Climate Science, Development Practice, and Policy Interactions in Dryland Agroecological Systems

The literature on drought, livelihoods, and poverty suggests that dryland residents are especially vulnerable to climate change. However, assessing this vulnerability and sharing lessons between dryland communities on how to reduce vulnerability has proven difficult because of multiple definitions of vulnerability, complexities in quantification, and the temporal and spatial variability inherent in dryland agroecological systems. In this closing editorial, we review how we have addressed these challenges through a series of structured, multiscale, and interdisciplinary vulnerability assessment case studies from drylands in West Africa, southern Africa, Mediterranean Europe, Asia, and Latin America. These case studies adopt a common vulnerability framework but employ different approaches to measuring and assessing vulnerability. By comparing methods and results across these cases, we draw out the following key lessons: (1) Our studies show the utility of using consistent conceptual frameworks for vulnerability assessments even when quite different methodological approaches are taken; (2) Utilizing narratives and scenarios to capture the dynamics of dryland agroecological systems shows that vulnerability to climate change may depend more on access to financial, political, and institutional assets than to exposure to environmental change; (3) Our analysis shows that although the results of quantitative models seem authoritative, they may be treated too literally as predictions of the future by policy makers looking for evidence to support different strategies. In conclusion, we acknowledge there is a healthy tension between bottom-up/ qualitative/place-based approaches and top-down/quantitative/generalizable approaches, and we encourage researchers from different disciplines with different disciplinary languages, to talk, collaborate, and engage effectively with each other and with stakeholders at all levels

Simulation of daily field management and crop performance in Southwest Germany under climate and technological change

The work performed in the course of this dissertation has been to define a systematic agricultural management response to environmental and economic conditions that is functional under hypothetical scenarios, especially involving climatic forecasts into the future. This was done through the use of the FARMACTOR/Expert-N coupled modelling framework that links agent-based management parameters with crop growth simulation, as the two are strongly interconnected. Starting with the completed FARMACTOR framework that had yet to be thoroughly tested, this work involved the verification of the modelling procedure, population of appropriate data resources for calibration and application, and the presentation of simulation experiments in peer-reviewed publication. The innovative linkage of agent-based management with biophysical simulation has led to FARMACTOR becoming a reference for international research on integrated economic/ecological study, impacting the scientific community through its unique contribution to analysis of anthropogenic landscape systems. FARMACTOR, as adapted in the course of this dissertation, has presented concepts that add to the robustness with which agroecosystem simulation is conducted on field and regional scale. Breaking away from the convention of static management input into crop models is an important step in this regard. Especially under scenarios of future climate change, dynamic field management lends to the plausibility of projected crop performance. If simulation modelling is to be an important tool in efforts to mitigate and/or adapt to climate change, elements such as dynamic management may be indispensable components of modelling frameworks. The impact of management has too great of an influence on agroecosystem functioning to be ignored. The effort in the course of this dissertation to systematically account for the likewise crucial factor of subspecies genetic variation is also an early example of improving agroecosystem simulation. As of the commencement of this work, agricultural species were, for the most part, simulated as just that, a species, when the variance of growth process within a species is a fundamental component of agronomy. Cultivar choice is one of the most important tools available to agricultural practitioners in terms of regional/localized agriculture. At least the simulation of multiple cultivars, or agricultural subspecies, is necessary to capture the heterogeneous responses to identical environmental conditions. This work has presented a sound methodology to account for breeding progress, based on observed trends in crop phenotypes, while also demonstrating a methodology for comparing results of the regional simulation of multiple cultivars. Spatial or temporal adaptation to climate is mandatory in terms of agricultural-sector profitability and food security, from local to global scales. Simulation modelling could eventually prove to be a useful tool in predicting the suitability of different crops or cultivars for unique biomes, whether in terms of agricultural intensification, producing more on a fixed land area, or expanding production into new areas. Simulation will most likely prove be an effective alternative to resource-intensive field trials, at the very least the two are complementary. This dissertation has, in part, demonstrated the potential for utilizing field experiments, to varying degrees of specificity, through model parameter optimization procedures, to produce local and regional projections of crop performance and adaptive measures likely to be undertaken by farmers. A statistical model developed alongside, and sharing the principals of environmental planting triggers incorporated in the agent-based model, was used to define a predictive model for maize planting dates throughout Germany. The two models achieved comparable accuracy, while differing in their advantages and drawbacks. The statistical model is not associated with a complete set of economic and biophysical attributes that can both be drivers of the bioeconomic model and informative outputs. Its advantage lies in its simplicity in regional applicability, able to predict (or project, if using future simulated weather), planting dates throughout the whole of Germany. The yield component of the statistical model demonstrates that the date of planting is a stronger driver of yields than the weather during the weeks that influence planting dates. Because maize is planted in spring, on bare fields, as opposed to wheat and other fall crops planted following the harvest of a previous crop, the statistical model is not as effective in predicting fall planting dates as FARMACTOR which can accurately simulate the harvest date of a crop preceding fall sowing.Der Klimawandel stellt ein dauerhaftes Herausforderung für die Agrarwirtschaft dar. Das steigende wissenschaftliche Interesse an landwirtschaftlicher Produktivität unter veränderten Umweltbedingungen ist zielführend für diese Arbeit. Die Modellierung umwelt- und ökonomiebedingter Anpassungen landwirtschaftlicher Feldarbeiten ist eine Methode um dieser Fragestellung zu begegnen. Diese Modellierung kann unter hypothetischen Szenarien und insbesondere für Prognosen zukünftiger Klimaauswirkungen genutzt werden. Hierzu wurde das gekoppelte Modellsystem Farmactor/Expert-N verwendet, das die beiden interagierenden Bereiche des agentebasierten Managements und das Pflanzenwachstum miteinander verbindet. Beginnend mit dem FARMACTOR Modell, beinhaltet diese Dissertation eine Überprüfung der Modellfunktion, die Diskussion geeigneter Datenressourcen für die Kalibrierung und Anwendung, sowie die Präsentation der Ergebnisse von Simulationsexperimenten. Letztere wurden in peer-review Publikationen veröffentlicht. Durch die innovative Verbindung von agentenbasierten Management-Parametern und biophysikalischer Simulation ist FARMACTOR zu einer internationalen Referenz in der Forschung von integrierten ökonomischen / ökologischen Studien geworden und findet Berücksichtigung im wissenschaftlichen Diskurs zur Analyse anthropogener Landschaftssysteme. Die Anwendung von FARMACTOR im Rahmen dieser Arbeit trägt wesentlich zur Erhöhung der Plausibilität von Agroökosystemsimulationen auf dem Feld und auf regionaler Ebene bei. Die Abwendung von der Annahme des statischen Managements in Modellierungssystemen ist dabei ein wichtiger Schritt. Gerade unter Szenarien zukünftiger Klimaänderungen steigt die Plausibilität der projizierten Erntemengen und anderer simulierter Leistungen durch die Annahme dynamischer Managementmethoden. Bei dem Einsatz der Simulationsmodellierung zur Anpassung an den Klimawandel sind Elemente wie das dynamische Feldmanagement daher unverzichtbare Komponenten von Modellierungssystemen. Räumliche und zeitliche Anpassung an das Klima sind notwendig in Bezug auf die Rentabilität des landwirtschaftlichen Sektors und für den Erhalt der Ernährungssicherheit auf lokaler sowie globaler Ebene. Simulationen können in der Vorhersage der Eignung verschiedener Managementverfahren, Kulturen und Sorten ein nützliches Werkzeug sein, ob im Hinblick auf die Intensivierung der Landwirtschaft - mehr auf bestehender Fläche zu produzieren - oder im Hinblick auf die Erschließung neuer Anbaugebiete. Der Einsatz von Simulationen kann eine wirksame Alternative zu ressourcenintensiven Feldversuchen darstellen oder zumindest komplementär zu diesen eingesetzt zu werden. Diese Dissertation hat das Potenzial der Verwendung von Feldversuchsdaten in Modellparameteroptimierungsverfahren aufgezeigt, um lokale und regionale Projektionen der Ernteleistung und Anpassungsmaßnahmen zu erstellen. Zeitgleich wurde ein statistisches Modell entwickelt, um eine Vorhersage für Maisaussattermine in Deutschland zu erstellen. Die beiden in der Arbeit verwendeten Modelle erreichen eine vergleichbare Genauigkeit, während sie sich in ihren Vor- und Nachteilen unterscheiden. Das statistische Modell ist nicht mit einem kompletten Satz von wirtschaftlichen und biophysikalischen Eigenschaften ausgestattet, die sowohl Input als auch Output des bioökonomischen Modells sein können. Der Vorteil des statistischen Modells liegt in seiner Einfachheit und in der regionalen Anwendbarkeit, Aussaattermine vorherzusagen (oder zu projizieren). Die Ertragskomponente des statistischen Modells zeigt unter anderem, dass der Aussaattermin ein stärkerer Treiber für Erträge ist, als das Wetter während der Wochen, die die Aussaattermine beeinflussen. Mais und andere Sommerkulturen werden hauptsächlich auf kahlem Boden ausgesät, im Vergleich zu Winterkulturen wie Weizen, dessen Aussaat stark von der Vorkultur abhängig ist. Daher ist das statistische Modell hier nicht vergleichbar effektiv. Das bioökonomische Modell hingegen hat den Vorteil, mit Einbeziehung von Fruchtfolge und zuverlässiger Simulation von Ernteterminen, die Herbstaussaat zuverlässiger zu treffen

Facing up to the paradigm of ecological intensification in agronomy: Revisiting methods, concepts and knowledge

International audienceAgriculture is facing up to an increasing number of challenges, including the need to ensure various ecosystem services and to resolve apparent conflicts between them. One of the ways forward for agriculture currently being debated is a set of principles grouped together under the umbrella term “ecological intensification”. In published studies, ecological intensification has generally been considered to be based essentially on the use of biological regulation to manage agroecosystems, at field, farm and landscape scales. We propose here five additional avenues that agronomic research could follow to strengthen the ecological intensification of current farming systems. We begin by assuming that progress in plant sciences over the last two decades provides new insight of potential use to agronomists. Potentially useful new developments in plant science include advances in the fields of energy conversion by plants, nitrogen use efficiency and defence mechanisms against pests. We then suggest that natural ecosystems may also provide sources of inspiration for cropping system design, in terms of their structure and function on the one hand, and farmers’ knowledge on the other. Natural ecosystems display a number of interesting properties that could be incorporated into agroecosystems. We discuss the value and limitations of attempting to 'mimic' their structure and function, while considering the differences in objectives and constraints between these two types of system. Farmers develop extensive knowledge of the systems they manage. We discuss ways in which this knowledge could be combined with, or fed into scientific knowledge and innovation, and the extent to which this is likely to be possible. The two remaining avenues concern methods. We suggest that agronomists make more use of meta-analysis and comparative system studies, these two types of methods being commonly used in other disciplines but barely used in agronomy. Meta-analysis would make it possible to quantify variations of cropping system performances in interaction with soil and climate conditions more accurately across environments and socio-economic contexts. Comparative analysis would help to identify the structural characteristics of cropping and farming systems underlying properties of interest. Such analysis can be performed with sets of performance indicators and methods borrowed from ecology for analyses of the structure and organisation of these systems. These five approaches should make it possible to deepen our knowledge of agroecosystems for action

Integrative framework and methods for coastal area management: proceedings

Coastal zone management, ASEAN,

Building Ownership, Renovation Investments, and Energy Performance—A Study of Multi-Family Dwellings in Gothenburg

The European building stock was renewed at a rapid pace during the period 1950–1975. In many European countries, the building stock from this time needs to be renovated, and there are opportunities to introduce energy efficiency measures in the renovation process. information availability and increasingly available analysis tools make it possible to assess the impact of policy and regulation. This article describes methods developed for analyzing investments in renovation and energy performance based on building ownership and inhabitant socio-economic information developed for Swedish authorities, to be used for the Swedish national renovations strategy in 2019. This was done by analyzing measured energy usage and renovation investments made during the last 30 years, coupled with building specific official information of buildings and resident area characteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses show that more costly renovations lead to decreasing energy usage for heating, but buildings that have been renovated during the last decades have a higher energy usage when accounting for current heating system, ownership, and resident socio-economic background. It is appropriate to include an affordability aspect in larger renovation projects since economically disadvantaged groups are over-represented in buildings with poorer energy performance

Monitorización y Evaluación Participativa en Agricultura Regenerativa: Del conocimiento y los impactos locales a la adopción a gran escala

The advanced state of land degradation affecting more than 3,200 million people worldwide have raised great international concern regarding the sustainability of socio-ecological systems, urging the large-scale adoption of contextualized sustainable land management. The agricultural industrial model is a major cause of land degradation due to the promotion of unsustainable management practices that deteriorate the quality of soils compromising their capacity to function and deliver ecosystem services. The consequences derived from land degradation are especially devastating in semi-arid regions prone to desertification, where rainfall scarcity and irregularity intensifies crop failure risks and resource degradation, compromising the long term sustainability of these regions. Regenerative agriculture (RA) has recently gained increasing recognition as a plausible solution to restore degraded agroecosystems worldwide. RA is a farming approach foreseen to reverse land degradation, increase biodiversity, boost production and enhance the delivery of multiple ecosystem services by following a series of soil quality restoration principles and practices. Despite its promising benefits, RA has been limitedly adopted in semiarid regions. Major reasons explaining this seemingly incongruous mismatch are the scarce and contrasting empirical data proving its effectiveness, top-down research approaches and lack of farmer involvement in agroecosystem restoration projects and decision-making, and the generally slow response of soils to management changes in semiarid regions, which may delay the appearance of visible results discouraging farmers from adopting RA. In the high steppe plateau of southeast Spain, an on-going process of large-scale landscape restoration through adoption of regenerative agriculture was initiated in 2015. The high steppe plateau is one of the European regions most affected by land degradation and desertification processes and represents one of the world´s largest areas for the production of rainfed organic almonds. In 2015, local farmers created the AlVelAl association with the support of the Commonland Foundation, business entrepreneurs, regional governments, and research institutions, and started to apply RA at their farms. The objective was to restore vast extensions of degraded land for increasing the productivity and biodiversity of their agroecosystems, increasing the resilience to climate change, generating job opportunities and enhancing social cohesion in the region, in a time frame of 20 years following Commonlands´ 4-Returns approach. However, the limited empirical information supporting RA effectiveness, the lack of reference examples in the region, and the slowness with which visible ecological restoration processes usually occur in semiarid regions were considered major obstacles hindering RA adoption in the region. To effectively address this knowledge gap, support farmers and expedite RA adoption, this research proposed horizontal research fostering the creation of learning communities between farmers and researchers, putting together local and scientific knowledge to improve the understanding of RA. This thesis presents a participatory monitoring and evaluation research (PM&E) applying a combination of social and ecological methods to evaluate the potential of PM&E to enhance knowledge exchange between farmers and researchers on Regenerative Agriculture in the context of the high steppe plateau. The aim of this thesis is twofold: on one hand, to increase the understanding on RA impacts, on the other hand, to evaluate the potential contribution of PM&E to enable social learning and contribute to the adaptation and long term adoption of RA in the high steppe plateau and semiarid regions in general. To facilitate PM&E of the impacts of sustainable land management and agricultural innovations like RA, Chapter 2 presents a participatory methodological framework that guides the identification and selection of technical and local indicators of soil quality, generating a monitoring system of soil quality for PM&E by farmers and researchers. The methodological framework includes the development of a visual soil assessment tool integrating local indicators of soil quality for farmers´ monitoring. The framework consists of 7 phases: 1) Definition of research and monitoring objectives; 2) Identification, selection and prioritization of Technical Indicators of Soil Quality (TISQ); 3) Identification, selection and prioritization of Local Indicators of Soil Quality; 4) Development of a visual soil assessment tool integrating LISQ; 5) Testing and validation of the visual soil evaluation tool; 6) Monitoring and assessment of sustainable land management impacts by researchers and farmers using TISQ and the visual soil evaluation tool respectively and; 7) Exchange of monitoring results between all involved participants, and joint evaluation of impacts. To facilitate PM&E of RA in the steppe highlands, Phases 1 to 5 were applied through a series of participatory methods including a first meeting with AlVelAl board members for the definition of research objectives, farm visits, participatory workshops, and conducting formal and informal interviews, among others. Technical indicators of soil quality were identified, selected and prioritized by researchers through an extensive literature review and ad-hoc expert consultation with expertise in soil quality assessment and monitoring. Local indicators of soil quality were identified, selected, prioritized and validated by farmers in two participatory workshops. The co-developed visual soil assessment tool, named the farmer manual, was tested and validated during the second workshop. Local indicators selected by farmers focused mostly on supporting, regulating and provisioning ecosystem services including water regulation, erosion control, soil fertility and crop performance. Technical indicators selected by researchers focused mostly on soil properties including aggregate stability, soil nutrients, microbial biomass and activity, and leaf nutrients, covering crucial supporting services. The combination of local and technical indicators provided complementary information, improving the coverage and feasibility of RA impact assessment, compared to using technical or local indicators alone. The methodological framework developed in this chapter facilitated the identification and selection of local and technical indicators of soil quality to generate relevant monitoring systems and visual soil assessment tools adapted to local contexts, thus improving knowledge exchange and mutual learning between farmers and researchers to support the implementation of RA and optimize the provision of ecosystem services. Implementation of RA usually happens gradually due to socioeconomic, informational, practical, environmental and political constraints Thus, RA adoption by farmers, in practice, translates into different combinations of RA practices, with a diversity of management, based on farmer capabilities, environmental conditions, and expected restoration results. To help the design, adoption and implementation of most effective RA practices to optimize the restoration of agroecosystems, Chapter 3 presents the impacts of the different combinations of RA practices implemented by participating farmers on crucial soil quality and crop performance indicators using previously selected technical indicators of soil quality over a period of 2 years. This chapter corresponds to the application of phase 6 of the methodological framework developed in Chapter 2. RA impacts were assessed in 9 farms on one field with regenerative management and one nearby field with conventional management based on frequent tillage, that were selected together with farmers. Fields were clustered under regenerative management based on the RA practices applied and distinguished 4 types of RA treatments: 1) reduced tillage with green manure (GM), 2) reduced tillage with organic amendments (OA), 3) reduced tillage with green manure and organic amendments (GM&OA), and 4) no tillage with permanent natural covers and organic amendments (NT&OA). The impacts of RA compared to conventional management were evaluated by comparing physical (bulk density and aggregate stability), chemical (pH, salinity, total N, P, K, available P, and exchangeable cations) and biological (SOC, POC, PON, microbial activity) properties of soil quality, and the nutritional status of almond trees (leaf N, P and K). Our results show that GM improved soil physical properties, presenting higher soil aggregate stability. We found that OA improved most soil chemical and biological properties, showing higher contents of SOC, POC, PON, total N, K, P, available P, exchangeable cations and microbial respiration. RA treatments combining ground covers and organic amendments (GM&OA and NT&OA) exhibited greater overall soil quality restoration than individual practices. NT&OA stood out for presenting the highest soil quality improvements. All RA treatments maintained similar crop nutritional status compared to conventional management. We concluded that RA has strong potential to restore the physical, chemical and biological quality of soils of woody agroecosystems in Mediterranean drylands without compromising their nutritional status. Furthermore, farming management combinations of multiple regenerative practices are expected to be more effective than applying individual RA practices. In parallel to researchers´ assessment of RA impacts, farmers assessed RA impacts in their farms by using the farmer manual jointly developed in participatory workshops. Chapter 4 presents the RA impact results from farmers´ assessment, and documented farmers´ insights, in the third year of PM&E, on the visual soil assessment process using the farmer manual, and on PM&E outcomes regarding the facilitation of participation and learning processes. This chapter corresponds to the application of phase 6 and phase 7 of the methodological framework developed in Chapter 2. Farmers´ visual soil assessment indicated regenerative agriculture as a promising solution to restore degraded agroecosystems in semiarid Mediterranean drylands, although observed soil quality improvements were relatively small compared to conventional management, and more time and efforts are needed to attain desired restoration targets. The monitoring results on RA reported by farmers were complementary to researchers´ findings using technical indicators of soil quality. Farmers’ evaluation of the research project highlighted the PM&E research as an educational process that helped them look differently at their land and their restoration efforts and facilitated the creation of relationships of support and trust, learning and capacity building that are fundamental conducive conditions to enhance farming innovation efficiency and adoption. Farmers confirmed that generating spaces for farmer-to-farmer diffusion of knowledge and on-farm experiences is a key driver to expedite farming testing and adoption of innovations. Farmers insights revealed the need to actively involve them in all decision making phases of VSA tools and support them in initial implementation, in order to develop tools that meet farmers´ needs, to enhance VSA tool adoption, and facilitate reaching restoration goals. Furthermore, farmers´ evaluation of the farmer manual suggested the need to reinforce the multipurpose usefulness and potential benefits of collectively recording restoration progress in a systematized way, to enhance VSA tool adoption. Farmers´ insights on the PM&E research reinforces the importance of developing learning communities of farmers and researchers that provide a platform for exchange of experiences and support, as a crucial factor to favor social learning and support the adoption of long-term agricultural innovations. The success of PM&E research for agroecosystem restoration can be improved by integrating iterative phases where farmers can evaluate and adjust research activities and outcomes. We concluded that the process of PM&E that leads to enhanced social capital, social learning and improved understanding of restoration efforts has as much value as the actual restoration outcomes on the ground. Social learning is considered an important precondition for the adoption of contextualized sustainable land management and farming innovations like RA. The main objective of involving farmers and researchers in PM&E of RA was to enable social learning for enhanced understanding of RA impacts and support adoption of RA. Although there is a growing body of literature asserting the achievement of social learning through participatory processes, social learning has been loosely defined, sparsely assessed, and only partially covered when measured. Confirming that a participatory process has favored social learning implies demonstrating that there has been an acquisition of knowledge and change in perceptions at individual and collective level in the people involved in the participatory process, and that this change in perceptions has been generated through social relations. Chapter 5 presents an assessment of how the PM&E research process enabled social learning by effectively increasing knowledge exchange and understanding of RA impacts between participating farmers and researchers, and multiple stakeholders of farmers´ social networks. Occurrence of social learning was assessed by covering its social-cognitive (perceptions) and social-relational (social networks) dimensions. This chapter discusses the potential of PM&E to foster adoption and out-scaling of sustainable land management and farming innovations like RA by promoting the generation of information fluxes between farmers and researchers participating in PM&E and the agricultural community of which they form part. To assess changes in farmers´ perceptions and shared fluxes of information on RA before starting the PM&E and after three years of research, we applied fuzzy cognitive mapping and social network analysis as graphical semi-quantitative methods. Our results showed that PM&E enabled social learning amongst participating farmers who strengthened and enlarged their social networks on information sharing, and presented a more complex and broader common understanding of regenerative agriculture impacts and benefits. This supports the idea that PM&E thereby creates crucial preconditions for the adoption and out-scaling of RA. This study was one of the first studies in the field of natural resource management and innovation adoption proving that social learning occurred by providing evidence of both the socialcognitive and social-relational dimension. Our findings are relevant for the design of PM&E processes, agroecosystem Living Labs, and landscape restoration initiatives that aim to support farmers´ adoption and out-scaling of contextualized farming innovations and sustainable land management. We concluded that PM&E where the democratic involvement of participants is the bedrock of the whole research process and the needs and concerns of the farming community are taken as the basis for collaborative research represents a great opportunity to generate inclusive, engaging, efficient, and sound restoration processes and transitions towards sustainable and resilient agroecosystems

Monitorización y Evaluación Participativa en Agricultura Regenerativa: Del conocimiento y los impactos locales a la adopción a gran escala

The advanced state of land degradation affecting more than 3,200 million people worldwide have raised great international concern regarding the sustainability of socio-ecological systems, urging the large-scale adoption of contextualized sustainable land management. The agricultural industrial model is a major cause of land degradation due to the promotion of unsustainable management practices that deteriorate the quality of soils compromising their capacity to function and deliver ecosystem services. The consequences derived from land degradation are especially devastating in semi-arid regions prone to desertification, where rainfall scarcity and irregularity intensifies crop failure risks and resource degradation, compromising the long term sustainability of these regions. Regenerative agriculture (RA) has recently gained increasing recognition as a plausible solution to restore degraded agroecosystems worldwide. RA is a farming approach foreseen to reverse land degradation, increase biodiversity, boost production and enhance the delivery of multiple ecosystem services by following a series of soil quality restoration principles and practices. Despite its promising benefits, RA has been limitedly adopted in semiarid regions. Major reasons explaining this seemingly incongruous mismatch are the scarce and contrasting empirical data proving its effectiveness, top-down research approaches and lack of farmer involvement in agroecosystem restoration projects and decision-making, and the generally slow response of soils to management changes in semiarid regions, which may delay the appearance of visible results discouraging farmers from adopting RA. In the high steppe plateau of southeast Spain, an on-going process of large-scale landscape restoration through adoption of regenerative agriculture was initiated in 2015. The high steppe plateau is one of the European regions most affected by land degradation and desertification processes and represents one of the world´s largest areas for the production of rainfed organic almonds. In 2015, local farmers created the AlVelAl association with the support of the Commonland Foundation, business entrepreneurs, regional governments, and research institutions, and started to apply RA at their farms. The objective was to restore vast extensions of degraded land for increasing the productivity and biodiversity of their agroecosystems, increasing the resilience to climate change, generating job opportunities and enhancing social cohesion in the region, in a time frame of 20 years following Commonlands´ 4-Returns approach. However, the limited empirical information supporting RA effectiveness, the lack of reference examples in the region, and the slowness with which visible ecological restoration processes usually occur in semiarid regions were considered major obstacles hindering RA adoption in the region. To effectively address this knowledge gap, support farmers and expedite RA adoption, this research proposed horizontal research fostering the creation of learning communities between farmers and researchers, putting together local and scientific knowledge to improve the understanding of RA. This thesis presents a participatory monitoring and evaluation research (PM&E) applying a combination of social and ecological methods to evaluate the potential of PM&E to enhance knowledge exchange between farmers and researchers on Regenerative Agriculture in the context of the high steppe plateau. The aim of this thesis is twofold: on one hand, to increase the understanding on RA impacts, on the other hand, to evaluate the potential contribution of PM&E to enable social learning and contribute to the adaptation and long term adoption of RA in the high steppe plateau and semiarid regions in general. To facilitate PM&E of the impacts of sustainable land management and agricultural innovations like RA, Chapter 2 presents a participatory methodological framework that guides the identification and selection of technical and local indicators of soil quality, generating a monitoring system of soil quality for PM&E by farmers and researchers. The methodological framework includes the development of a visual soil assessment tool integrating local indicators of soil quality for farmers´ monitoring. The framework consists of 7 phases: 1) Definition of research and monitoring objectives; 2) Identification, selection and prioritization of Technical Indicators of Soil Quality (TISQ); 3) Identification, selection and prioritization of Local Indicators of Soil Quality; 4) Development of a visual soil assessment tool integrating LISQ; 5) Testing and validation of the visual soil evaluation tool; 6) Monitoring and assessment of sustainable land management impacts by researchers and farmers using TISQ and the visual soil evaluation tool respectively and; 7) Exchange of monitoring results between all involved participants, and joint evaluation of impacts. To facilitate PM&E of RA in the steppe highlands, Phases 1 to 5 were applied through a series of participatory methods including a first meeting with AlVelAl board members for the definition of research objectives, farm visits, participatory workshops, and conducting formal and informal interviews, among others. Technical indicators of soil quality were identified, selected and prioritized by researchers through an extensive literature review and ad-hoc expert consultation with expertise in soil quality assessment and monitoring. Local indicators of soil quality were identified, selected, prioritized and validated by farmers in two participatory workshops. The co-developed visual soil assessment tool, named the farmer manual, was tested and validated during the second workshop. Local indicators selected by farmers focused mostly on supporting, regulating and provisioning ecosystem services including water regulation, erosion control, soil fertility and crop performance. Technical indicators selected by researchers focused mostly on soil properties including aggregate stability, soil nutrients, microbial biomass and activity, and leaf nutrients, covering crucial supporting services. The combination of local and technical indicators provided complementary information, improving the coverage and feasibility of RA impact assessment, compared to using technical or local indicators alone. The methodological framework developed in this chapter facilitated the identification and selection of local and technical indicators of soil quality to generate relevant monitoring systems and visual soil assessment tools adapted to local contexts, thus improving knowledge exchange and mutual learning between farmers and researchers to support the implementation of RA and optimize the provision of ecosystem services. Implementation of RA usually happens gradually due to socioeconomic, informational, practical, environmental and political constraints Thus, RA adoption by farmers, in practice, translates into different combinations of RA practices, with a diversity of management, based on farmer capabilities, environmental conditions, and expected restoration results. To help the design, adoption and implementation of most effective RA practices to optimize the restoration of agroecosystems, Chapter 3 presents the impacts of the different combinations of RA practices implemented by participating farmers on crucial soil quality and crop performance indicators using previously selected technical indicators of soil quality over a period of 2 years. This chapter corresponds to the application of phase 6 of the methodological framework developed in Chapter 2. RA impacts were assessed in 9 farms on one field with regenerative management and one nearby field with conventional management based on frequent tillage, that were selected together with farmers. Fields were clustered under regenerative management based on the RA practices applied and distinguished 4 types of RA treatments: 1) reduced tillage with green manure (GM), 2) reduced tillage with organic amendments (OA), 3) reduced tillage with green manure and organic amendments (GM&OA), and 4) no tillage with permanent natural covers and organic amendments (NT&OA). The impacts of RA compared to conventional management were evaluated by comparing physical (bulk density and aggregate stability), chemical (pH, salinity, total N, P, K, available P, and exchangeable cations) and biological (SOC, POC, PON, microbial activity) properties of soil quality, and the nutritional status of almond trees (leaf N, P and K). Our results show that GM improved soil physical properties, presenting higher soil aggregate stability. We found that OA improved most soil chemical and biological properties, showing higher contents of SOC, POC, PON, total N, K, P, available P, exchangeable cations and microbial respiration. RA treatments combining ground covers and organic amendments (GM&OA and NT&OA) exhibited greater overall soil quality restoration than individual practices. NT&OA stood out for presenting the highest soil quality improvements. All RA treatments maintained similar crop nutritional status compared to conventional management. We concluded that RA has strong potential to restore the physical, chemical and biological quality of soils of woody agroecosystems in Mediterranean drylands without compromising their nutritional status. Furthermore, farming management combinations of multiple regenerative practices are expected to be more effective than applying individual RA practices. In parallel to researchers´ assessment of RA impacts, farmers assessed RA impacts in their farms by using the farmer manual jointly developed in participatory workshops. Chapter 4 presents the RA impact results from farmers´ assessment, and documented farmers´ insights, in the third year of PM&E, on the visual soil assessment process using the farmer manual, and on PM&E outcomes regarding the facilitation of participation and learning processes. This chapter corresponds to the application of phase 6 and phase 7 of the methodological framework developed in Chapter 2. Farmers´ visual soil assessment indicated regenerative agriculture as a promising solution to restore degraded agroecosystems in semiarid Mediterranean drylands, although observed soil quality improvements were relatively small compared to conventional management, and more time and efforts are needed to attain desired restoration targets. The monitoring results on RA reported by farmers were complementary to researchers´ findings using technical indicators of soil quality. Farmers’ evaluation of the research project highlighted the PM&E research as an educational process that helped them look differently at their land and their restoration efforts and facilitated the creation of relationships of support and trust, learning and capacity building that are fundamental conducive conditions to enhance farming innovation efficiency and adoption. Farmers confirmed that generating spaces for farmer-to-farmer diffusion of knowledge and on-farm experiences is a key driver to expedite farming testing and adoption of innovations. Farmers insights revealed the need to actively involve them in all decision making phases of VSA tools and support them in initial implementation, in order to develop tools that meet farmers´ needs, to enhance VSA tool adoption, and facilitate reaching restoration goals. Furthermore, farmers´ evaluation of the farmer manual suggested the need to reinforce the multipurpose usefulness and potential benefits of collectively recording restoration progress in a systematized way, to enhance VSA tool adoption. Farmers´ insights on the PM&E research reinforces the importance of developing learning communities of farmers and researchers that provide a platform for exchange of experiences and support, as a crucial factor to favor social learning and support the adoption of long-term agricultural innovations. The success of PM&E research for agroecosystem restoration can be improved by integrating iterative phases where farmers can evaluate and adjust research activities and outcomes. We concluded that the process of PM&E that leads to enhanced social capital, social learning and improved understanding of restoration efforts has as much value as the actual restoration outcomes on the ground. Social learning is considered an important precondition for the adoption of contextualized sustainable land management and farming innovations like RA. The main objective of involving farmers and researchers in PM&E of RA was to enable social learning for enhanced understanding of RA impacts and support adoption of RA. Although there is a growing body of literature asserting the achievement of social learning through participatory processes, social learning has been loosely defined, sparsely assessed, and only partially covered when measured. Confirming that a participatory process has favored social learning implies demonstrating that there has been an acquisition of knowledge and change in perceptions at individual and collective level in the people involved in the participatory process, and that this change in perceptions has been generated through social relations. Chapter 5 presents an assessment of how the PM&E research process enabled social learning by effectively increasing knowledge exchange and understanding of RA impacts between participating farmers and researchers, and multiple stakeholders of farmers´ social networks. Occurrence of social learning was assessed by covering its social-cognitive (perceptions) and social-relational (social networks) dimensions. This chapter discusses the potential of PM&E to foster adoption and out-scaling of sustainable land management and farming innovations like RA by promoting the generation of information fluxes between farmers and researchers participating in PM&E and the agricultural community of which they form part. To assess changes in farmers´ perceptions and shared fluxes of information on RA before starting the PM&E and after three years of research, we applied fuzzy cognitive mapping and social network analysis as graphical semi-quantitative methods. Our results showed that PM&E enabled social learning amongst participating farmers who strengthened and enlarged their social networks on information sharing, and presented a more complex and broader common understanding of regenerative agriculture impacts and benefits. This supports the idea that PM&E thereby creates crucial preconditions for the adoption and out-scaling of RA. This study was one of the first studies in the field of natural resource management and innovation adoption proving that social learning occurred by providing evidence of both the socialcognitive and social-relational dimension. Our findings are relevant for the design of PM&E processes, agroecosystem Living Labs, and landscape restoration initiatives that aim to support farmers´ adoption and out-scaling of contextualized farming innovations and sustainable land management. We concluded that PM&E where the democratic involvement of participants is the bedrock of the whole research process and the needs and concerns of the farming community are taken as the basis for collaborative research represents a great opportunity to generate inclusive, engaging, efficient, and sound restoration processes and transitions towards sustainable and resilient agroecosystems

Models of natural pest control : Towards predictions across agricultural landscapes

Natural control of invertebrate crop pests has the potential to complement or replace conventional insecticide based practices, but its mainstream application is hampered by predictive unreliability across agroecosystems. Inconsistent responses of natural pest control to changes in landscape characteristics have been attributed to ecological complexity and system-specific conditions. Here, we review agroecological models and their potential to provide predictions of natural pest control across agricultural landscapes. Existing models have used a multitude of techniques to represent specific crop-pest-enemy systems at various spatiotemporal scales, but less wealthy regions of the world are underrepresented. A realistic representation of natural pest control across systems appears to be hindered by a practical trade-off between generality and realism. Nonetheless, observations of context-sensitive, trait-mediated responses of natural pest control to land-use gradients indicate the potential of ecological models that explicitly represent the underlying mechanisms. We conclude that modelling natural pest control across agroecosystems should exploit existing mechanistic techniques towards a framework of contextually bound generalizations. Observed similarities in causal relationships can inform the functional grouping of diverse agroecosystems worldwide and the development of the respective models based on general, but context-sensitive, ecological mechanisms. The combined use of qualitative and quantitative techniques should allow the flexible integration of empirical evidence and ecological theory for robust predictions of natural pest control across a wide range of agroecological contexts and levels of knowledge availability. We highlight challenges and promising directions towards developing such a general modelling framework.Peer reviewe

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space