisation models. We also found that individualisation via mathematical optimisation is one perspective of FBDGs to increase consumer acceptance, but the application of mathematical optimisation for population-based and individual FBDGs requires more experience and evaluation for further improvements.

Estimating irrigation water demand in the Moroccan Drâa Valley using contingent valuation.

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.

A generic bio-economic farm model for environmental and economic assessment of agricultural systems.

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.