Designing resilient farming systems for a turbulent world: learning from communities at the frontline

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Asresehegn, Miranda MEUWISSEN, Vivian Valencia, Steffen Schulz, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6227313/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In a rapidly changing world, designing resilient farming systems is critical. Recent socio-ecological research hypothesized that the general resilience of farming system to disturbances is related to the interplay between four key resilience attributes—Agencies, Buffers, Connectivity, and Diversity (ABCD). However, the relative importance of these attributes in coping with multiple concurrent disturbances remains unclear. This study leverages longitudinal socio-ecological data, including biotic, abiotic and socio-political shocks and community responses, to explore how the ABCD attributes mediate farming system resilience. Using satellite-derived soil moisture content, green soil cover, and aboveground biomass data, complemented by focus group discussions in twelve communities, we analyzed the land restoration outcomes in the face of multiple disturbance and the contributions of ABCD attributes to resilience. The findings revealed that “bright spot” communities that had already been improving their natural resource management were consistently more resilient to multiple shocks. Our results also show that attributes A and B are essential to cope with multiple disturbances, while the contributions of C and D were more nuanced and depended on the type of disturbance. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental social sciences Farming systems Resilience resilience attributes ABCD Bright spots Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In today’s increasingly turbulent world, the ability of farming systems to adapt and transform—referred to as resilience—has become critically important. Rapid and unpredictable changes in environmental, social, and economic contexts, driven by both natural and human-induced catastrophes such as extreme droughts, floods, pest outbreaks, social unrest, and spiking commodity prices, pose significant challenges to farming systems worldwide (Nauck et al., 2021; Cvetković, 2024; Sharma & Pandey, 2024). These challenges often exceed the adaptive capacity of conventional farming systems. For example, the COVID-19 pandemic exposed the vulnerabilities of agricultural systems and disrupted food security globally, demonstrating the fragility of existing systems in the face of natural and manmade crises (Darnhofer, 2021; Meuwissen et al., 2021). The unpredictable nature of such crises underscores the need for innovative and transformative approaches to meet the growing demand for food, fiber, and energy amidst these uncertainties. Resilience, as a concept, has thus gained prominence among policymakers, sustainability scientists, and development practitioners (Nauck et al., 2021; Shilomboleni et al., 2024). Unlike traditional notions of efficiency, which often prioritize short-term gains by minimizing redundancy, resilience emphasizes long-term sustainability by enabling systems to withstand, adapt to, and transform in response to challenges (Duchek, 2020; Golgeci et al., 2020; Meuwissen et al., 2019). At its core, resilience comprises three interconnected capacities: 1) robustness: or the ability to maintain system structure despite disturbances; 2) adaptability: the ability to adjust system structures to evolving challenges, and 3) transformability: the capacity for fundamental reconfiguration when existing systems are no longer viable (Folke et al., 2010; Darnhofer, 2014). Drawing on socio-ecological and socio-psychological theories, resilience can be further categorized into two types: individual resilience and collective resilience. Individual resilience is influenced by personal traits such as optimism and coping strategies, as well as external support networks. In contrast, collective resilience emerges from shared resources, governance mechanisms, and a collective identity that fosters mutual support (Folke, 2006; Norris et al., 2008). These types operate along two dimensions: general resilience, which enables systems to address a wide array of challenges, including unexpected ones, and specific resilience, which focuses on the capacity to tackle specific expected challenges, e.g. flooding or drought (Folke et al., 2010). This theoretical framing emphasizes the multi-dimensional and context-dependent nature of resilience, requiring tailored approaches to address the unique challenges faced by farming systems. Resilience is an inherent yet latent characteristic of farming systems, making it difficult to predict their capacity to endure, adapt, and transform in response to unforeseen shocks and disturbances (Feindt et al., 2022). Traditionally, resilience has been assessed retrospectively—measured only after farming systems experience one or multiple shocks (e.g., Toorop et al., 2023). Recent research has sought to complement this reactive approach by identifying ex-ante attributes indicative of resilient farming systems. In this regard, four key attributes have emerged: 1) Agency, the ability of system actors to make decisions and take action; 2) Buffering, the capacity to absorb disturbances while minimizing damage; 3) Connectivity, the physical and virtual linkages between system components; and 4) Diversity, the variety of components and processes that enhance adaptability and flexibility (Fonteijn et al., 2022; Meuwissen et al., 2022). Recent studies indicated that these attributes operate systematically, influencing the entire farming system, and exhibited stability by maintaining core functions while allowing adaptation and evolution (Meuwissen et al., 2019; Duchek, 2020; Mathijs & Wauters, 2020). However, their specific appearance vary over time, across regions, and at different scales (Feindt et al., 2022). Several resilience studies emphasized on the relationship between attributes and resilience capacity of farming systems with focus on large farms in Europe (Nera et al., 2020; Paas et al., 2021a; Reidsma et al., 2020). These investigations highlight the interconnectedness of the resilience attributes to cope with, adapt to, and transform in response to disturbances. Notably, Reidsma et al. (2023) highlighted the likely change of the contribution of the attributes when the current European farming system transition to alternative future farming models to cope with unknown shocks. Their findings revealed that shifts in farming system can alter the importance of the attributes. Despite these advancements, two key knowledge gaps remain: 1) which resilience attributes are most critical for the collective resilience of smallholder farming systems, and 2) how the contribution of these attributes change in response to multiple concurrent disruptions. Addressing these gaps is essential for designing more resilient farming systems, particularly in regions where smallholder agriculture plays a dominant role. We chose to address these gaps by studying the resilience of communities that have proven to be resilient by thriving despite their exposure to multiple different- abiotic, biotic, and socio-political shocks. The selected communities have been participating in the Ethiopian National Sustainable Land Management flagship Program (SLMP) which is currently operating in more than 3000 community watersheds ( http://nrdsmis.moa.gov.et/app/landing ), each of which comprises a natural drainage area covering on average 500 ha (Mugoro et al., 2020). The SLMP is aimed at reducing land degradation and enhancing agricultural productivity (Schmidt and Tadesse 2019; Ponce et al., 2021) Land degradation, encompassing the depletion of soil, water, and vegetation resources, poses a significant threat to agricultural productivity and global food security (IPCC, 2019; FAO, 2021). In response, community-led land management initiatives have emerged as critical strategies for enhancing soil health, boosting agricultural productivity, and meeting the growing demand for food, fiber, and energy (FAO, 2016; O'Donoghue et al., 2022). These efforts improve key land functions, such as soil cover, moisture retention, and biomass production (Saleem, 2019; Wei et al., 2021; Erkossa et al., 2022). Improved soil cover protects against erosion and enhances soil quality, while increased moisture and biomass directly contribute to land productivity (Lozano‐Parra et al., 2018).Therefore, we chose soil moisture content, permanent soil cover, and increased biomass as positive Key Performance Indicators (KPIs), for successful land restoration, reflecting advances in land productivity and the capacity to protect soil erosion (Edward et al., 2019; Belayneh et al., 2024). After a decade of implementation (2012–2021), not all 3,000 communities have been equally successful in the restoration of their land resources, as measured by the proportion of watershed area under sustainable land management. In our previous study (Asresehegn et al., 2025, under review), we identified the unique features of the highly successful-land restoration bright spots—communities with exceptionally high restoration performance (Valencia et al., 2022). We showed that these communities have been characterized by self-organization, strong local leadership, and active community participation in the design and implementation of land management programs a (Asresehegn et al., 2025, under review). Our current study focused on 12 community watersheds—six highly successful ("bright spots") and six that have been unsuccessful in land restoration performance. Both sets of community watersheds have achieved these results amidst recurring disruptions that have included severe drought, the COVID-19 pandemic, desert locust invasions and armed conflict. In some instances, two or three shocks coincided. Using the trend of land restoration outcomes derived from mutli-source independent earth observation data, We assessed the extent to which the outcomes of the land restoration actions were influenced by unexpected biotic, abiotic, or sociopolitical disruptions. These disruptions may challenge sustainable land management by depleting resources or redirecting their use for immediate needs. At the same time, such challenges can also drive innovation, prompting communities to adopt adaptive strategies and reaffirm commitments to sustainable practices (Måren et al., 2022; Frietsch et al., 2023). Figure 1 demonstrates the conceptual understanding on the interplay between restoration actions, disruptions, and possible restoration outcomes. Therefore, the contrast in land restoration outcomes between successful and unsuccessful communities in the face of these multiple biotic, abiotic and socio-political disturbances provided valuable opportunities for understanding the trend of the land restoration outcomes of the communities in the face of these challenges and the relative importance of the resilience attributes to coping with multiple concurrent shocks. In summary, this study specifically aims to: i) assess the land restoration outcomes of communities in the face of multiple shocks; ii) understand the relative importance of agency, buffers, connectivity and diversity in coping with the impact of multiple shocks; iii) identify key attributes required for resilience of smallholder farming system to unknown disturbance. 2. Methodology 2.1. Assessment of land restoration outcomes We utilized observational data combined with participatory research. Observational data included analysis of available Earth Observation (EO) data from multiple independent sources on the aforementioned KPIs of the selected community watersheds: above ground biomass, soil moisture content, and green soil cover fraction for the period 2000 to 2023. Participatory approaches involved focus group discussions with community watershed members to capture experiential and context-specific insights. These methods were complementary: the observational data provided objective data on the trend of land restoration outcomes in the face of the multiple disturbance, while the participatory approach offered nuanced insights into how diverse communities respond to shocks and the relative importance of resilience attributes in mediating the extent of impact of individual and multiple shocks on restoration outcomes. To assess the restoration outcomes of the selected communities, we focused on the three KPIs: green soil cover fraction (the proportion of months in a year that soil remains covered by vegetation), soil moisture content, and above ground biomass. Using these variables derived from mutli-source independent EO data, we analyzed these metrics on a yearly basis starting in 2000 for the 12 case study communities. The analysis was conducted using the following approaches: i. Green Soil Cover Fraction Green cover fraction was derived from Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team (GLAD) at the University of Maryland (Potapov et al., 2020 ). The dataset harmonizes Landsat 5 TM, Landsat 7 ETM + and Landsat 8 OLI/TIRS, and Landsat 8 OLI/TIRS. Landsat is the only high resolution which has acquired high quality and consistent ~ 30m resolution earth observation data back from 2000 to 2022 around the globe (Consoli et al., 2024 ). During this analysis, the satellite image of the study sites for 2023 was not available. A bimonthly product (i.e. one image per two months) was derived for the study watershed for each year for the period 2000 to 2022 using weighted temporal aggregation, where weights are assigned based on the clear sky fraction. This approach helped to remove the noise as well as collect a stable seasonality trait of each pixel (Consoli et al., 2024 ; Tian et al., 2024 ). The green soil cover fraction was derived using the concept of the bare soil fraction index. The bare soil fraction was calculated by dividing the number of pixels classified as bare surface within a year’s time series (identified by NDVI values below 0.35) by the total number of pixels analyzed in that year (Potapov et al., 2020 ). Subsequently, the green soil cover fraction was defined as: $$\:Green\:Soil\:cover\:fraction={\sum\:}_{i=1}^{n}\left(1-\frac{X(NDVI\le\:0.35)}{N}\right)x100\%$$ Where: N = total number of pixels analyzed in the watershed for a given year i X(NDVI ≥ 0.35) = the number of pixels with NDVI values below 0.35 for the given year n = number of observations in a given year i ii. Soil Moisture Content We utilized the Global Land Surface Satellite (GLASS) soil moisture product (available for 2000–2020) to monitor changes in soil moisture content within the uppermost soil layer (0–5 cm) of the study watershed. During the assessment period (conducted in October 2024), GLASS images were unavailable for 2021–2023. The soil moisture data were generated using an ensemble machine learning approach that integrated multiple datasets, including surface reflectance, in-situ soil moisture observations, European reanalysis (ERA-5-Land) soil moisture products, and auxiliary data such as DEM and soil information from Soil Grids (Pablos et al., 2016 ). Although the data were not derived from direct observation, they provided extensive spatial coverage and exhibited high spatiotemporal consistency (Zhang et al., 2023 ). Finally, the soil moisture data generated using the machine learning were resampled to a 30-meter resolution to align with the pixel size of Landsat-derived products. iii. Above Ground Biomass We applied a method developed by Liu et al. ( 2015 ) to estimate aboveground biomass (AGB) using satellite-derived passive microwave instruments. According to Liu et al. ( 2015 ), Vegetation Optical Depth (VOD) has a nonlinear correlation with aboveground biomass. Enhanced Vegetation Index (EVI) images from the years 2000 to 2023, which were available during the assessment period (October 2024), were obtained for the study sites. These data, derived from the MODIS MOD13Q1 product in Google Earth Engine, provide 16-day composite EVI observations. To ensure data quality, we extracted stable signals and filtered noise on a yearly basis using a pixel-wise weighted Savitzky-Golay smoothing filter from phenofit, a R package (Kong et al. 2022 ). Subsequently, we fitted a non-linear model by resampling 250-meter EVI data into a 0.025-degree (~ 10 km) grid, averaged to align with the VOD data. The relationship between EVI and aboveground biomass was modeled as follows: $$\:AGB=a*\text{arctan}\left(b*\left(EVIy-c\right)\right)+d$$ Where: AGB: Above Ground Biomass a,b,c,d: Model parameters EVIy: Enhanced Vegetation Index for year y The model is calibrated within the bounding box of Ethiopia. The fitting result shows R^2 = 0.87 and is able to simulate the steep increase of biomass with higher EVI values running concurrently with high biomass forests. EVI values do not exhibit the same high biomass saturation as suffered by NDVI based metrics (Fig. 2 ). Once the model parameters were determined, it was applied to the 250-meter yearly EVI sum at the watershed level to produce a 250-meter resolution time series of aboveground biomass for the years 2000 to 2023. Finally, we compiled yearly data for the above three key land restoration performance Indicators (KPI)—green soil cover fraction, soil moisture content, and above ground biomass—for the case study community watersheds. The annual median values of each parameter were used to analyze the trends and compare among community watersheds, categorized into high- and low-performance groups based on proportion of watershed area under SLM. These comparisons were conducted within the semi-arid Tigray region and the humid Southern region of Ethiopia, both of which have experienced varying shocks. 2.2. Assessment of resilience attributes We assessed agency, buffering, connectivity, and diversification as the key attributes that enable food systems to adapt, respond, or transform in the face of sudden disruptions (de Steenhuijsen Piters et al., 2021 ; Fonteijn et al., 2022 ). Adding to previous approaches to assess resilience attributes of farming systems (Cabell & Oelofse, 2012 ; Meuwissen et al., 2019 ; Paas et al., 2021 ; San Martín et al., 2021 ), we used focus group discussions (FGDs) with community representatives, members of the community watershed development executive team members formally elected by the community, to identify the attributes that helped farming systems cope with specific disruptions, and to evaluate the relative important of the attributes across multiple disruptions. A total of 12 FGDs were conducted, each comprising 8 to 10 members of the community watershed development executive team members selected based on their willingness and availability during the data collection period (January to March 2024). Drawing on literature and input from key informants, the first author identified several major disruptions in the last two decades that affected farming systems in northern and southern Ethiopia. These included severe droughts in 2002/2003 and 2015/16 (Singh et al., 2016 ), Desert Locust outbreaks during the 2019/20, 2020/21, and 2021/22 cropping seasons (Nandelenga & Legesse, 2020 ; Ilukor & Gourlay, 2021 ), and the Civil War from late 2020 to 2022 in Tigray (Nyssen et al., 2024 ), and the COVID-19 pandemic from 2020 to 2021 (Lanyero et al., 2021 ) across all regions of the country. To understand the perceived influence of these disruptions on farming systems, each FGD was tasked with rating the impact of these events in their respective communities using a scale of 1 (very low) to 5 (very high). Figure 3 presents the median ratings of the FGDs in Tigray for the impact of drought, desert locust invasion, and civil war, as well as the median ratings of the FGDs across the two regions for the impact of the COVID-19 pandemic on farming systems. Each group was then asked, "What resilience attribute enabled you to cope with these disruptions?" We did not limit the FGDs to a predefined set of ABCD resilience attributes, allowing participants to explore their local resilience experiences. After extensive brainstorming, the FGDs identified 35 resilience features, after which response saturation was reached. These features were further refined by each FGD community for relevance to their specific watersheds. Ultimately, 28 features were consistently recognized by at least seven of the 12 communities (over 50%). The first author categorized these 28 features into the four resilience attributes—agency, buffering, connectivity, and diversity—based on their characteristics. This classification included Eight features under agency, Eleven under buffering, Nine under connectivity, and Seven under diversity (see Annex 1). Following this, each attribute was elaborated and contextualized with input from FGD participants. The groups then rated the contribution of each attribute to coping with disruptions over the past decade, reaching a consensus on their ratings. A Likert scale (0–5) was used, where 0 indicated no contribution and 5 indicated very high contribution. 2.3. Description of case study sites The case study communities are located in the semiarid Tigray region of northern Ethiopia and the humid areas of southern Ethiopia. These regions represent smallholder mixed crop-livestock systems typical of the Ethiopian highlands (Amede et al., 2019). Farming in these areas depends heavily on watershed resources such as land, water, and vegetation. The watersheds encompass both cultivated lands - with farm sizes averaging less than 1 hectare per household for crop production - and communal areas, such as forest patches, pasturelands, and bushlands. While cultivated lands are individually managed, the communal areas are collectively used for livestock grazing, fuelwood collection, and other ecosystem services. Under Ethiopian law, all land is public property; farmers have usufruct rights but cannot permanently transfer ownership (Tareke, 2019 ). Agricultural inputs such as fertilizers, seeds, pesticides, and veterinary services are primarily provided through government agencies at subsidized cost. However, challenges persist in ensuring the timely and high-quality supply of these inputs (Leta et al., 2017 ). Land degradation is a significant challenge in these watersheds, driven by both biophysical and socioeconomic factors. Biophysical causes include undulating topography, intense rainfall, and erosion-prone soils. Socioeconomic drivers, on the other hand, include poor resource governance, political instability, overgrazing, and unsustainable vegetation use (Brhane & Mekonen, 2009 ; Adugna et al., 2015 ; Kassa et al., 2016). These factors collectively undermine the productive capacity of land and exacerbate rural poverty. To combat land degradation, communities in these watersheds are organized into watershed user cooperatives. These cooperatives play a pivotal role in the restoration and sustainable management of land, water, and vegetation resources under the Ethiopian government’s Sustainable Land Management Program (SLMP) initiatives (FAO, 2022). Oversight is provided by watershed development executive committees—hereafter referred to as executive teams—elected by community members with the support of district and community-level extension workers. The executive teams represent diverse social groups, including women, landless youth, farmers, water users, beekeepers, the elderly, and religious leaders. This inclusivity ensures that watershed management decisions reflect the needs and priorities of the broader community. The teams are responsible for assessing watershed challenges, preparing mitigation measures, drafting community bylaws on resource use, resolving conflicts, mobilizing local resources, and monitoring the implementation of restoration activities (Ministry of Agriculture, 2020; Asresehegn et al., under review). Over the past 12 years(2012–2023), these communities have implemented various land management practices with contrasting performance levels in terms of area under sustainable management. During the intervention period from 2012 to 2023, six bright spot communities—three from the semi-arid North and three from the humid South—achieved sustainable land management coverage of 88–93% of their watershed areas. In contrast, six low-performing communities from the same regions managed only 18–47% coverage. The high performing communities, referred here “bright spots,” achieved this success by prioritizing vegetation restoration and adapting their farming systems to address evolving environmental challenges (Asresehegn et al., under review). The case study sites in the Tigray region faced compounded challenges, including severe droughts, the COVID-19 pandemic, desert locust outbreaks, and civil conflict, all of which disrupted farming systems and restoration efforts. In contrast, communities in southern Ethiopia only identified COVID-19 as their primary challenge during the same period (Figure 4). 3. Results 3.1 Trend of Land Restoration Outcomes The trend analysis of soil moisture content, green soil fraction, and above ground biomass revealed diverging impacts of sudden disturbances on land restoration outcomes (Fig. 5 ). In the semiarid region of Tigray, disturbances such as the severe drought in 2015, desert locust invasions (2019–2021), the Covid-19 outbreak (2020–2022), and the civil war (2020–2022) disrupted restoration efforts. Similarly, in the humid southern regions, the Covid-19 outbreak (2020–2022) was the primary disturbance affecting outcomes. The analysis compared the effects of these events across high- and low-performing communities in land management. a. Soil Moisture Content In the semiarid region of Tigray, soil moisture content declined during the severe drought of 2015, affecting both high- and low-performing communities. However, the bright spot communities regained and consistently improved the soil moisture content during the subsequent years including during the desert locust invasion (2019–2020), while it gradually diminished in low-performing ones during the same period. In contrast, in the humid southern regions, where no major disturbances were observed except the Covid-19 outbreak in the end of 2020, soil moisture remained relatively stable in both high- and low-performing communities. A slight decline was observed in low-performing communities between 2018 and 2020, but no major changes were noted in bright spot communities during the same period. b. Green Soil Cover Fraction The green soil cover fraction exhibited similar trends to soil moisture in the semiarid region of Tigray. During the severe drought of 2015, green soil cover declined in both high- and low-performing communities. However, from 2019 to 2022, during the multiple disturbances including desert locust invasion, the Covid-19 outbreak, and the civil war, the green soil fraction showed an increasing trend in high performing communities while it consistently declined in low-performing communities. In the humid southern regions, the green soil fraction remained consistent, with slight variations between high- and low-performing communities during the COVID-19 outbreak (2020–2022), the primary disturbance in the region. During this period, the average soil moisture content in low-performing communities showed a slight decline compared to that in bright spot communities. c. Above Ground Biomass Unlike soil moisture and green soil fraction, above ground biomass in the semiarid region of Tigray remained stable during the severe drought of 2015 for both high- and low-performing communities. However, during the 2019 to 2022 multiple sudden disturbance of desert locust invasion, the Covid-19 outbreak, and the civil war, above ground biomass increased in bright spot communities but remained stable in low-performing ones. In the humid southern regions, above ground biomass in bright spot communities increased steadily during the Covid-19 outbreak (2020–2022). In contrast, low-performing communities experienced a consistent decline in above ground biomass over the same period. 3.2 Resilience attributes of land management performance groups to multiple shocks The Focus Group Discussions attributed varying levels of importance to each of the 28 resilience attributes, depending on the type of disturbance, levels of land management performance, and agroclimatic regions (Fig. 6 ). The communities across the semiarid and humid regions consistently identified agency, buffers, connectivity, and diversity as critical resilience attributes to cope with the impacts of COVID-19 pandemic. Each attribute is captured by several key indicators including local leadership, technical advisors, and self-organization for agency; safety nets and cash savings for buffers; access to information and communication infrastructure for connectivity; and integrated pest and soil fertility management for diversity Differences in emphasis in the attributes emerged between the bright spots and low-performing communities. While bright spot communities consistently recognized the relatively higher contribution of community leadership and self-organization capacities in both regions, the importance of these attributes varied among low-performing communities across regions. Furthermore, buffers such as the use of community resources, formal and informal safety nets, and the preservation of feed and food were recognized as highly important by bright spot communities in both regions for coping with the impact of COVID-19. In contrast, low-performing communities prioritized attributes differently. For example, low-performing communities in the humid southern regions identified formal and informal safety nets as highly important, whereas those in the semi-arid Tigray region placed greater emphasis on the preservation of feed and food. Regional differences across the same performance groups were also evident. In the humid regions, bright spot communities prioritized agency attributes, such as information providers, local leadership, and ambitions for change. Conversely, bright spot communities in the semiarid regions of Tigray placed greater emphasis on buffering mechanisms, including water conservation, multiple cropping, and community labor forces.. In the face of multiple shocks, such as severe drought, desert locust invasions, civil war, and COVID-19, notable differences in resilience attributes of the communities emerged. Agency and buffering attributes were consistently recognized critical to coping with these challenges in the semi-arid in Tigray region. However, the contribution of connectivity and diversity attributes varied depending on the type of shock. For example, connectivity attributes, such as access to roads, communication infrastructure, and basic services, were vital in managing environmental shocks like drought and locust invasions. Yet, these were less effective during socio-political shocks like the civil war. Similarly, diversification strategies (e.g., crop and livelihood diversification) proved effective for environmental and political shocks but were less impactful during the COVID-19 pandemic. Performance-based differences were also observed in mitigating the impact of combined shocks. Bright spots demonstrated the effective use of a broad range of agency and buffering attributes. Meanwhile, low-performing communities relied more heavily on specific buffering and connectivity mechanisms. 4. Discussion The findings of this study revealed divergent trends in land restoration outcomes among communities exposed to multiple shocks. Bright spot communities exhibited overall improvements in soil moisture content, green soil cover, and aboveground biomass despite severe drought, the COVID-19 pandemic, desert locust infestations, and civil war, indicating the general resilience of their farming systems. The findings also highlighted the resilience attributes identified by the community as necessary in mitigating the impacts of multiple shocks. Notably, agency and buffers were required critical across all shocks, emphasizing their role in enhancing the resilience of smallholder farming systems to future disturbances. We discussed the analysis result of the land restoration outcomes restoration outcomes, the resilience attributes that enabled communities to cope with multiple shocks, and implications for anticipated future unknown disruption. 4.1 Resilience of Community Land Restoration Outcomes to Drought Our results highlighted the greater sensitivity of soil moisture and green soil cover to drought conditions compared to the buffered response of above-ground biomass. Above-ground biomass, which includes living plant material such as stems, leaves, and reproductive structures, appeared more resilient to short-term drought stress. This observation resonates with previous studies, such as Li et al. ( 2022 ), who found that reduced soil moisture during droughts in semiarid regions severely impacts plant water availability, often leading to diminished green soil cover as vegetation becomes stressed or dies off. Similarly, de Meira Junior et al. ( 2020 ) noted that above-ground biomass demonstrates a delayed response to short-term drought due to its capacity to utilize residual water resources, thus buffering its reaction to initial drought stress. The analysis also showed that severe drought had similar effects on soil moisture and green cover across both bright spot and low-performing communities in the semiarid region. This uniform response likely results from the limited implementation of land management measures during the early stages of the Sustainable Land Management (SLM) Program, which began in 2012. By 2015, when the drought occurred, most bright spot communities were still focused on establishing governance rules, resource-sharing agreements, and conflict resolution mechanisms, as noted in earlier research (Asresehegn et al., under review). These preparatory activities typically precede the on-ground implementation of land management practices. As a result, the capacity of communities to mitigate the immediate effects of the drought were similar for high and low-performing communities. 4.2 Resilience of Community Land Restoration Outcomes to multiple shocks The distinct trend of restoration outcomes in bright spots and low-performing communities, particularly after 2018, can be partly explained by the impact of multiple concurrent disturbances such as desert locust invasions, the COVID-19 pandemic, and armed conflict in the semiarid regions, with the pandemic also affecting the humid areas. In the absence of coordinated and appropriate community action, desert locusts, known for their capacity to consume vast amounts of vegetation, could lead to significant reductions in green soil cover and above-ground biomass (Odhiambo et al., 2021 ). Furthermore, the COVID-19 pandemic disrupted labor availability, supply chains, and market access, exacerbating the challenges faced by smallholder communities and hindering their ability to manage land effectively (Rahaman et al., 2020 ). Armed conflicts could further complicate this scenario by diverting resources and attention away from land restoration efforts, thus aggravating the negative impacts of these disturbances (Hassoun et al., 2024 ). Bright spot communities demonstrated the resilience capacity to cope with these shocks through their consistent improvements in soil moisture, green soil cover, and above-ground biomass, which were directly linked to the extensive SLM practices implemented in the watersheds. During focus group discussions, bright spot communities consistently explained, "Our long-term engagement in the conservation and sustainable management of the land, water, and vegetation resources helped us to expand the land area of the watershed under irrigation and produce two to three crop cycles a year during the crises. Furthermore, we developed local solutions to mitigate the impact of desert locusts." These attributes, highlighted by the bright spots, underscore the significance of sustainable land management interventions and local knowledge to adapt to evolving challenges, in line with Haile et al. ( 2021 ) and with Kerner et al. ( 2024 ) who reported on the resilience of farmers in Tigray during the war. During one of the discussions, a community leader in Tigray region, noted that while disturbances generally pose challenges, they also present opportunities for innovation. He explained how floods, once a major disaster for farming in his village, had become an opportunity: "We have learned how to effectively store and use floods after the severe drought." Studies support this adaptive approach, showing that with better coordination, stronger partnerships, and support from local agencies, communities can develop more robust strategies that enhance long-term productivity and sustainability (Nyawira et al., 2016 ; FAO, 2020 ; Shilomboleni et al., 2024 ). 4.4 Relative importance of resilience attributes to cope with multiple shocks Our analysis showed that, across various disturbances, bright spot communities consistently recognized the importance of key resilience attributes, including agency (local innovations, leadership, self-organization), buffering (water conservation and storage; and access to local savings). However, the contribution of connectivity and diversity attributes varied across disturbances. During focus group discussions, community members in the semiarid areas, who experienced multiple shocks, explained that while connectivity attributes such as access to roads and basic services were important in coping with the impacts of severe drought and desert locusts, their role was minimal or even negative during times of conflict and the COVID-19 pandemic, as these factors increased vulnerability, enabling soldiers to reach villages rapidly. While physical connectivity such as roads and access to basic services faced limitations during the covid 19 pandemic, virtual connectivity - such as communication infrastructure, remittances, and access to information - played a vital role in maintaining social ties, knowledge exchanges, and coordination of activities. Budd et al. ( 2020 ) and Pattanasri et al. ( 2022 ) also found that during the lockdown due to COVID-19, virtual communication significantly contributed to maintaining social connections, accessing information, and coordinating efforts among communities. However, both physical and virtual connectivity, except these within the community, were not functional or played negative role during the civil war. Figure 7 summarizes the resilience attributes observed in bright spot communities across multiple shocks, and shows the varying significance of individual agency, buffering, connectivity and diversity attributes to cope with the impact of multiple shocks. The result highlights that a minimum number of agency and buffering attributes are always required. For connectivity, it shows that exposure to information, inter-community linkages, and basic services are important to cope with multiple disturbance. For diversity, there is universal requirement for livelihood diversification, alternative energy sources, and integrated pest and soil fertility management. These findings challenge the prevailing notion that resilience attributes are stable and systemic (Meuwissen et al., 2019 ; Duchek, 2020 ; Mathijs & Wauters, 2020 ; van der Lee et al., 2022 ). These studies have emphasized that resilience attributes are interrelated, suggesting that the four attributes function systematically within farming systems and are collectively necessary for coping with shocks. Furthermore, these studies have argued that resilience attributes remain stable, implying that their importance in maintaining core functions of farming systems are consistent. However, our findings indicate that high-performing (bright spot) communities sustained positive land restoration outcomes amid multiple concurrent shocks while relying minimally on agency and buffers. Additionally, we observed that the relative importance of resilience attributes in maintaining the function of the farming systems varied across different types of shocks. These results highlight the need for further research across diverse contexts to better understand the dynamic interplay between resilience attributes. The results also revealed that a combination of proactive and reactive approaches are required for communities to cope with multiple shocks. Proactive approaches, such as enhancing local leadership capacities, fostering local innovations, self-organization, water conservation and storage, and food preservation, helped communities anticipate disruptions and provide relief. In contrast, reactive approaches—such as utilizing community resources, mobilizing forces, accessing information, and using labor-saving farming techniques—helped them communities adapt to new challenges. These findings align with recent studies on organizational resilience, which emphasize the importance of balancing proactive and reactive strategies to cope with both predicted and unpredicted disturbances (Denyer, 2017 ; Pinkwart et al., 2022 ). 4.4 Anticipating the next, unknown shock As Fig. 7 summarizes the essential attributes to cope with biotic, abiotic and socio-political shock, its insights may help communities to anticipate and plan for the next shocks, even when their timing or nature is yet unknown. Our findings show that community leadership, community resources, formal/informal safety nets, buffers of food and feed, and access to information were consistently critical to cope with all shocks, regardless of their nature, followed by the availability of technical advice, an ambition for change, self-organization, water conservation, a communal working force and labor-saving crops. These findings are of direct relevance to help communities prioritize actions to enhance their ex-ante resilience to known or unknown shocks. The extent to which these ‘essential attributes’ are specific to farming communities in the Ethiopian Highlands, or generic to multiple smallholder farming systems across the global South is subject to ongoing studies involving bright spot farming communities in the Global Network of Lighthouse Farms. 5. Conclusion This study addresses the question of how communities respond in the face of multiple shocks. By analyzing the land restoration outcomes in the face of diverse and compounding biotic, abiotic and socio-political shocks, we were able to assess how resilience attributes—agency, buffers, diversity, and connectivity—varied in terms of their relative importance depending on the type of shock the community faced. In our previous study (Asresehegn et al., 2025, under review), we identified strong local leadership and self-organized communities that are actively engaged in the design and implementation of land management measures in their watershed, as key features of successful land restoration "bright spot" communities. These characteristics enabled them to accelerate the implementation of sustainable land management practices, primarily focused on vegetation regeneration, with the intended goal of transforming farming systems. In this study, we addressed the question of how shocks disrupt these land management outcomes and how different resilience attributes mediated the extent of the impact of shocks on restoration outcomes Overall, we find that drought negatively affected soil moisture content and green soil cover, while above-ground biomass showed better resilience to short-term drought conditions. These findings emphasize the importance of prioritizing on-the-ground implementation of land management, particularly tree-based interventions such as agroforestry and agro-silviculture alongside strengthening leadership, technical advisory support, and the self-organization capacity of communities, as crucial for building resilience to disturbances, especially in the early stages of land management. Bright spot communities recognized that agency and buffers are essential for sustaining and improving land management outcomes, enabling farming systems to withstand multiple, unpredictable disturbances. In this study, agency was associated with local leadership, innovation, farmer technicians, local information providers, and community self-organization, while buffers were defined by accumulated local resources, including water, infrastructure, savings, and stored produce, which communities could rely on during shocks. The roles of connectivity and diversity were found to be context-dependent. For example, while access roads enhanced community resilience to biotic and abiotic disturbances by facilitating the supply of essential services and external support during severe drought and desert locust invasions, their impact during socio-political disruptions, such as civil war and COVID-19 lockdowns, was limited and, in some cases, even increased vulnerability. Specifically, during the civil war, access roads enabled armed groups to reach villages more easily, leading to property destruction and heightened insecurity. We also found that a combination of attributes is necessary to effectively cope with and adapt to multiple disturbances. While attributes including conservation and saving of resources, established local leadership, knowledges and innovation, access to information and basic services help reduce the immediate effects of disturbances, other attributes such as mobilizing community labor force, remittance and livelihood diversification enable farmers to adapt and continue functioning under new, often changing conditions. Declarations Author Contribution Tewodros, Rogier and Miranda wrote the main manuscript text , Yu-feng under took remote sensing data analysis. all authors reviewed the manuscript. Data Availability https://doi.org/10.17026/SS/F7O51S References Adugna, A., Abegaz, A., & Cerdà, A. J. S. E. D. (2015). Soil erosion assessment and control in Northeast Wollega, Ethiopia. Solid Earth Discussions, 7(4), 3511-3540. Amede, T., & Lemenih, M. (2019). The highland mixed farming system of Africa: Diversifying livelihoods in fragile ecosystems. 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Asresehegn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYLACHiR2AhAzPgAz2fGoRtfCbABmMpOghU0CnxZ79h7DB2/32NgzsB9/9uHDH5s8fv61x6p52+4xmOOyheeMseGcZ2mJDTw5xjNntqUVS854l3abt62YwbIZhxaJ3G3SPAcOA92Tw8zM23A4ccONM2a3c9sSGAwO49Ai/xasxZ6B//lj5j9//ifuB2opxqtFgheshbFBIsGYmYHtQOIG/h4zZrxazuR/NpxzIC2xTeKNMWNvW3LijBs8xtJ/ziXw4NLC3n4s8cGbAzb2/Pzpjxl+/LFL7O8/Y/hxRlmCnMHxBux6YIANzpJIgNiPXz0K4D9AguJRMApGwSgYCQAASvBaxdeNvoQAAAAASUVORK5CYII=","orcid":"","institution":"Wageningen University and Research","correspondingAuthor":true,"prefix":"","firstName":"Tewodros","middleName":"G.","lastName":"Asresehegn","suffix":""},{"id":435062180,"identity":"47d3c4fd-c8d6-4979-8bc5-8df46e15f4c1","order_by":1,"name":"Miranda MEUWISSEN","email":"","orcid":"","institution":"Wageningen University","correspondingAuthor":false,"prefix":"","firstName":"Miranda","middleName":"","lastName":"MEUWISSEN","suffix":""},{"id":435062185,"identity":"7a8917a6-2cba-4dd7-af17-d3a6beca940c","order_by":2,"name":"Vivian Valencia","email":"","orcid":"","institution":"Bishop’s University","correspondingAuthor":false,"prefix":"","firstName":"Vivian","middleName":"","lastName":"Valencia","suffix":""},{"id":435062188,"identity":"b418d4c2-ba09-4f4c-94e8-92fb2735cfd0","order_by":3,"name":"Steffen Schulz","email":"","orcid":"","institution":"Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ)","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"","lastName":"Schulz","suffix":""},{"id":435062192,"identity":"0d1329f8-591a-44c4-bc7d-b5890197b268","order_by":4,"name":"Ichsani Wheeler","email":"","orcid":"","institution":"OpenGeoHub","correspondingAuthor":false,"prefix":"","firstName":"Ichsani","middleName":"","lastName":"Wheeler","suffix":""},{"id":435062194,"identity":"73987241-5144-4380-8944-18dc78228a95","order_by":5,"name":"Yu-Feng Ho","email":"","orcid":"","institution":"OpenGeoHub","correspondingAuthor":false,"prefix":"","firstName":"Yu-Feng","middleName":"","lastName":"Ho","suffix":""},{"id":435062197,"identity":"6485b55d-6adf-49d0-b302-684199552f86","order_by":6,"name":"Rogier P.O. Schulte","email":"","orcid":"","institution":"Wageningen University and Research","correspondingAuthor":false,"prefix":"","firstName":"Rogier","middleName":"P.O.","lastName":"Schulte","suffix":""}],"badges":[],"createdAt":"2025-03-14 14:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6227313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6227313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79571897,"identity":"208ab653-a27a-44ad-b833-e77a8ad731c9","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66623,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of land restoration outcomes of communities, solid and dashed lines indicate increasing and decreasing trend of restoration outcomes, respectively after shock\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/6190b6d8552c9a8c3ea0194c.png"},{"id":79571899,"identity":"1aab4b09-7625-4886-b495-f60d86fc6252","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128227,"visible":true,"origin":"","legend":"\u003cp\u003ethe result of non-linear model to predict above ground biomass from EVI yearly in Ethiopia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/accf2c49355e67b5810348df.png"},{"id":79572603,"identity":"133ac281-2367-4b33-898d-9dc03530d58b","added_by":"auto","created_at":"2025-03-31 10:54:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81865,"visible":true,"origin":"","legend":"\u003cp\u003eAgricultural shocks and level of influence on the farming system identified by communities\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/d463ba4054fcb69f3033ce47.png"},{"id":79571906,"identity":"231c9095-76c3-4232-a5af-a4fbed0cd8d6","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":303018,"visible":true,"origin":"","legend":"\u003cp\u003eCase study sites and identified agricultural shocks by region\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/cc4a5316a16a1b08b35d7f64.png"},{"id":79571902,"identity":"e230abb9-b186-4eba-b68a-2b38dcf0f1cb","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198530,"visible":true,"origin":"","legend":"\u003cp\u003eTrends land restoration outcomes (Above Ground biomass, Green Soil Cover Fraction, and Soil Moisture Content) in the face of multiple shocks across the case study community watersheds in Semiarid and Humid agroclimatic zones by performance groups\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/54d574a93bd4be01a132df1e.png"},{"id":79571901,"identity":"6842e076-e71a-458b-8bcb-e49f66810a8c","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":453966,"visible":true,"origin":"","legend":"\u003cp\u003eResilience attributes of Land Management Performance Groups during Sever Drought, Desert Locust Invasion, Civil War, Covid-19 Pandemic, and Combined (Desert locust invasion + Covid 19 pandemic + Civil war) Disturbances in the semiarid region in Tigray and humid areas in the Southern Regions. H-SRs= Humid- Southern Regions; SA-TR= Semi-arid Tigray Region; HH= Household; IPM= Integrated Pest Management; ISFM=Integrated Soil Fertility Management\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/3aeaa2f9496029dfca726764.png"},{"id":79572606,"identity":"61c8986c-7a94-4eff-a147-70ee2e8eb437","added_by":"auto","created_at":"2025-03-31 10:54:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":321836,"visible":true,"origin":"","legend":"\u003cp\u003eResponses of High-Performance Communities on the Contribution of Resilience Attributes Amid Multiple Shocks: Minimum, Maximum, and Median Values\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/a0caf77e11ea96814f54f74e.png"},{"id":79574382,"identity":"b151439d-ba9a-4ae7-aed7-b2fa99154602","added_by":"auto","created_at":"2025-03-31 11:10:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1998485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/b04b6b3a-42c2-4e8f-9f9c-3e9965e9892c.pdf"},{"id":79571898,"identity":"6382a08c-dc91-44b4-8086-83a12b0d4544","added_by":"auto","created_at":"2025-03-31 10:46:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17964,"visible":true,"origin":"","legend":"","description":"","filename":"Annex1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6227313/v1/eb7f91281238415cf345f665.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Designing resilient farming systems for a turbulent world: learning from communities at the frontline","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn today\u0026rsquo;s increasingly turbulent world, the ability of farming systems to adapt and transform\u0026mdash;referred to as resilience\u0026mdash;has become critically important. Rapid and unpredictable changes in environmental, social, and economic contexts, driven by both natural and human-induced catastrophes such as extreme droughts, floods, pest outbreaks, social unrest, and spiking commodity prices, pose significant challenges to farming systems worldwide (Nauck et al., 2021; Cvetković, 2024; Sharma \u0026amp; Pandey, 2024). These challenges often exceed the adaptive capacity of conventional farming systems. For example, the COVID-19 pandemic exposed the vulnerabilities of agricultural systems and disrupted food security globally, demonstrating the fragility of existing systems in the face of natural and manmade crises (Darnhofer, 2021; Meuwissen et al., 2021). The unpredictable nature of such crises underscores the need for innovative and transformative approaches to meet the growing demand for food, fiber, and energy amidst these uncertainties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResilience, as a concept, has thus gained prominence among policymakers, sustainability scientists, and development practitioners (Nauck et al., 2021; Shilomboleni et al., 2024). Unlike traditional notions of efficiency, which often prioritize short-term gains by minimizing redundancy, resilience emphasizes long-term sustainability by enabling systems to withstand, adapt to, and transform in response to challenges (Duchek, 2020; Golgeci et al., 2020; Meuwissen et al., 2019). At its core, resilience comprises three interconnected capacities: 1) robustness: or the ability to maintain system structure despite disturbances; 2) adaptability: the ability to adjust system structures to evolving challenges, and 3) transformability: the capacity for fundamental reconfiguration when existing systems are no longer viable (Folke et al., 2010; Darnhofer, 2014).\u003c/p\u003e\n\u003cp\u003eDrawing on socio-ecological and socio-psychological theories, resilience can be further categorized into two types: individual resilience and collective resilience. Individual resilience is influenced by personal traits such as optimism and coping strategies, as well as external support networks. In contrast, collective resilience emerges from shared resources, governance mechanisms, and a collective identity that fosters mutual support (Folke, 2006; Norris et al., 2008). These types operate along two dimensions: general resilience, which enables systems to address a wide array of challenges, including unexpected ones, and specific resilience, which focuses on the capacity to tackle specific expected challenges, e.g. flooding or drought (Folke et al., 2010). This theoretical framing emphasizes the multi-dimensional and context-dependent nature of resilience, requiring tailored approaches to address the unique challenges faced by farming systems.\u003c/p\u003e\n\u003cp\u003eResilience is an inherent yet latent characteristic of farming systems, making it difficult to predict their capacity to endure, adapt, and transform in response to unforeseen shocks and disturbances (Feindt et al., 2022). Traditionally, resilience has been assessed retrospectively\u0026mdash;measured only after farming systems experience one or multiple shocks (e.g., Toorop et al., 2023). Recent research has sought to complement this reactive approach by identifying ex-ante attributes indicative of resilient farming systems. In this regard, four key attributes have emerged: 1) Agency, the ability of system actors to make decisions and take action; 2) Buffering, the capacity to absorb disturbances while minimizing damage; 3) Connectivity, the physical and virtual linkages between system components; and 4) Diversity, the variety of components and processes that enhance adaptability and flexibility (Fonteijn et al., 2022; Meuwissen et al., 2022).\u003c/p\u003e\n\u003cp\u003eRecent studies indicated that these attributes operate systematically, influencing the entire farming system, and exhibited stability by maintaining core functions while allowing adaptation and evolution (Meuwissen et al., 2019; Duchek, 2020; Mathijs \u0026amp; Wauters, 2020). However, their specific appearance \u0026nbsp;vary over time, across regions, and at different scales (Feindt et al., 2022). Several resilience studies emphasized on the relationship between attributes and resilience capacity of farming systems with focus on large farms in Europe (Nera et al., 2020; Paas et al., 2021a; Reidsma et al., 2020). These investigations highlight the interconnectedness of the resilience attributes to cope with, adapt to, and transform in response to disturbances.\u003c/p\u003e\n\u003cp\u003eNotably, Reidsma et al. (2023) highlighted the likely change of the contribution of the attributes when the current European farming system transition to alternative future farming models to cope with unknown shocks. Their findings revealed that shifts in farming system can alter the importance of the attributes. Despite these advancements, two key knowledge gaps remain: 1) which resilience attributes are most critical for the collective resilience of smallholder farming systems, and 2) how the contribution of these attributes change in response to multiple concurrent disruptions. Addressing these gaps is essential for designing more resilient farming systems, particularly in regions where smallholder agriculture plays a dominant role.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe chose to address these gaps by studying the resilience of communities that have proven to be resilient by thriving despite their exposure to multiple different- abiotic, biotic, and socio-political shocks. The selected communities have been participating in the Ethiopian National Sustainable Land Management flagship Program (SLMP) which is currently operating in more than 3000 community watersheds (\u003ca href=\"http://nrdsmis.moa.gov.et/app/landing\"\u003ehttp://nrdsmis.moa.gov.et/app/landing\u003c/a\u003e), each of which comprises a natural drainage area covering on average 500 ha (Mugoro et al., 2020). The SLMP is aimed at reducing land degradation and enhancing agricultural productivity (Schmidt and Tadesse 2019; Ponce et al., 2021)\u003c/p\u003e\n\u003cp\u003eLand degradation, encompassing the depletion of soil, water, and vegetation resources, poses a significant threat to agricultural productivity and global food security (IPCC, 2019; FAO, 2021). In response, community-led land management initiatives have emerged as critical strategies for enhancing soil health, boosting agricultural productivity, and meeting the growing demand for food, fiber, and energy (FAO, 2016; O\u0026apos;Donoghue et al., 2022). These efforts improve key land functions, such as soil cover, moisture retention, and biomass production (Saleem, 2019; Wei et al., 2021; Erkossa et al., 2022). Improved soil cover protects against erosion and enhances soil quality, while increased moisture and biomass directly contribute to land productivity (Lozano‐Parra et al., 2018).Therefore, we chose soil moisture content, permanent soil cover, and increased biomass as positive Key Performance Indicators (KPIs), for successful land restoration, reflecting advances in land productivity and the capacity to protect soil erosion (Edward et al., 2019; Belayneh et al., 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter a decade of implementation (2012\u0026ndash;2021), not all 3,000 communities have been equally successful in the restoration of their land resources, as measured by the proportion of watershed area under sustainable land management. \u0026nbsp;In our previous study (Asresehegn et al., 2025, under review), we identified the unique features of the highly successful-land restoration bright spots\u0026mdash;communities with exceptionally high restoration performance (Valencia et al., 2022). We showed that these communities have been characterized by self-organization, strong local leadership, and active community participation in the design and implementation of land management programs a (Asresehegn et al., 2025, under review).\u003c/p\u003e\n\u003cp\u003eOur current study focused on 12 community watersheds\u0026mdash;six highly successful (\u0026quot;bright spots\u0026quot;) and six that have been unsuccessful in land restoration performance. Both sets of community watersheds have achieved these results amidst recurring disruptions that have included severe drought, the COVID-19 pandemic, desert locust invasions and armed conflict. In some instances, two or three shocks coincided.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Using the trend of land restoration outcomes derived from mutli-source independent earth observation data, We assessed the extent to which the outcomes of the land restoration actions were influenced by unexpected biotic, abiotic, or sociopolitical disruptions. These disruptions may challenge sustainable land management by depleting resources or redirecting their use for immediate needs. At the same time, such challenges can also drive innovation, prompting communities to adopt adaptive strategies and reaffirm commitments to sustainable practices (M\u0026aring;ren et al., 2022; Frietsch et al., 2023). Figure 1 demonstrates the conceptual understanding on the interplay between restoration actions, disruptions, and possible restoration outcomes. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the contrast in land restoration outcomes between successful and unsuccessful communities in the face of these multiple biotic, abiotic and socio-political disturbances provided valuable opportunities for understanding the trend of the land restoration outcomes of the communities in the face of these challenges and the relative importance of the resilience attributes to coping with multiple concurrent shocks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, this study specifically aims to: i) assess the land restoration outcomes of communities in the face of multiple shocks; ii) understand the relative importance of agency, buffers, connectivity and diversity in coping with the impact of multiple shocks; iii) identify key attributes required for resilience of smallholder farming system to unknown disturbance.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Assessment of land restoration outcomes\u003c/h2\u003e\n \u003cp\u003eWe utilized observational data combined with participatory research. Observational data included analysis of available Earth Observation (EO) data from multiple independent sources on the aforementioned KPIs of the selected community watersheds: above ground biomass, soil moisture content, and green soil cover fraction for the period 2000 to 2023. Participatory approaches involved focus group discussions with community watershed members to capture experiential and context-specific insights. These methods were complementary: the observational data provided objective data on the trend of land restoration outcomes in the face of the multiple disturbance, while the participatory approach offered nuanced insights into how diverse communities respond to shocks and the relative importance of resilience attributes in mediating the extent of impact of individual and multiple shocks on restoration outcomes.\u003c/p\u003e\n \u003cp\u003eTo assess the restoration outcomes of the selected communities, we focused on the three KPIs: green soil cover fraction (the proportion of months in a year that soil remains covered by vegetation), soil moisture content, and above ground biomass. Using these variables derived from mutli-source independent EO data, we analyzed these metrics on a yearly basis starting in 2000 for the 12 case study communities. The analysis was conducted using the following approaches:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003ei. Green Soil Cover Fraction\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eGreen cover fraction was derived from Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team (GLAD) at the University of Maryland (Potapov et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The dataset harmonizes Landsat 5 TM, Landsat 7 ETM\u0026thinsp;+\u0026thinsp;and Landsat 8 OLI/TIRS, and Landsat 8 OLI/TIRS. Landsat is the only high resolution which has acquired high quality and consistent\u0026thinsp;~\u0026thinsp;30m resolution earth observation data back from 2000 to 2022 around the globe (Consoli et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). During this analysis, the satellite image of the study sites for 2023 was not available.\u003c/p\u003e\n \u003cp\u003eA bimonthly product (i.e. one image per two months) was derived for the study watershed for each year for the period 2000 to 2022 using weighted temporal aggregation, where weights are assigned based on the clear sky fraction. This approach helped to remove the noise as well as collect a stable seasonality trait of each pixel (Consoli et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tian et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe green soil cover fraction was derived using the concept of the bare soil fraction index. The bare soil fraction was calculated by dividing the number of pixels classified as bare surface within a year\u0026rsquo;s time series (identified by NDVI values below 0.35) by the total number of pixels analyzed in that year (Potapov et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Subsequently, the green soil cover fraction was defined as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:Green\\:Soil\\:cover\\:fraction={\\sum\\:}_{i=1}^{n}\\left(1-\\frac{X(NDVI\\le\\:0.35)}{N}\\right)x100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;total number of pixels analyzed in the watershed for a given year i\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eX(NDVI\u0026thinsp;\u0026ge;\u0026thinsp;0.35)\u0026thinsp;=\u0026thinsp;the number of pixels with NDVI values below 0.35 for the given year\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;number of observations in a given year i\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\u003cspan\u003e\n \u003cp\u003eii. Soil Moisture Content\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eWe utilized the Global Land Surface Satellite (GLASS) soil moisture product (available for 2000\u0026ndash;2020) to monitor changes in soil moisture content within the uppermost soil layer (0\u0026ndash;5 cm) of the study watershed. During the assessment period (conducted in October 2024), GLASS images were unavailable for 2021\u0026ndash;2023.\u003c/p\u003e\n \u003cp\u003eThe soil moisture data were generated using an ensemble machine learning approach that integrated multiple datasets, including surface reflectance, in-situ soil moisture observations, European reanalysis (ERA-5-Land) soil moisture products, and auxiliary data such as DEM and soil information from Soil Grids (Pablos et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although the data were not derived from direct observation, they provided extensive spatial coverage and exhibited high spatiotemporal consistency (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, the soil moisture data generated using the machine learning were resampled to a 30-meter resolution to align with the pixel size of Landsat-derived products.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003eiii. Above Ground Biomass\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eWe applied a method developed by Liu et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) to estimate aboveground biomass (AGB) using satellite-derived passive microwave instruments. According to Liu et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), Vegetation Optical Depth (VOD) has a nonlinear correlation with aboveground biomass. Enhanced Vegetation Index (EVI) images from the years 2000 to 2023, which were available during the assessment period (October 2024), were obtained for the study sites. These data, derived from the MODIS MOD13Q1 product in Google Earth Engine, provide 16-day composite EVI observations.\u003c/p\u003e\n \u003cp\u003eTo ensure data quality, we extracted stable signals and filtered noise on a yearly basis using a pixel-wise weighted Savitzky-Golay smoothing filter from phenofit, a R package (Kong et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subsequently, we fitted a non-linear model by resampling 250-meter EVI data into a 0.025-degree (~\u0026thinsp;10 km) grid, averaged to align with the VOD data. The relationship between EVI and aboveground biomass was modeled as follows:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:AGB=a*\\text{arctan}\\left(b*\\left(EVIy-c\\right)\\right)+d$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eAGB: Above Ground Biomass\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003ea,b,c,d: Model parameters\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eEVIy: Enhanced Vegetation Index for year y\u003c/em\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe model is calibrated within the bounding box of Ethiopia. The fitting result shows R^2\u0026thinsp;=\u0026thinsp;0.87 and is able to simulate the steep increase of biomass with higher EVI values running concurrently with high biomass forests. EVI values do not exhibit the same high biomass saturation as suffered by NDVI based metrics (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Once the model parameters were determined, it was applied to the 250-meter yearly EVI sum at the watershed level to produce a 250-meter resolution time series of aboveground biomass for the years 2000 to 2023.\u003c/p\u003e\n \u003cp\u003eFinally, we compiled yearly data for the above three key land restoration performance Indicators (KPI)\u0026mdash;green soil cover fraction, soil moisture content, and above ground biomass\u0026mdash;for the case study community watersheds. The annual median values of each parameter were used to analyze the trends and compare among community watersheds, categorized into high- and low-performance groups based on proportion of watershed area under SLM. These comparisons were conducted within the semi-arid Tigray region and the humid Southern region of Ethiopia, both of which have experienced varying shocks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Assessment of resilience attributes\u003c/h2\u003e\n \u003cp\u003eWe assessed agency, buffering, connectivity, and diversification as the key attributes that enable food systems to adapt, respond, or transform in the face of sudden disruptions (de Steenhuijsen Piters et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fonteijn et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Adding to previous approaches to assess resilience attributes of farming systems (Cabell \u0026amp; Oelofse, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Meuwissen et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paas et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; San Mart\u0026iacute;n et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), we used focus group discussions (FGDs) with community representatives, members of the community watershed development executive team members formally elected by the community, to identify the attributes that helped farming systems cope with specific disruptions, and to evaluate the relative important of the attributes across multiple disruptions.\u003c/p\u003e\n \u003cp\u003eA total of 12 FGDs were conducted, each comprising 8 to 10 members of the community watershed development executive team members selected based on their willingness and availability during the data collection period (January to March 2024). Drawing on literature and input from key informants, the first author identified several major disruptions in the last two decades that affected farming systems in northern and southern Ethiopia. These included severe droughts in 2002/2003 and 2015/16 (Singh et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), Desert Locust outbreaks during the 2019/20, 2020/21, and 2021/22 cropping seasons (Nandelenga \u0026amp; Legesse, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ilukor \u0026amp; Gourlay, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the Civil War from late 2020 to 2022 in Tigray (Nyssen et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the COVID-19 pandemic from 2020 to 2021 (Lanyero et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) across all regions of the country.\u003c/p\u003e\n \u003cp\u003eTo understand the perceived influence of these disruptions on farming systems, each FGD was tasked with rating the impact of these events in their respective communities using a scale of 1 (very low) to 5 (very high). Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the median ratings of the FGDs in Tigray for the impact of drought, desert locust invasion, and civil war, as well as the median ratings of the FGDs across the two regions for the impact of the COVID-19 pandemic on farming systems.\u003c/p\u003e\n \u003cp\u003eEach group was then asked, \u0026quot;What resilience attribute enabled you to cope with these disruptions?\u0026quot; We did not limit the FGDs to a predefined set of ABCD resilience attributes, allowing participants to explore their local resilience experiences. After extensive brainstorming, the FGDs identified 35 resilience features, after which response saturation was reached. These features were further refined by each FGD community for relevance to their specific watersheds. Ultimately, 28 features were consistently recognized by at least seven of the 12 communities (over 50%).\u003c/p\u003e\n \u003cp\u003eThe first author categorized these 28 features into the four resilience attributes\u0026mdash;agency, buffering, connectivity, and diversity\u0026mdash;based on their characteristics. This classification included Eight features under agency, Eleven under buffering, Nine under connectivity, and Seven under diversity (see Annex 1). Following this, each attribute was elaborated and contextualized with input from FGD participants. The groups then rated the contribution of each attribute to coping with disruptions over the past decade, reaching a consensus on their ratings. A Likert scale (0\u0026ndash;5) was used, where 0 indicated no contribution and 5 indicated very high contribution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Description of case study sites\u003c/h2\u003e\n \u003cp\u003eThe case study communities are located in the semiarid Tigray region of northern Ethiopia and the humid areas of southern Ethiopia. These regions represent smallholder mixed crop-livestock systems typical of the Ethiopian highlands (Amede et al., 2019). Farming in these areas depends heavily on watershed resources such as land, water, and vegetation. The watersheds encompass both cultivated lands - with farm sizes averaging less than 1 hectare per household for crop production - and communal areas, such as forest patches, pasturelands, and bushlands. While cultivated lands are individually managed, the communal areas are collectively used for livestock grazing, fuelwood collection, and other ecosystem services.\u003c/p\u003e\n \u003cp\u003eUnder Ethiopian law, all land is public property; farmers have usufruct rights but cannot permanently transfer ownership (Tareke, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Agricultural inputs such as fertilizers, seeds, pesticides, and veterinary services are primarily provided through government agencies at subsidized cost. However, challenges persist in ensuring the timely and high-quality supply of these inputs (Leta et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eLand degradation is a significant challenge in these watersheds, driven by both biophysical and socioeconomic factors. Biophysical causes include undulating topography, intense rainfall, and erosion-prone soils. Socioeconomic drivers, on the other hand, include poor resource governance, political instability, overgrazing, and unsustainable vegetation use (Brhane \u0026amp; Mekonen, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Adugna et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kassa et al., 2016). These factors collectively undermine the productive capacity of land and exacerbate rural poverty.\u003c/p\u003e\n \u003cp\u003eTo combat land degradation, communities in these watersheds are organized into watershed user cooperatives. These cooperatives play a pivotal role in the restoration and sustainable management of land, water, and vegetation resources under the Ethiopian government\u0026rsquo;s Sustainable Land Management Program (SLMP) initiatives (FAO, 2022). Oversight is provided by watershed development executive committees\u0026mdash;hereafter referred to as executive teams\u0026mdash;elected by community members with the support of district and community-level extension workers.\u003c/p\u003e\n \u003cp\u003eThe executive teams represent diverse social groups, including women, landless youth, farmers, water users, beekeepers, the elderly, and religious leaders. This inclusivity ensures that watershed management decisions reflect the needs and priorities of the broader community. The teams are responsible for assessing watershed challenges, preparing mitigation measures, drafting community bylaws on resource use, resolving conflicts, mobilizing local resources, and monitoring the implementation of restoration activities (Ministry of Agriculture, 2020; Asresehegn et al., under review).\u003c/p\u003e\n \u003cp\u003eOver the past 12 years(2012\u0026ndash;2023), these communities have implemented various land management practices with contrasting performance levels in terms of area under sustainable management.\u003c/p\u003e\n \u003cp\u003eDuring the intervention period from 2012 to 2023, six bright spot communities\u0026mdash;three from the semi-arid North and three from the humid South\u0026mdash;achieved sustainable land management coverage of 88\u0026ndash;93% of their watershed areas. In contrast, six low-performing communities from the same regions managed only 18\u0026ndash;47% coverage. The high performing communities, referred here \u0026ldquo;bright spots,\u0026rdquo; achieved this success by prioritizing vegetation restoration and adapting their farming systems to address evolving environmental challenges (Asresehegn et al., under review).\u003c/p\u003e\n \u003cp\u003eThe case study sites in the Tigray region faced compounded challenges, including severe droughts, the COVID-19 pandemic, desert locust outbreaks, and civil conflict, all of which disrupted farming systems and restoration efforts. In contrast, communities in southern Ethiopia only identified COVID-19 as their primary challenge during the same period (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Trend of Land Restoration Outcomes\u003c/h2\u003e\n \u003cp\u003eThe trend analysis of soil moisture content, green soil fraction, and above ground biomass revealed diverging impacts of sudden disturbances on land restoration outcomes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the semiarid region of Tigray, disturbances such as the severe drought in 2015, desert locust invasions (2019\u0026ndash;2021), the Covid-19 outbreak (2020\u0026ndash;2022), and the civil war (2020\u0026ndash;2022) disrupted restoration efforts. Similarly, in the humid southern regions, the Covid-19 outbreak (2020\u0026ndash;2022) was the primary disturbance affecting outcomes. The analysis compared the effects of these events across high- and low-performing communities in land management.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cem\u003ea. Soil Moisture Content\u003c/em\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eIn the semiarid region of Tigray, soil moisture content declined during the severe drought of 2015, affecting both high- and low-performing communities. However, the bright spot communities regained and consistently improved the soil moisture content during the subsequent years including during the desert locust invasion (2019\u0026ndash;2020), while it gradually diminished in low-performing ones during the same period.\u003c/p\u003e\n \u003cp\u003eIn contrast, in the humid southern regions, where no major disturbances were observed except the Covid-19 outbreak in the end of 2020, soil moisture remained relatively stable in both high- and low-performing communities. A slight decline was observed in low-performing communities between 2018 and 2020, but no major changes were noted in bright spot communities during the same period.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cem\u003eb. Green Soil Cover Fraction\u003c/em\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe green soil cover fraction exhibited similar trends to soil moisture in the semiarid region of Tigray. During the severe drought of 2015, green soil cover declined in both high- and low-performing communities. However, from 2019 to 2022, during the multiple disturbances including desert locust invasion, the Covid-19 outbreak, and the civil war, the green soil fraction showed an increasing trend in high performing communities while it consistently declined in low-performing communities.\u003c/p\u003e\n \u003cp\u003eIn the humid southern regions, the green soil fraction remained consistent, with slight variations between high- and low-performing communities during the COVID-19 outbreak (2020\u0026ndash;2022), the primary disturbance in the region. During this period, the average soil moisture content in low-performing communities showed a slight decline compared to that in bright spot communities.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cem\u003ec. Above Ground Biomass\u003c/em\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eUnlike soil moisture and green soil fraction, above ground biomass in the semiarid region of Tigray remained stable during the severe drought of 2015 for both high- and low-performing communities. However, during the 2019 to 2022 multiple sudden disturbance of desert locust invasion, the Covid-19 outbreak, and the civil war, above ground biomass increased in bright spot communities but remained stable in low-performing ones.\u003c/p\u003e\n \u003cp\u003eIn the humid southern regions, above ground biomass in bright spot communities increased steadily during the Covid-19 outbreak (2020\u0026ndash;2022). In contrast, low-performing communities experienced a consistent decline in above ground biomass over the same period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Resilience attributes of land management performance groups to multiple shocks\u003c/h2\u003e\n \u003cp\u003eThe Focus Group Discussions attributed varying levels of importance to each of the 28 resilience attributes, depending on the type of disturbance, levels of land management performance, and agroclimatic regions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The communities across the semiarid and humid regions consistently identified agency, buffers, connectivity, and diversity as critical resilience attributes to cope with the impacts of COVID-19 pandemic. Each attribute is captured by several key indicators including local leadership, technical advisors, and self-organization for agency; safety nets and cash savings for buffers; access to information and communication infrastructure for connectivity; and integrated pest and soil fertility management for diversity\u003c/p\u003e\n \u003cp\u003eDifferences in emphasis in the attributes emerged between the bright spots and low-performing communities. While bright spot communities consistently recognized the relatively higher contribution of community leadership and self-organization capacities in both regions, the importance of these attributes varied among low-performing communities across regions.\u003c/p\u003e\n \u003cp\u003eFurthermore, buffers such as the use of community resources, formal and informal safety nets, and the preservation of feed and food were recognized as highly important by bright spot communities in both regions for coping with the impact of COVID-19. In contrast, low-performing communities prioritized attributes differently. For example, low-performing communities in the humid southern regions identified formal and informal safety nets as highly important, whereas those in the semi-arid Tigray region placed greater emphasis on the preservation of feed and food.\u003c/p\u003e\n \u003cp\u003eRegional differences across the same performance groups were also evident. In the humid regions, bright spot communities prioritized agency attributes, such as information providers, local leadership, and ambitions for change. Conversely, bright spot communities in the semiarid regions of Tigray placed greater emphasis on buffering mechanisms, including water conservation, multiple cropping, and community labor forces..\u003c/p\u003e\n \u003cp\u003eIn the face of multiple shocks, such as severe drought, desert locust invasions, civil war, and COVID-19, notable differences in resilience attributes of the communities emerged. Agency and buffering attributes were consistently recognized critical to coping with these challenges in the semi-arid in Tigray region. However, the contribution of connectivity and diversity attributes varied depending on the type of shock. For example, connectivity attributes, such as access to roads, communication infrastructure, and basic services, were vital in managing environmental shocks like drought and locust invasions. Yet, these were less effective during socio-political shocks like the civil war. Similarly, diversification strategies (e.g., crop and livelihood diversification) proved effective for environmental and political shocks but were less impactful during the COVID-19 pandemic.\u003c/p\u003e\n \u003cp\u003ePerformance-based differences were also observed in mitigating the impact of combined shocks. Bright spots demonstrated the effective use of a broad range of agency and buffering attributes. Meanwhile, low-performing communities relied more heavily on specific buffering and connectivity mechanisms.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings of this study revealed divergent trends in land restoration outcomes among communities exposed to multiple shocks. Bright spot communities exhibited overall improvements in soil moisture content, green soil cover, and aboveground biomass despite severe drought, the COVID-19 pandemic, desert locust infestations, and civil war, indicating the general resilience of their farming systems. The findings also highlighted the resilience attributes identified by the community as necessary in mitigating the impacts of multiple shocks. Notably, agency and buffers were required critical across all shocks, emphasizing their role in enhancing the resilience of smallholder farming systems to future disturbances. We discussed the analysis result of the land restoration outcomes restoration outcomes, the resilience attributes that enabled communities to cope with multiple shocks, and implications for anticipated future unknown disruption.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Resilience of Community Land Restoration Outcomes to Drought\u003c/h2\u003e \u003cp\u003eOur results highlighted the greater sensitivity of soil moisture and green soil cover to drought conditions compared to the buffered response of above-ground biomass. Above-ground biomass, which includes living plant material such as stems, leaves, and reproductive structures, appeared more resilient to short-term drought stress. This observation resonates with previous studies, such as Li et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that reduced soil moisture during droughts in semiarid regions severely impacts plant water availability, often leading to diminished green soil cover as vegetation becomes stressed or dies off. Similarly, de Meira Junior et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that above-ground biomass demonstrates a delayed response to short-term drought due to its capacity to utilize residual water resources, thus buffering its reaction to initial drought stress.\u003c/p\u003e \u003cp\u003eThe analysis also showed that severe drought had similar effects on soil moisture and green cover across both bright spot and low-performing communities in the semiarid region. This uniform response likely results from the limited implementation of land management measures during the early stages of the Sustainable Land Management (SLM) Program, which began in 2012. By 2015, when the drought occurred, most bright spot communities were still focused on establishing governance rules, resource-sharing agreements, and conflict resolution mechanisms, as noted in earlier research (Asresehegn et al., under review). These preparatory activities typically precede the on-ground implementation of land management practices. As a result, the capacity of communities to mitigate the immediate effects of the drought were similar for high and low-performing communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Resilience of Community Land Restoration Outcomes to multiple shocks\u003c/h2\u003e \u003cp\u003eThe distinct trend of restoration outcomes in bright spots and low-performing communities, particularly after 2018, can be partly explained by the impact of multiple concurrent disturbances such as desert locust invasions, the COVID-19 pandemic, and armed conflict in the semiarid regions, with the pandemic also affecting the humid areas. In the absence of coordinated and appropriate community action, desert locusts, known for their capacity to consume vast amounts of vegetation, could lead to significant reductions in green soil cover and above-ground biomass (Odhiambo et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the COVID-19 pandemic disrupted labor availability, supply chains, and market access, exacerbating the challenges faced by smallholder communities and hindering their ability to manage land effectively (Rahaman et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Armed conflicts could further complicate this scenario by diverting resources and attention away from land restoration efforts, thus aggravating the negative impacts of these disturbances (Hassoun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBright spot communities demonstrated the resilience capacity to cope with these shocks through their consistent improvements in soil moisture, green soil cover, and above-ground biomass, which were directly linked to the extensive SLM practices implemented in the watersheds. During focus group discussions, bright spot communities consistently explained, \"Our long-term engagement in the conservation and sustainable management of the land, water, and vegetation resources helped us to expand the land area of the watershed under irrigation and produce two to three crop cycles a year during the crises. Furthermore, we developed local solutions to mitigate the impact of desert locusts.\" These attributes, highlighted by the bright spots, underscore the significance of sustainable land management interventions and local knowledge to adapt to evolving challenges, in line with Haile et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and with Kerner et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who reported on the resilience of farmers in Tigray during the war.\u003c/p\u003e \u003cp\u003eDuring one of the discussions, a community leader in Tigray region, noted that while disturbances generally pose challenges, they also present opportunities for innovation. He explained how floods, once a major disaster for farming in his village, had become an opportunity: \"We have learned how to effectively store and use floods after the severe drought.\" Studies support this adaptive approach, showing that with better coordination, stronger partnerships, and support from local agencies, communities can develop more robust strategies that enhance long-term productivity and sustainability (Nyawira et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; FAO, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shilomboleni et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Relative importance of resilience attributes to cope with multiple shocks\u003c/h2\u003e \u003cp\u003eOur analysis showed that, across various disturbances, bright spot communities consistently recognized the importance of key resilience attributes, including agency (local innovations, leadership, self-organization), buffering (water conservation and storage; and access to local savings). However, the contribution of connectivity and diversity attributes varied across disturbances. During focus group discussions, community members in the semiarid areas, who experienced multiple shocks, explained that while connectivity attributes such as access to roads and basic services were important in coping with the impacts of severe drought and desert locusts, their role was minimal or even negative during times of conflict and the COVID-19 pandemic, as these factors increased vulnerability, enabling soldiers to reach villages rapidly.\u003c/p\u003e \u003cp\u003eWhile physical connectivity such as roads and access to basic services faced limitations during the covid 19 pandemic, virtual connectivity - such as communication infrastructure, remittances, and access to information - played a vital role in maintaining social ties, knowledge exchanges, and coordination of activities. Budd et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Pattanasri et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also found that during the lockdown due to COVID-19, virtual communication significantly contributed to maintaining social connections, accessing information, and coordinating efforts among communities. However, both physical and virtual connectivity, except these within the community, were not functional or played negative role during the civil war.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the resilience attributes observed in bright spot communities across multiple shocks, and shows the varying significance of individual agency, buffering, connectivity and diversity attributes to cope with the impact of multiple shocks. The result highlights that a minimum number of agency and buffering attributes are always required. For connectivity, it shows that exposure to information, inter-community linkages, and basic services are important to cope with multiple disturbance. For diversity, there is universal requirement for livelihood diversification, alternative energy sources, and integrated pest and soil fertility management. These findings challenge the prevailing notion that resilience attributes are stable and systemic (Meuwissen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Duchek, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mathijs \u0026amp; Wauters, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van der Lee et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies have emphasized that resilience attributes are interrelated, suggesting that the four attributes function systematically within farming systems and are collectively necessary for coping with shocks. Furthermore, these studies have argued that resilience attributes remain stable, implying that their importance in maintaining core functions of farming systems are consistent. However, our findings indicate that high-performing (bright spot) communities sustained positive land restoration outcomes amid multiple concurrent shocks while relying minimally on agency and buffers. Additionally, we observed that the relative importance of resilience attributes in maintaining the function of the farming systems varied across different types of shocks. These results highlight the need for further research across diverse contexts to better understand the dynamic interplay between resilience attributes.\u003c/p\u003e \u003cp\u003eThe results also revealed that a combination of proactive and reactive approaches are required for communities to cope with multiple shocks. Proactive approaches, such as enhancing local leadership capacities, fostering local innovations, self-organization, water conservation and storage, and food preservation, helped communities anticipate disruptions and provide relief. In contrast, reactive approaches\u0026mdash;such as utilizing community resources, mobilizing forces, accessing information, and using labor-saving farming techniques\u0026mdash;helped them communities adapt to new challenges. These findings align with recent studies on organizational resilience, which emphasize the importance of balancing proactive and reactive strategies to cope with both predicted and unpredicted disturbances (Denyer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pinkwart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Anticipating the next, unknown shock\u003c/h2\u003e \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the essential attributes to cope with biotic, abiotic and socio-political shock, its insights may help communities to anticipate and plan for the next shocks, even when their timing or nature is yet unknown. Our findings show that community leadership, community resources, formal/informal safety nets, buffers of food and feed, and access to information were consistently critical to cope with all shocks, regardless of their nature, followed by the availability of technical advice, an ambition for change, self-organization, water conservation, a communal working force and labor-saving crops. These findings are of direct relevance to help communities prioritize actions to enhance their \u003cem\u003eex-ante\u003c/em\u003e resilience to known or unknown shocks. The extent to which these \u0026lsquo;essential attributes\u0026rsquo; are specific to farming communities in the Ethiopian Highlands, or generic to multiple smallholder farming systems across the global South is subject to ongoing studies involving bright spot farming communities in the Global Network of Lighthouse Farms.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study addresses the question of how communities respond in the face of multiple shocks. By analyzing the land restoration outcomes in the face of diverse and compounding biotic, abiotic and socio-political shocks, we were able to assess how resilience attributes\u0026mdash;agency, buffers, diversity, and connectivity\u0026mdash;varied in terms of their relative importance depending on the type of shock the community faced. In our previous study (Asresehegn et al., 2025, under review), we identified strong local leadership and self-organized communities that are actively engaged in the design and implementation of land management measures in their watershed, as key features of successful land restoration \"bright spot\" communities. These characteristics enabled them to accelerate the implementation of sustainable land management practices, primarily focused on vegetation regeneration, with the intended goal of transforming farming systems. In this study, we addressed the question of how shocks disrupt these land management outcomes and how different resilience attributes mediated the extent of the impact of shocks on restoration outcomes\u003c/p\u003e \u003cp\u003eOverall, we find that drought negatively affected soil moisture content and green soil cover, while above-ground biomass showed better resilience to short-term drought conditions. These findings emphasize the importance of prioritizing on-the-ground implementation of land management, particularly tree-based interventions such as agroforestry and agro-silviculture alongside strengthening leadership, technical advisory support, and the self-organization capacity of communities, as crucial for building resilience to disturbances, especially in the early stages of land management.\u003c/p\u003e \u003cp\u003eBright spot communities recognized that agency and buffers are essential for sustaining and improving land management outcomes, enabling farming systems to withstand multiple, unpredictable disturbances. In this study, agency was associated with local leadership, innovation, farmer technicians, local information providers, and community self-organization, while buffers were defined by accumulated local resources, including water, infrastructure, savings, and stored produce, which communities could rely on during shocks.\u003c/p\u003e \u003cp\u003eThe roles of connectivity and diversity were found to be context-dependent. For example, while access roads enhanced community resilience to biotic and abiotic disturbances by facilitating the supply of essential services and external support during severe drought and desert locust invasions, their impact during socio-political disruptions, such as civil war and COVID-19 lockdowns, was limited and, in some cases, even increased vulnerability. Specifically, during the civil war, access roads enabled armed groups to reach villages more easily, leading to property destruction and heightened insecurity.\u003c/p\u003e \u003cp\u003eWe also found that a combination of attributes is necessary to effectively cope with and adapt to multiple disturbances. While attributes including conservation and saving of resources, established local leadership, knowledges and innovation, access to information and basic services help reduce the immediate effects of disturbances, other attributes such as mobilizing community labor force, remittance and livelihood diversification enable farmers to adapt and continue functioning under new, often changing conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTewodros, Rogier and Miranda wrote the main manuscript text , Yu-feng under took remote sensing data analysis. all authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ehttps://doi.org/10.17026/SS/F7O51S\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdugna, A., Abegaz, A., \u0026amp; Cerd\u0026agrave;, A. J. S. E. D. (2015). Soil erosion assessment and control in Northeast Wollega, Ethiopia. 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Environmental Research Letters, 17(5), 051002.\u003c/li\u003e\n\u003cli\u003evan der Lee, J., Kangogo, D., G\u0026uuml;lzari, Ş. \u0026Ouml;., Dentoni, D., Oosting, S., Bijman, J., \u0026amp; Klerkx, L. (2022). Theoretical positions and approaches to resilience assessment in farming systems. A review. Agronomy for Sustainable Development, 42(2), 27.\u003c/li\u003e\n\u003cli\u003eWei, X., Zhou, Q., Cai, M., \u0026amp; Wang, Y. (2021). Effects of vegetation restoration on regional soil moisture content in the humid karst areas\u0026mdash;A case study of Southwest China. Water, 13(3), 321.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Liang, S., Ma, H., He, T., Wang, Q., Li, B., ... \u0026amp; Xiong, C. (2023). Generation of global 1-km daily soil moisture product from 2000 to 2020 using ensemble learning. Earth System Science Data Discussions, 2023, 1-37.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Agriculture](https://www.nature.com/npjsustainagric/)","snPcode":"44264","submissionUrl":"https://submission.springernature.com/new-submission/44264/3","title":"npj Sustainable Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Farming systems, Resilience, resilience attributes, ABCD, Bright spots ","lastPublishedDoi":"10.21203/rs.3.rs-6227313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6227313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eIn a rapidly changing world, designing resilient farming systems is critical. Recent socio-ecological research hypothesized that the general resilience of farming system to disturbances is related to the interplay between four key resilience attributes—Agencies, Buffers, Connectivity, and Diversity (ABCD). However, the relative importance of these attributes in coping with multiple concurrent disturbances remains unclear. This study leverages longitudinal socio-ecological data, including biotic, abiotic and socio-political shocks and community responses, to explore how the ABCD attributes mediate farming system resilience. Using satellite-derived soil moisture content, green soil cover, and aboveground biomass data, complemented by focus group discussions in twelve communities, we analyzed the land restoration outcomes in the face of multiple disturbance and the contributions of ABCD attributes to resilience. The findings revealed that “bright spot” communities that had already been improving their natural resource management were consistently more resilient to multiple shocks. Our results also show that attributes A and B are essential to cope with multiple disturbances, while the contributions of C and D were more nuanced and depended on the type of disturbance.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Designing resilient farming systems for a turbulent world: learning from communities at the frontline","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 10:45:59","doi":"10.21203/rs.3.rs-6227313/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-21T13:07:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T16:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48360242493286841380778425084094439337","date":"2025-08-11T19:37:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T02:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244834001287019599806669122482849360302","date":"2025-04-21T18:59:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-22T12:46:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T23:36:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T11:33:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Sustainable Agriculture","date":"2025-03-14T14:30:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Agriculture](https://www.nature.com/npjsustainagric/)","snPcode":"44264","submissionUrl":"https://submission.springernature.com/new-submission/44264/3","title":"npj Sustainable Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cf2761fc-3f4e-45c5-8cb0-0b86b1fe4a93","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46327895,"name":"Earth and environmental sciences/Ecology"},{"id":46327896,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-04T22:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 10:45:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6227313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6227313","identity":"rs-6227313","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}