How to support climate resilience in smallholder agricultural systems remains a topic of uncertainty and debate among researchers and development organizations (Hansen et al. 2019, Tomich et al. 2019a). Institutional interventions such as microinsurance schemes have recently gained traction as tools for agricultural development and poverty reduction in the Global South (Hazell et al. 2010, SwissRe 2013, Kramer et al. 2019). Simultaneously, there is an increasing drive for ecological intensification to sustain or enhance both livelihoods and natural resources (Bommarco et al. 2013, FAO 2018, HLPE 2019). Such financial and ecological strategies both act as a form of insurance by reducing risk in agricultural production, yet they function through distinct mechanisms: ecological insurance improves ecological functioning to stabilize and increase production over time, whereas financial insurance stabilizes agricultural income on a seasonal basis against climate shocks. Given these distinct mechanisms, ecological and financial strategies may provide benefits for smallholder systems that are heterogeneous both throughout the population and over time. Thus, when considered together, these disparate strategies may be complementary. To make progress toward sustainable development therefore requires an integrated perspective on the benefits of ecological and financial development strategies. We aim to provide a valuable contribution toward this goal by conducting a rigorous comparative assessment of how two particular ecological and financial strategies may affect smallholder climate resilience.
Microinsurance is a form of low-sum financial insurance specifically targeted at low-income households. In recent decades, it has gained traction in the international agricultural community as a resilience-enhancing strategy (SwissRe 2013, Müller et al. 2017, Kramer et al. 2019). By providing financial compensation during droughts, microinsurance directly builds the ex-post coping capacity (i.e., following the occurrence of a shock event) of smallholder households. Additionally, by reducing production risk, microinsurance can provide ex-ante benefits that enable risk-averse households to engage in different production activities and potentially escape poverty traps (Barrett et al. 2007, Carter et al. 2018). Index-based insurance, which gives payouts based on a predetermined climate index (e.g., rainfall) has been advocated as a tool for sustainable development because it helps to overcome some of the “moral hazard” issues associated with conventional indemnity-based insurance, i.e., the tendency for insured households to reduce their own risk management and increase costs for insurers (Hazell et al. 2010).
Farm management practices based on ecological principles take a different approach to smallholder climate resilience. By increasing ecosystem functions and diversity, they provide farmers a form of natural insurance (Finger and Buchmann 2015, Valente et al. 2019, Schaub et al. 2020). In particular, planting of nitrogen (N)-fixing leguminous cover crops to be incorporated into the soil as green manure builds resilience by increasing soil organic matter (SOM) and nutrient availability, which help to maintain or increase crop yields over time without other external inputs (Drinkwater et al. 1998, Snapp et al. 2005, Blanco-Canqui et al. 2012, Bommarco et al. 2013). Use of legume cover crops as green manures is receiving increasing attention (in the academic literature, from governments, and from non-profit and development organizations) as an approach for building smallholder resilience through conservation agriculture, regenerative agriculture, and agroecology (Florentín et al. 2011, Kaye and Quemada 2017, Wittwer et al. 2017, FAO 2018, HLPE 2019).
Despite their benefits, both microinsurance and legume cover cropping exhibit potential tradeoffs that may affect their relative performance. For example, insurance often does not incentivize sustainable management practices (O’Connor 2013) and may even lead to maladaptive outcomes in socio-environmental systems (Müller et al. 2017). In contrast, adopting legume cover cropping may lead to short-term losses in labor or yields as farmers transition to new management practices and build soil fertility (Martini et al. 2004). The structure of the payouts that these strategies provide may also contribute to divergent effects; although both entail annual costs, the ex-post benefits of index-based microinsurance are only experienced during shock years in which the index is triggered, whereas cover cropping provides a more consistent, though likely smaller, economic benefit (Rosa-Schleich et al. 2019). When considered together, it is therefore possible that microinsurance and cover cropping provide complementary benefits (Hansen et al. 2019).
However, it remains a challenge to understand the conditions (i.e., when, where, and for whom) under which each of these strategies may be most beneficial to smallholder climate resilience. A deeper understanding of their benefits can help to inform and target agricultural research and development and contribute to the debate on the relative merits of financial and ecological development approaches (Tomich et al. 2019b). Given the nascence of research on the impacts of both microinsurance and legume cover cropping on the global agricultural stage, observational datasets do not exist to evaluate their relative or complementary effects. In addition, both strategies involve feedbacks between household assets and underlying ecological systems, necessitating an integrated social-ecological perspective.
Process-based simulation models are powerful tools for extending the understanding of these relationships and feedbacks beyond existing empirical datasets, as well as exploring changes in conditions and processes that would be impossible to control for in the field (Magliocca et al. 2013). Simulation models that combine social and ecological processes (henceforth social-ecological simulation models) have been extensively used to explore questions related to resilience and smallholder agricultural livelihoods (Kremmydas et al. 2018, Dressler et al. 2019, Egli et al. 2019). In the context of microinsurance, an agent-based model (ABM) was used to show that there can exist long-term maladaptive feedbacks related to livestock insurance in pastoral systems (John et al. 2019). Models incorporating soil nutrient dynamics have shown that access to credit, fertilizer, and improved seeds can help to reduce poverty but does not guarantee long-term social-ecological sustainability (Schreinemachers et al. 2007). Process-based models have been used to explore the effects of different policies to mitigate N losses (Kaye-Blake et al. 2019) and to assess the emergence of poverty traps (Stephens et al. 2012). However, despite the suitability of social-ecological simulation models to investigate short- and long-term tradeoffs and to compare disparate resilience-enhancing strategies across a population, such temporal and distributional effects are rarely studied (Williams et al. 2020).
For this study, we developed a household-level, social-ecological simulation model of a mixed crop-livestock smallholder agricultural system. Rather than being calibrated to a specific location, the model was purposely stylized and represented the general characteristics of many mixed crop-livestock systems in the Global South. As such, the model is intended as a tool for generating hypotheses to be empirically tested by researchers in specific contexts, as well as for illustrating key social-ecological dynamics relevant for informing future interventions, programs, or public policy directed at poverty alleviation.
Using the model, we address the following questions:
In answering these questions, we operationalized the concept of resilience using measures of household wealth and income. In the model, these economic measures were mediated by ecological capital (i.e., soil nutrients). Our perspective is therefore an ecological-economic one. We hypothesized that financial insurance provided greater benefits to resilience in the short-term, but that over time the benefits of cover cropping for SOM would provide equal or superior resilience benefits. Thus, when applied together, the strategies will demonstrate complementarity over time. Additionally, because cover cropping constitutes a progressive ecological adaptation of the agroecosystem, we expected its benefit to be strongest for poor households with degraded soil fertility.
Our model description generally follows the Overview, Design Concepts, Details, and Decisions (ODD+D) format (Müller et al. 2013). We provide the full protocol in Appendix 1. The model was implemented in Python and code is available at CoMSES.net (refer to Data Availability Statement).
The social-ecological simulation model was developed to investigate climate resilience in smallholder mixed crop-livestock systems, which are prevalent in many dryland regions in the Global South, in which crop growth is limited by rainfall (Powell et al. 2004, Thornton and Herrero 2015). To more easily disentangle the key social-ecological dynamics, we sought to limit model complicatedness (Sun et al. 2016). As such, the model does not draw from extensive empirical data to represent a specific location, but we draw several parameters from Ethiopian data sources to define the relative scales of model elements (e.g., crop and livestock prices). We affectionately named the model SMASH: Stylized Model of Agricultural Smallholder Households.
Our model analysis examined the general mechanisms through which selected household-level adaptation strategies affect climate resilience. Because of the model’s stylized nature, we did not seek to directly generate policy-relevant recommendations through the model analysis. Rather, our assessment intends to (1) generate hypotheses that can be tested by researchers in future empirical studies and (2) provide theoretical grounding for future agricultural development and poverty reduction programs to integrate ecological and economic adaptation strategies.
The model (Fig. 1) represents a population of smallholder households that engage in agriculture and carry wealth solely in the form of livestock. Each household is defined by static land holdings and consumption requirements and has dynamic income and wealth. Livestock are grazed on a combination of on-farm crop residues and an external rangeland, which is not explicitly modeled. Each household’s land (or “field”) has an evolving level of organic and inorganic nutrients, the dynamics of which influence crop yields. The model is spatially implicit, no environmental feedbacks beyond the household scale are represented, and households do not interact.
The model operates at an annual time step. Each year of the simulation involves calculation of (1) soil nutrient flows, (2) crop yields, and (3) household income and wealth.
The model represents two pools of soil nutrients: organic and inorganic. The organic pool represents SOM and soil organic N together in a stylized manner, with fluxes primarily corresponding to the organic N portion of SOM. Although crop yields are also limited by other nutrients, we focused on N because it is generally the most limiting nutrient for crop growth (Robertson and Vitousek 2009). We henceforth refer to this pool as SOM, though we note that we quantify it using kg N/ha rather than as a percentage of bulk soil. Each year, inorganic nutrients are mineralized from both added organic matter and from the SOM pool (Fig. 1). These inorganic nutrients are available to that year’s food crop.
There are several points at which nutrients enter and leave the system (Fig. 1). First, a fraction of the mineralized nutrients is lost through leaching. This fraction is higher with lower levels of SOM (Drinkwater et al. 1998, Bommarco et al. 2013). Second, all nutrients contained in the harvested component of the crop are exported from the modeled system. Third, 10% of the crop residues are assumed to be lost or removed (Assefa et al. 2013). Nutrients enter the system through livestock manure, which qualitatively represents nutrient import from external grazing land. Hence, households with larger livestock herds have larger SOM additions, and consistent cropping with no replenishment of SOM will slowly degrade soil fertility over time (Reeves 1997).
In many mixed crop-livestock systems, households apply inorganic fertilizers to supplement in-soil nutrients for crop growth. However, inorganic fertilizer was not included in this version of the model. Including fertilizer would require additional assumptions about household decision making related to fertilizer use and livestock nutrient management, as well as complicate the model dynamics. We interpreted our results in light of this assumption.
We modeled crop yields using the yield gap concept, in which yields are reduced from a maximum potential value through water and/or nutrient limitations (Tittonell and Giller 2013). We first simulated the regional climate condition, which was the same over all households and was independently sampled each year from a normal distribution. Using this, we calculated field-level water reduction factors. Here, field-level SOM helps to reduce drought sensitivity (Bommarco et al. 2013). Next, if the available field-level inorganic N was insufficient to produce this water-constrained yield, production was limited by the available inorganic N. Finally, the resulting value was perturbed by a field-level, normally distributed stochastic error term. This term conceptually represents all uncontrollable factors affecting crop yields and other positive or negative household-level shocks, as well as local variability in the observed climate conditions within a region containing a population of smallholder households.
The model makes several assumptions with respect to household income and wealth. First, households do not have access to financial savings and instead use livestock as a bank account. Hence, wealth and livestock are equivalent in the model. Second, we do not consider non-farm employment markets. Third, households cannot purchase fodder for their livestock under baseline conditions, making livestock a risky wealth stock. These conditions are characteristic of many mixed crop-livestock systems in the Global South (Powell et al. 2004, Thornton and Herrero 2015), in which livestock are the primary savings mechanism. We interpreted our results in the light of these assumptions.
Households have a fixed annual consumption requirement. They earn income solely from harvested crops, which are sold each year at a constant price. If net income is in surplus, households add to their wealth stores by purchasing livestock. If net income is in deficit, households sell the required amount of livestock as a coping measure (Bellemare and Barrett 2006, Moyo and Swanepoel 2010). If income is in deficit and the household has no available wealth stores, we assume that they can perfectly reduce their consumption (i.e., wealth cannot be negative, and households do not exit the modeled system). Finally, we do not model livestock reproduction or mortality.
The ability for households to accumulate wealth is constrained by fodder availability for livestock (Valbuena et al. 2012, Assefa et al. 2013); we assume that a fixed percentage of livestock feed requirements must come from on-farm crop residues and that households cannot keep livestock that they cannot feed. Hence, households with larger land area (i.e., producing a greater quantity of crop residue) have larger wealth capacities. Additionally, this implies that in a year of complete crop failure, households lose all livestock that were dependent on crop residues.
The structure of the model implies the existence of a feedback loop; surplus income enables accumulation of livestock, providing additional organic matter, which both decreases drought sensitivity and increases future crop yields and income. A household’s ability to experience this positive feedback cycle is mediated by a combination of random and non-random factors; households’ attributes such as land endowment and SOM determine their wealth-generating ability and hence predispose them to certain trajectories. In addition, stochasticity through household-level random yield effects introduces a degree of path dependence into the model; a household that is unlucky one year (i.e., has a large, negative random effect in their crop yields) may be pushed into a poverty trap (Tittonell 2014, Haider et al. 2018) with decreasing livestock herds, SOM, crop yields, and income.
Given our interest in exploring the distributional effects of resilience strategies, we specified the model with three types of households that differ exclusively in their land endowment. We referred to these types as: land-poor, middle, and land-rich. We used pattern-oriented modeling (POM; Grimm et al. 2005) to estimate values for unknown model parameters that lead to a set of desired emergent model behaviors. To qualitatively represent both chronic and transitory poverty dynamics (Barrett 2005), we selected baseline parameters such that the land-poor households were “always poor” (i.e., never maintain positive levels of wealth throughout the simulation), the middle households were “sometimes poor,” and the land-rich households were “never poor”. Additionally, we required that SOM never increased to a maximum value under baseline conditions and that the middle households could recover from shocks. See further details in Appendix 1.
We represented both microinsurance and legume cover cropping in the model as scenarios, rather than as an outcome of an explicit decision-making process. Thus, we did not focus on the question of “how” to expand the use of these strategies. Instead, we explored what the potential benefits might be “if” each strategy is taken up, when these benefits may be experienced, and by whom. We therefore assumed that households always engaged in a given strategy, regardless of their previous experiences or wealth.
We included a representation of index-based crop insurance. A household with insurance must pay an annual premium to participate and receives a payout in any year that the climate condition is below a pre-specified threshold (e.g., the 10th percentile). The payout rate is the same for all households and is equivalent to the crop yield under average climate conditions, assuming a nutrient limitation factor of 0.5. Insurance payouts supplement the households’ income and, in contrast to regular income, can be used to buy fodder for livestock. Thus, the insurance de-risks the wealth stock and represents a form of asset protection rather than replacement (Carter et al. 2018). Because we did not model fertilizer or other agricultural production investments, we considered only the ex-post coping effects of microinsurance and not its ex-ante risk-reducing benefits.
Legume cover crops are grown in the fallow season and incorporated into the soil as green manures. Through biological N2 fixation and production of high-N biomass, green manures provide additional organic N inputs to the soil. Livestock are not grazed on the cover crops. We assume that the cover crops’ growth declines under adverse rainfall conditions in the same way as crop yields; thus, in a year with no rainfall, cover crops fail and no N is fixed (Serraj et al. 1999). We assumed an annual financial cost equal to the annual cost of insurance. By assuming that the labor required for cover cropping would otherwise be applied to other income-generating activities, this financial proxy for labor is appropriate.
We operationalized climate resilience in two distinct ways. We conceptualized both of these as nested within “development resilience,” which describes “the capacity over time...to avoid poverty in the face of various stressors and in the wake of myriad shocks” (Barrett and Constas 2014). The first measure represents the longer-term capacity of households to avoid poverty (i.e., retain positive livestock holdings) in the presence of climate variability and evolving SOM levels.
We referred to this resilience measure as the “poverty-reducing” capacity, Rpov:
where the probability is evaluated over 300 model replications at time Tpov (e.g., Tpov = 50 years). We conducted a convergence analysis to determine the appropriate number of model replications that ensured our estimates were not strongly influenced by model stochasticity (Appendix 2).
To compare a household’s poverty-reducing capacity under cover cropping (CC) and insurance (Ins), we calculated:
where the ≻ sign is read as “is preferable to.”
The second resilience measure assesses the shorter-term capacity of a household to maintain or increase its income in the wake of a drought. We referred to this as the “shock-absorbing” capacity, Rshock. Its measurement requires some explanation. First, we simulated the system under randomly generated climatic variability with a single-year “shock” (i.e., drought event) imposed in year Tshock. We measured the drought’s severity by its percentile in the climate distribution. For example, a 5% drought represents a 1 in 20-year event. The drought interacted with the model through its effect on food crop and cover crop yields in the same year, as well as any possible insurance payout (Fig. 1). This could have long-term implications if the household was required to sell livestock because this both reduces their future buffering capacity and reduces organic N inputs to their field.
To investigate the temporal dynamics of the shock-absorbing capacity, we ran experiments that differed across two dimensions. The first dimension represents the point in time at which the shock occured in the simulation (Tshock). Because both strategies (microinsurance and cover cropping) are applied in every year, Tshock is equivalent to the amount of time the given strategy has been in use. The second dimension represents the period of time over which the effects of and recovery from the shock are assessed (Tassess).
Thus, we calculated:
To compare the shock-absorbing capacity of a household under the two strategies, we calculated:
To investigate complementarities between the two strategies, we compared the resilience outcomes with both strategies implemented together (i.e., the households engage in both microinsurance and cover cropping and paying the costs for both) against the outcomes of each strategy in isolation. We considered complementarity as a situation in which engaging in both strategies yielded additional benefits above that derived from engaging in one strategy alone (either cover cropping or microinsurance) and a tradeoff as a situation in which engaging in both strategies was less beneficial than engaging in a single strategy. Tradeoffs may occur, for example, if the benefits of adding microinsurance to complement cover cropping do not offset the increased cost for the insurance premiums.
For both measures of resilience, our focus on wealth and income may appear to represent solely economic outcomes and not ecological ones. However, because a household’s wealth- and income-generating abilities are mediated over time by SOM, we indirectly incorporated ecological capital into our resilience measures. Additionally, through our dual resilience measurement, we combined stability properties with the ability to resist or undergo qualitative changes in structure (Holling 1973). Thus, a resilient household can both cope with drought-induced disturbance and resist entering a social-ecologically degraded “poor” state. However, because we did not focus on household decision-making or landscape-level processes, we did not consider facets of resilience related to adaptive responses or transformative system-level transitions (Folke 2016, Walker 2020).
We structured our analysis into four main experiments (Table 1). The first and second experiments respectively examined the shock-absorbing capacity (Rshock) and the poverty-reducing capacity (Rpov) of households under a range of time horizons. In these two experiments, we examined resilience under cover cropping and microinsurance, as well as with both strategies implemented together. In the third experiment, we tested how different assumptions about the costs and benefits of the two strategies affected the resilience comparisons (i.e., P(CC ≻ Ins)shock and P(CC ≻ Ins)pov) to identify “robust regions” within the parameter space (Lempert 2002). Here, we systematically varied the annual costs of both microinsurance and cover cropping, the microinsurance “strike rate” (i.e., percent of years with a payout), and the amount of N fixed by the cover crops. When the microinsurance cost factor is one, the insurance is actuarially fair. A cost factor less than one represents subsidized insurance and a factor greater than one implies net profits to the insurer.
In the final experiment, we explored how the resilience comparisons changed under different socio-environmental conditions. To do this, we conducted a sensitivity analysis on the parameters of the model. We employed a meta-modeling approach for global sensitivity analysis (Iooss and Lemaître 2015) in which we first ran our model under a wide range of perturbed parameter configurations and then fit a non-parametric regression model to explain how both resilience assessments changed over the perturbed parameter space. From the meta-model, we constructed a measure of “partial dependence,” which describes the relationship between each parameter and the resilience measures as assessed by the meta-model. We described this methodology in Appendix 3.
Before presenting the results of our main experiments, we first illustrate the representative behavior of the model under three simulations: baseline conditions with regular climate variability (Fig. 2A), in the wake of a drought (Fig. 2B), and with the two strategies (Fig. 2C). To most effectively demonstrate the relevant characteristics of the model, we assessed a different time period and different outcome measures in each representative simulation.
First, as specified by the calibration approach, under baseline conditions and regular climate variability, the land-poor households do not earn enough income to satisfy their consumption requirements and so always become poor (i.e., have zero wealth), whereas the middle households sometimes become poor and the land-rich households are never poor (Fig. 2A). The divergent outcomes for the middle households emphasize the path dependence in the model; all middle households begin the simulation in the same condition, but the randomness in the calculation of crop yields leads to divergent trajectories, particularly when droughts cause some households to either irrevocably lose their wealth reserves or to experience transitory poverty. Households with positive wealth reserves, through external nutrient input from livestock manure, are able to maintain their SOM, but SOM steadily declines for households with no wealth reserves (Fig. 2A). An imposed drought leads to a decline in wealth that persists for several years (Fig. 2B). Due to the wealth-SOM feedback in the model, this results in a marginally lower SOM than the drought-free counterfactual (Fig. 2B).
Microinsurance and cover cropping affect the model dynamics in several ways. Microinsurance premiums, which cost 10% of average yields, slightly decrease income under regular years, but the insurance payouts buffer the effects of drought when payouts are received (Fig. 2C). Cover cropping’s benefit to income in general increases over time and is strongest in years with higher rainfall (Fig. 2C). These effects are due to the higher inorganic nutrient availability (from decomposition of cover crop residues) that reduces the extent to which nutrients inhibit crop yields. Because nutrient availability is more critical in high-rainfall years when water is not a constraining factor, the largest benefits are therefore experienced at these times.
Our results conform with our main hypothesis, showing that insurance as an ex-post coping strategy is preferable in the short-term recovery from a drought, but that there is a time at and beyond which cover cropping provides larger benefits (Fig. 3). This is not a single point, however, but a line of (Tshock, Tassess) pairs. When assessing the effects solely in the year of the shock (Tassess = 1), insurance is the preferable strategy (i.e., P(CC ≻ Ins) < 0.5) in 100% of the simulations over all time. After 15 years of legume cover cropping, it takes approximately 5 years following a shock for the cumulative benefits of cover crops to outweigh the benefit of the insurance payout (i.e., transition to red in Fig. 3). After 25 years of cover cropping, this decreases to 3. These effects are qualitatively consistent for each of the three household types (Appendix 4, Fig. A4.3), showing that all types of households strongly benefit from insurance in the wake of a shock. However, when the drought is not severe enough to trigger an insurance payout, cover cropping consistently provides superior shock absorption benefits (Appendix 4, Fig. A4.4).
Because of the strong power of microinsurance in buffering the effects of drought, adding microinsurance to complement cover cropping always increases shock-absorbing capacity (Fig. 4A). In contrast, adding cover cropping to complement microinsurance leads to tradeoffs in the short-term (black region in Figure 4B). This is for two reasons. First, in the year of the drought (i.e., Tassess = 1), crop yields are constrained by water availability rather than nutrient availability, so cover cropping provides little or no direct benefit to offset its costs. Second, it takes time for cover cropping to build SOM and, consequently, the water retention capacity of the soil. Thus, tradeoffs are stronger when Tshock is lower. Nevertheless, as the amount of time for which cover cropping is practiced increases (i.e., as Tshock increases), its direct benefits to water retention enabled through higher SOM lead to complementary effects even in the year of the shock (Fig. 4B). Similarly, as Tassess increases, cover cropping provides progressively larger benefits that lead to long-term complementarity. Additional experimentation reveals that the long-term benefits of microinsurance and legume cover crops are greater than the sum of both strategies in isolation, i.e., they are synergistic (Appendix 5).
Under regular climate variability, legume cover cropping reduces poverty (Fig. 5). The effect is strongest for the land-poor households, who after 50 years of cover cropping are 21% more likely to avoid poverty. For the middle households, cover cropping almost eliminates poverty altogether. These strong effects are explained by the ecological feedback that cover cropping enables; higher SOM increases the productive ability of the households, thus increasing income over time (Appendix 4, Fig. A4.1A). However, there is a one- to two-year period in which the costs of cover cropping outweigh the benefits, resulting in decreased income for all household types (Appendix 4, Fig. A4.1A).
The results show a very different effect for insurance; for both the land-poor and middle households, insurance (modeled with ex-post coping benefits only) is not effective as a poverty alleviation mechanism (Fig. 5). Despite reducing income variability, the lower mean income in non-drought years because of required insurance premium payments leads to lower mean levels of wealth and SOM (Appendix 4, Fig. A4.1). This demonstrates that although the insurance scheme is actuarially fair, the required premium payments can enable an ecological feedback in the model whereby the payouts in shock years do not adequately compensate the income losses in regular years.
With respect to complementarity, for both land-poor and middle households, adding cover cropping to complement microinsurance successfully reduces poverty (Fig. 5). However, particularly for the land-poor households, the converse is not true; adding microinsurance to complement cover cropping increases poverty above the levels seen with cover cropping by itself. Hence, under the conditions of the model, increasing mean incomes, in this case, through cover cropping, is a more effective strategy for poverty alleviation than reducing income variability.
The measure of poverty reduction assessed in Figure 5 is not relevant for the land-rich households because they are not at risk of poverty under baseline conditions. Supplementary experimentation reveals that, in contrast to land-poor and middle households, microinsurance enables a positive ecological feedback with higher levels of wealth and SOM (Appendix 4, Fig. A4.1). Thus, households not vulnerable to poverty derive some benefit from the reduced income variability provided by microinsurance. To examine this more deeply for a land-rich household, in Appendix 6 we assessed the strategies’ effects on a measure of risk-averse utility. Over a range of levels of risk aversion, microinsurance provides welfare benefits to land-rich households. This benefit is initially greater than that of cover cropping, but over time cover cropping’s utility benefit surpasses microinsurance’s.
The superiority of microinsurance for shock absorption is robust to changes in the assumed strategy characteristics (Figs. 6B, 6D). When evaluating shock absorption over a three-year recovery period, insurance provides on-par or superior benefits to cover cropping up to cost factors of around two (i.e., a case in which the annual premium is twice the expected annual payout). Cover crops would need to be both freely available through household production (i.e., cost factor of zero) and fix very high levels of N to provide benefits equivalent to insurance (top-left of Fig. 6B). When effects are assessed only during the year of the shock (i.e., Tassess = 1), insurance remains strongly preferable for shock absorption under all conditions in which a payout is received (Appendix 4, Fig. A4.5).
The superiority of cover crops for poverty reduction is also robust (Figs. 6A, 6C). Only at high cover cropping costs and low N2 fixation rates does insurance become preferable (Fig. 6A). Similarly, the cost factor for microinsurance generally has to be lower than one for it to reduce poverty more than cover cropping (Fig. 6C). Interestingly, more frequent microinsurance payouts appear to provide better poverty reduction benefits (top-left of Fig. 6C). Additional experimentation with the microinsurance payout frequency revealed a tradeoff: providing more regular payouts effectively buffers income losses from moderate shocks but requires a higher annual premium that leads to increased vulnerability during more extreme shocks even when payouts are received (Appendix 7).
We use the sensitivity analysis (Fig. 7) both to assess the sensitivity of the model to its parameters and to draw insights about which resilience-enhancing strategy may be more preferable in different socio-environmental contexts. In Figure 7, the slopes of the lines give an indication of the magnitude and direction of the sensitivity of the P(CC ≻ Ins) assessments for each parameter. Because this was generated under a single set of settings for Tassess, Tshock, and Tpov (Table 1), in this section we are more interested in the slopes of the lines than the absolute P(CC ≻ Ins) values.
As consumption requirements (i.e., household living costs) are increased in the model, cover cropping becomes a better strategy for poverty reduction (i.e., the dashed line is upward sloping in Fig. 7A). This complements the results of Figure 5; higher consumption requirements result in more households becoming poor (Appendix 4, Fig. A4.7A), thus accentuating the poverty-reducing effects of cover cropping and further demonstrating cover cropping’s pro-poor benefits. Other household-level parameters do not exert considerable influence on the comparisons (Figs. 7B, 7C), and this low sensitivity provides strength to our results in the above sections.
Changes to the average climate condition have divergent and nonlinear effects on the resilience strategy comparisons (Fig. 7D). Cover cropping provides the largest relative poverty reduction at moderate climate conditions. This is because under low climate conditions (i.e., low rainfall), cover crops fix less N and so do not provide long-term SOM benefits (Appendix 4, Fig. A4.7B), reducing their relative ability as a poverty reduction strategy. Conversely, with high climate conditions (i.e., more rainfall), more households have livestock and so are able to maintain SOM in their fields without cover crops (Appendix 4, Fig. A4.7B), also reducing cover crops’ relative poverty reduction effect. For shock absorption, microinsurance is more beneficial than cover cropping under drier conditions (i.e., lower climate mean). Here, cover cropping more effectively buffers shocks under conditions of higher average rainfall because of SOM stabilizing yields during the more moderate shocks.
Under higher climate variability, cover cropping provides larger relative benefits to resilience (Fig. 7E). This is because cover cropping, through building of SOM, moderates the relationship between climate variability and yield variability. Although microinsurance provides payouts when climate conditions fall below the threshold, it does not buffer against climatic variability in non-payout years. Thus, when climate variability is higher, microinsurance has a lower relative benefit on average.
Cover cropping offers larger relative benefits to resilience under more adverse land characteristics, including situations with low external rangeland availability (Fig. 7F), low soil fertility returns from livestock (Fig. 7G), low soil fertility (Fig. 7H), and low yield potential (Fig. 7I). This result is not surprising because cover cropping progressively builds the system’s natural capital. Relationships are qualitatively consistent between the two resilience measures.
Our results suggest that, when used solely as an ex-post risk coping strategy, microinsurance alone may not help households to escape poverty (Figs. 5, 6). The premium payments required for microinsurance pushed poor households into poverty traps, thereby increasing poverty relative to baseline conditions. The lack of benefit for poor households highlights potential concerns regarding equity (Fisher et al. 2019) and is in accordance with some empirical research on index-based livestock insurance (Chantarat et al. 2017). In addition, we found that the vulnerable non-poor (i.e., middle) households also experienced higher poverty levels with the insurance alone. In part, this result is explained by our exclusion of ex-ante effects of insurance that would enable risk-averse households to engage in higher productivity livelihood activities, e.g., fertilizer use, crop choice, and other drought management strategies (Müller et al. 2011, Mobarak and Rosenzweig 2013, Karlan et al. 2014, Cole et al. 2017, Kramer et al. 2019). Inclusion of these effects may change the outcomes for the middle households. Nevertheless, the potential for microinsurance to cause vulnerable non-poor households to enter (transitory or chronic) poverty warrants further consideration in models with more complex household behavioral representations, including issues of moral hazard and interaction with other behavioral adaptations (O’Hare et al. 2016), as well as empirical investigation in different socio-environmental contexts.
The robustness of the relative benefit of legume cover cropping for poverty reduction in our model is largely because of its assumed long-term benefits for agricultural productivity, which enable poor households to “step up” out of poverty (Dorward 2009). Other production technologies, such as improved crop varieties, cropping system diversification, irrigation, or conservation agriculture practices, may offer similar risk- and productivity-related benefits to cover cropping (Lin 2011, Hansen et al. 2019). Additionally, other studies have argued for fertilizer subsidies to break soil quality poverty traps (Barrett and Bevis 2015). Future research could evaluate and compare the resilience effects of such productivity-enhancing technologies and policies.
However, our analysis highlights the value of an integrated social-ecological perspective. Our results show that legume cover cropping, i.e., investing directly in soil fertility itself, offers substantial combined potential for long-term environmental improvement and poverty reduction for smallholder farms, which may not exist with non-ecological technologies like inorganic fertilizer. Beyond the modeled effects, ecologically based management strategies offer numerous benefits to field- and landscape-level ecosystem services (Bommarco et al. 2013, Dainese et al. 2019), as well as reduce dependence on external inputs (Shennan 2008). Reduced externalities and ancillary benefits may be difficult to quantify and slow to build, but ultimately contribute to social-ecological synergies and resilience of a more “general” nature than the “specified” version assessed by our model (Cabell and Oelofse 2012, Jacobi et al. 2018, Stratton et al. 2020, Weise et al. 2020). Thus, we recommend that future policies, projects, and programs for smallholder poverty reduction empirically examine the benefits of integrated ecological and economic approaches (Müller and Kreuer 2016, Beck et al. 2019).
Our results revealed a one- to two-year period before cover cropping provided net benefits, i.e., a transition period (Martini et al. 2004, Lamine and Bellon 2009, Stratton et al. 2021). We did not focus on decision making or barriers to cover cropping adoption, but these results highlight that liquidity constraints and large time discounting rates could make households unable or unwilling to forgo these short-term losses to engage in cover cropping or similar practices (Quaas et al. 2019). Thus, a long-term view may not be pragmatic if focusing exclusively on cover crops. Capacity building, educational opportunities, and subsidies for cover crop seeds and labor during the transition period may help to overcome this barrier (Baumgärtner and Quaas 2010, DeLonge et al. 2016, Duff et al. 2017). Integration of dynamic decision making and interactions with other institutional structures are avenues for future research on ecological resilience-enhancing strategies.
Our results illustrate the strong complementarity of microinsurance and cover cropping: when implemented together, the strategies can provide greater benefit than either in isolation (Fig. 4). Climate resilience and poverty reduction programs, development agendas, and empirical studies could further test this complementarity and investigate bundling of adaptation strategies (Kramer and Ceballos 2018, Kramer et al. 2019, Wong et al. 2020). Our study demonstrates the promise of simulation models, whether empirically calibrated to specific locations or stylized as in this study, as tools for ex-ante examination of resilience dynamics and interactions between strategies over long timescales. Particularly in situations in which empirical evidence is lacking, simulation modeling can provide important information about time lags, barriers to adoption, and required investments, which can help to inform the design of poverty reduction programs and aid allocation.
Different types of households may require different forms of intervention; our results showed that chronically poor (i.e., land-poor) households benefited greatly from the ecological strategy of cover cropping, which acted as a necessary “cargo net” to mitigate risk and increase asset bases (Barrett 2005), but that adding microinsurance to complement cover cropping did not provide complementary poverty reduction benefits (Fig. 5). Thus, risk mitigation strategies such as cover cropping could be emphasized for enabling chronically poor households to step up out of poverty. However, because cover cropping alone did not bring all land-poor households out of poverty (Fig. 5), bundling with additional interventions, such as social protection measures (Hansen et al. 2019), may be necessary and should be investigated in future research. Bundled cover cropping and microinsurance appears to offer the greatest benefit for the vulnerable non-poor (i.e., middle) and non-poor (i.e., land-rich) households. For the middle households, the bundled strategies reduced poverty by a comparable amount to cover cropping in isolation (Fig. 5), as well as provided long-term complementarity in the wake of a drought (Fig. 4). For the land-rich households, particularly those with higher risk aversion, the bundled strategies provided immediate welfare improvements (Appendix 6).
Environmental context can exert additional influences on the appropriate combination of financial and farm-based strategies. For example, legume cover cropping had a comparative advantage in harsher and more degraded landscapes (Fig. 7). However, annual cover cropping may not be an appropriate agricultural practice in contexts with very low rainfall because this can limit potential biomass accumulation and N fixation, as well as potentially reduce soil moisture content and subsequent crop yields (Unger and Vigil 1998). In these contexts, drought-tolerant cover crops or other sustainable agriculture practices, such as mulching or agroforestry (Shankarnarayan et al. 1987, Ewansiha and Singh 2006, Bayala et al. 2012), may be more effective, both in isolation and in combination with insurance. Additionally, future case-based studies should target the insurance strike rate to the given social-ecological context (Lybbert and Carter 2015, Kramer et al. 2019) because context will affect climate-yield relationships, cover cropping performance, and poverty dynamics.
We made several strong assumptions in our model that may influence the generalizability of our results. Most importantly, a critical component of our model is the wealth-based feedback loop in which wealth (livestock) directly fosters organic nutrient imports and improves crop productivity. In situations in which financial resources other than livestock are available (e.g., savings accounts), wealth would not be as strongly linked to field-level nutrient import. Additionally, large areas of grassland may be required to graze livestock to sustain nutrient applications on cropland, which might be infeasible given social-political constraints on land ownership and access (Dell’Angelo et al. 2017). Furthermore, perfect import of nutrients from rangelands is an optimistic assumption because of competing uses for nutrients (Tittonell and Giller 2013, Berre et al. 2021). In all cases, the implication is that the wealth-based feedback loop in our model may be exaggerated and thus the strategies’ effects on poverty overestimated. However, this exaggeration is the same under each strategy, so by focusing on the relative benefits of the two strategies, we reduced (though did not eliminate) the implications of this bias for our assessment.
Our modeled system most closely approximates an isolated rural community in which non-farm employment opportunities do not exist, use of fertilizer is low, and wealth is constrained by local environmental conditions (i.e., no access to savings accounts or fodder for purchase). Smallholder systems globally are undergoing diverse structural transformations, leading to increased livelihood diversification both within agriculture and into non-agricultural activities, increased intensification, and commodification and consolidation of land ownership (Barrett et al. 2010, De Schutter 2011, Alobo Loison 2015). Inclusion of such processes would affect our results. For example, including inorganic fertilizer as another mechanism to increase productivity would likely diminish the relative benefits of cover cropping, though fertilizer does not directly build SOM. Moderate fertilizer application and cover cropping could therefore be complementary practices (Giller et al. 1997). Non-farm employment opportunities may help to increase smallholder resilience under baseline conditions by providing a means through which the poor can step out of poverty (Hansen et al. 2019). Additionally, households may be willing to buy fodder to smooth their asset stocks even at the expense of their own consumption (Morduch 1995), which would reduce the effects of drought on asset stocks seen in our results. Future research could expand the scope of this stylistic model to include additional livelihood activities, behaviors, or exogenous drivers and better match it to specific empirical contexts.
Our study focused on potential benefits if support systems existed such that smallholders were able to adopt legume cover cropping and microinsurance. We did not incorporate household decision making with respect to uptake of the strategies or their spillover effects on other management practices. In reality, there exist financial, social, and informational barriers to the adoption of both ecological and financial strategies that have led to limited uptake in smallholder systems to date. Integrating decision making and approaches from ecological economics with the resilience perspective in this article is a promising avenue for future research.
We assessed the effects of microinsurance and legume cover cropping on climate resilience in a stylized mixed crop-livestock smallholder system. Our study offers a fresh, reconciliatory perspective to the current debate on strategies for climate risk management and poverty reduction (Hansen et al. 2019). Distinct agricultural development communities and organizations advocate for microinsurance and ecologically based management, sometimes with strong ideological disagreements. By providing a rigorous comparative assessment of these strategies, we hope to bring these paradigms together, illuminate their complementarity, and seed future collaborative empirical assessments and integrated applications to programs and policies for sustainable development.
Our model results can be boiled down to this: insurance provides an important buffering effect to climate shocks, whereas legume cover cropping progressively decreases poverty and the impacts of shocks over time. Together, these benefits underscore the potential complementarity of economic and ecological adaptation strategies for smallholder resilience. Future development programs and empirical research could test this complementarity in different socio-environmental contexts, including how it develops over time and throughout a heterogeneous population of households. Finally, development resilience provides a useful conceptual framework for quantitative resilience analyses that jointly consider the capacities for poverty reduction and shock absorption (Dou et al. 2020). An integrated approach to resilience assessment shows promise to mitigate tradeoffs and harness complementarities so as to improve smallholder livelihoods and social-ecological functioning.
The authors sincerely thank the three anonymous reviewers, whose suggestions greatly improved the quality of the paper. TGW was supported by funding from the German Federal Ministry of Education and Research (BMBF-01LN1315A) within the Junior Research Group POLISES, as well as a U.S. National Science Foundation (NSF) CNH Grant (Land transactions and investments: Impacts on agricultural production, ecosystem services, and food-energy security; DEB-1617364). GD acknowledges financial support by the BMBF within the project NamTip - Understanding and Managing Desertification Tipping Points in Dryland Social-Ecological Systems (FKZ: 01LC1821D). AES was supported by the NSF Graduate Research Fellowship Program and Rackham Graduate School at the University of Michigan. BM was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the project SEEMI (Social-Ecological Effects of Microinsurance; 321077328).
The model is implemented in Python. All code supporting the findings of this study is openly available to download from CoMSES.net at https://www.comses.net/codebases/ee47544a-7eb0-4482-8967-42d6b0c05060/releases/1.0.0/
Alobo Loison, S. 2015. Rural livelihood diversification in sub-Saharan Africa: a literature review. Journal of Development Studies 51(9):1125-1138. https://doi.org/10.1080/00220388.2015.1046445
Assefa, D., A. Nurfeta, and S. Banerjee. 2013. Assessment of feed resource availability and livestock production constraints in selected Kebeles of Adami Tullu Jiddo Kombolcha District, Ethiopia. African Journal of Agricultural Research 8(29):4067-4073. [online] URL: https://academicjournals.org/article/article1380882684_Assefa%20et%20al.pdf
Badgley, C., J. Moghtader, E. Quintero, E. Zakem, M. Jahi Chappell, K. Avilés-Vázquez, A. Samulon, and I. Perfecto. 2007. Organic agriculture and the global food supply. Renewable Agriculture and Food Systems 22(2):86-108. https://doi.org/10.1017/S1742170507001640
Barrett, C. B. 2005. Rural poverty dynamics: development policy implications. Agricultural Economics 32(s1):45-60. https://doi.org/10.1111/j.0169-5150.2004.00013.x
Barrett, C. B., B. J. Barnett, M. R. Carter, S. Chantarat, J. W. Hansen, A. G. Mude, D. Osgood, J. R. Skees, C. G. Turvey, and M. N. Ward. 2007. Poverty traps and climate risk: limitations and opportunities of index-based risk financing. IRI Technical Report No. 07-02. IRI, Chicago, Illinois, USA. https://doi.org/10.2139/ssrn.1141933
Barrett, C. B., and L. E. M. Bevis. 2015. The self-reinforcing feedback between low soil fertility and chronic poverty. Nature Geoscience 8:907-912. https://doi.org/10.1038/ngeo2591
Barrett, C. B., M. R. Carter, and C. P. Timmer. 2010. A century-long perspective on agricultural development. American Journal of Agricultural Economics 92(2):447-468. https://doi.org/10.1093/ajae/aaq005
Barrett, C. B., and M. A. Constas. 2014. Toward a theory of resilience for international development applications. Proceedings of the National Academy of Sciences 111(40):14625-14630. https://doi.org/10.1073/pnas.1320880111
Baumgärtner, S., and M. F. Quaas. 2010. Managing increasing environmental risks through agrobiodiversity and agrienvironmental policies. Agricultural Economics 41(5):483-496. https://doi.org/10.1111/j.1574-0862.2010.00460.x
Bayala, J., G. W. Sileshi, R. Coe, A. Kalinganire, Z. Tchoundjeu, F. Sinclair, and D. Garrity. 2012. Cereal yield response to conservation agriculture practices in drylands of West Africa: a quantitative synthesis. Journal of Arid Environments 78:13-25. https://doi.org/10.1016/j.jaridenv.2011.10.011
Beck, M. W., O. Quast, and K. Pfliegner. 2019. Ecosystem-based adaptation and insurance: success, challenges and opportunities. InsuResilience Global Partnership, GIZ, Bonn, Germany. [online] URL: https://www.adaptationcommunity.net/wp-content/uploads/2019/11/EbA_insurance_publication_2019_web.pdf
Bellemare, M. F., and C. B. Barrett. 2006. An ordered tobit model of market participation: evidence from Kenya and Ethiopia. American Journal of Agricultural Economics 88(2):324-337. https://doi.org/10.1111/j.1467-8276.2006.00861.x
Berre, D., T. Diarisso, N. Andrieu, C. Le Page, and M. Corbeels. 2021. Biomass flows in an agro-pastoral village in West-Africa: who benefits from crop residue mulching? Agricultural Systems 187(February):102981. https://doi.org/10.1016/j.agsy.2020.102981
Blanco-Canqui, H., M. M. Claassen, and D. R. Presley. 2012. Summer cover crops fix nitrogen, increase crop yield, and improve soil-crop relationships. Agronomy Journal 104:137-147. https://doi.org/10.2134/agronj2011.0240
Bommarco, R., D. Kleijn, and S. G. Potts. 2013. Ecological intensification: harnessing ecosystem services for food security. Trends in Ecology and Evolution 28(4):230-238. https://doi.org/10.1016/j.tree.2012.10.012
Cabell, J. F., and M. Oelofse. 2012. An indicator framework for assessing agroecosystem resilience. Ecology and Society 17(1):18. https://doi.org/10.5751/ES-04666-170118
Carter, M. R., S. A. Janzen, and Q. Stoeffler. 2018. Can insurance help manage climate risk and food insecurity? Evidence from the pastoral regions of East Africa. Pages 201-225 in L. Lipper, N. McCarthy, D. Zilberman, S. Asfaw, and G. Branca, editors. Climate smart agriculture. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-61194-5_10
Chantarat, S., A. G. Mude, C. B. Barrett, and C. G. Turvey. 2017. Welfare impacts of index insurance in the presence of a poverty trap. World Development 94:119-138. https://doi.org/10.1016/j.worlddev.2016.12.044
Cole, S., X. Giné, and J. Vickery. 2017. How does risk management influence production decisions? Evidence from a field experiment. Review of Financial Studies 30(6):1935-1970. https://doi.org/10.1093/rfs/hhw080
Dainese, M., E. A. Martin, M. A. Aizen, M. Albrecht, I. Bartomeus, R. Bommarco, L. G. Carvalheiro, R. Chaplin-Kramer, V. Gagic, L. A. Garibaldi, J. Ghazoul, H. Grab, M. Jonsson, D. S. Karp, C. M. Kennedy, D. Kleijn, C. Kremen, D. A. Landis, D. K. Letourneau, L. Marini, K. Poveda, R. Rader, H. G. Smith, T. Tscharntke, G. K. S. Andersson, I. Badenhausser, S. Baensch, A. D. M. Bezerra, F. J. J. A. Bianchi, V. Boreux, V. Bretagnolle, B. Caballero-Lopez, P. Cavigliasso, A. Ćetković, N. P. Chacoff, A. Classen, S. Cusser, F. D. da Silva e Silva, G. A. de Groot, J. H. Dudenhöffer, J. Ekroos, T. Fijen, P. Franck, B. M. Freitas, M. P. D. Garratt, C. Gratton, J. Hipólito, A. Holzschuh, L. Hunt, A. L. Iverson, S. Jha, T. Keasar, T. N. Kim, M. Kishinevsky, B. K. Klatt, A.-M. Klein, K. M. Krewenka, S. Krishnan, A. E. Larsen, C. Lavigne, H. Liere, B. Maas, R. E. Mallinger, E. Martinez Pachon, A. Martínez-Salinas, T. D. Meehan, M. G. E. Mitchell, G. A. R. Molina, M. Nesper, L. Nilsson, M. E. O’Rourke, M. K. Peters, M. Plećaš, S. G. Potts, D. de L. Ramos, J. A. Rosenheim, M. Rundlöf, A. Rusch, A. Sáez, J. Scheper, M. Schleuning, J. M. Schmack, A. R. Sciligo, C. Seymour, D. A. Stanley, R. Stewart, J. C. Stout, L. Sutter, M. B. Takada, H. Taki, G. Tamburini, M. Tschumi, B. F. Viana, C. Westphal, B. K. Willcox, S. D. Wratten, A. Yoshioka, C. Zaragoza-Trello, W. Zhang, Y. Zou, and I. Steffan-Dewenter. 2019. A global synthesis reveals biodiversity-mediated benefits for crop production. Science Advances 5(10):eaax0121. https://doi.org/10.1126/sciadv.aax0121
De Schutter, O. 2011. The green rush: the global race for farmland and the rights of land users. Harvard International Law Journal 52:503-556. [online] URL: https://harvardilj.org/wp-content/uploads/sites/15/2011/07/HILJ_52-2_De-Schutter1.pdf
Dell’Angelo, J., P. D’Odorico, and M. C. Rulli. 2017. Threats to sustainable development posed by land and water grabbing. Current Opinion in Environmental Sustainability 26-27:120-128. https://doi.org/10.1016/j.cosust.2017.07.007
DeLonge, M. S., A. Miles, and L. Carlisle. 2016. Investing in the transition to sustainable agriculture. Environmental Science and Policy 55:266-273. https://doi.org/10.1016/j.envsci.2015.09.013
Dorward, A. 2009. Integrating contested aspirations, processes and policy: development as hanging in, stepping up and stepping out. Development Policy Review 27(2):131-146. https://doi.org/10.1111/j.1467-7679.2009.00439.x
Dressler, G., J. Groeneveld, C. M. Buchmann, C. Guo, N. Hase, J. Thober, K. Frank, and B. Müller. 2019. Implications of behavioral change for the resilience of pastoral systems - lessons from an agent-based model. Ecological Complexity 40:100710. https://doi.org/10.1016/j.ecocom.2018.06.002
Drinkwater, L. E., P. Wagoner, and M. Sarrantonio. 1998. Legume-based cropping systems have reduced carbon and nitrogen losses. Nature 396(6708):262-265. https://doi.org/10.1038/24376
Duff, A. J., P. H. Zedler, J. A. Barzen, and D. L. Knuteson. 2017. The capacity-building stewardship model: assessment of an agricultural network as a mechanism for improving regional agroecosystem sustainability. Ecology and Society 22(1):45. https://doi.org/10.5751/ES-09146-220145
Dou, Y., P. J. Deadman, M. Berbés-Blázquez, N. D. Vogt, and O. Almeida. 2020. Pathways out of poverty through the lens of development resilience: an agent-based simulation. Ecology and Society 25(4):3. https://doi.org/10.5751/ES-11842-250403
Egli, L., H. Weise, V. Radchuk, R. Seppelt, and V. Grimm. 2019. Exploring resilience with agent-based models: state of the art, knowledge gaps and recommendations for coping with multidimensionality. Ecological Complexity 40(B):100718. https://doi.org/10.1016/j.ecocom.2018.06.008
Ewansiha, S. U., and B. B. Singh. 2006. Relative drought tolerance of important herbaceous legumes and cereals in the moist and semi-arid regions of West Africa. Journal of Food, Agriculture and Environment 4:188-190.
Food and Agriculture Organization of the United Nations (FAO). 2018. Agroecology: a pathway to achieving the SDGs. Rural 21. [online] URL: https://www.rural21.com/english/news/detail/article/agroecology-a-pathway-to-achieving-the-sdgs.html?no_cache=1
Finger, R., and N. Buchmann. 2015. An ecological economic assessment of risk-reducing effects of species diversity in managed grasslands. Ecological Economics 110:89-97. https://doi.org/10.1016/j.ecolecon.2014.12.019
Fisher, E., J. Hellin, H. Greatrex, and N. Jensen. 2019. Index insurance and climate risk management: addressing social equity. Development Policy Review 37:681-602. https://doi.org/10.1111/dpr.12387
Florentín, M. A., M. Peñalva, A. Calegari, and R. Derpsch. 2011. Green manure/cover crops and crop rotation in conservation agriculture on small farms. Plant Production and Protection Division, Food and Agriculture Organization of the United Nations, Rome, Italy.
Folke, C. 2016. Resilience (Republished). Ecology and Society 21(4):44. https://doi.org/10.5751/ES-09088-210444
Giller, K. E., G. Cadisch, C. Ehaliotis, E. Adams, W. D. Sakala, and P. L. Mafongoya. 1997. Building soil nitrogen capital in Africa. Pages 151-192 in R. J. Buresh, P. A. Sanchez, and F. Calhoun, editors. Replenishing soil fertility in Africa. Soil Science Society of America and American Society of Agronomy, Madison, Wisconsin, USA. https://doi.org/10.2136/sssaspecpub51.c7
Grimm, V., E. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H.-H. Thulke, J. Weiner, T. Wiegand, and D. L. DeAngelis. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310(5750):987-991. https://doi.org/10.1126/science.1116681
Haider, L. J., W. J. Boonstra, G. D. Peterson, and M. Schlüter. 2018. Traps and sustainable development in rural areas: a review. World Development 101:311-321. https://doi.org/10.1016/j.worlddev.2017.05.038
Hansen, J., J. Hellin, T. Rosenstock, E. Fisher, J. Cairns, C. Stirling, C. Lamanna, J. van Etten, A. Rose, and B. Campbell. 2019. Climate risk management and rural poverty reduction. Agricultural Systems 172:28-46. https://doi.org/10.1016/j.agsy.2018.01.019
Hazell, P., J. Anderson, N. Balzer, A. Hastrup Clemmensen, U. Hess, and F. Rispoli. 2010. The potential for scale and sustainability in weather index insurance for agriculture and rural livelihoods. International Fund for Agricultural Development and World Food Programme, Rome, Italy. [online] URL: https://www.ifad.org/documents/38714170/40239486/The+potential+for+scale+and+sustainability+in+weather+index+insurance+for+agriculture+and+rural+livelihoods.pdf/7a8247c7-d7be-4a1b-9088-37edee6717ca
High Level Panel of Experts on Food Security and Nutrition (HLPE). 2019. Agroecological and other innovative approaches for sustainable agriculture and food systems that enhance food security and nutrition. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome, Italy. http://www.fao.org/3/ca5602en/ca5602en.pdf
Holling, C. S. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4(1):1-23. https://doi.org/10.1146/annurev.es.04.110173.000245
Iooss, B., and P. Lemaître. 2015. A review on global sensitivity analysis methods. Pages 101-122 in G. Dellino and C. Meloni, editors. Uncertainty management in simulation-optimization of complex systems: algorithms and applications. Springer, Boston, Massachusetts, USA. https://doi.org/10.1007/978-1-4899-7547-8_5
Jacobi, J., S. Mukhovi, A. Llanque, H. Augstburger, F. Käser, C. Pozo, M. Ngutu Peter, J. M. F. Delgado, B. P. Kiteme, S. Rist, and C. Ifejika Speranza. 2018. Operationalizing food system resilience: an indicator-based assessment in agroindustrial, smallholder farming, and agroecological contexts in Bolivia and Kenya. Land Use Policy 79:433-446. https://doi.org/10.1016/j.landusepol.2018.08.044
John, F., R. Toth, K. Frank, J. Groeneveld, and B. Müller. 2019. Ecological vulnerability through insurance? Potential unintended consequences of livestock drought insurance. Ecological Economics 157:357-368. https://doi.org/10.1016/j.ecolecon.2018.11.021
Karlan, D., R. D. Osei, I. Osei-Akoto, and C. Udry. 2014. Agricultural decisions after relaxing credit and risk constraints. Quarterly Journal of Economics 129(2):597-652. https://econpapers.repec.org/article/oupqjecon/v_3a129_3ay_3a2014_3ai_3a2_3ap_3a597-652.htm
Kaye, J. P., and M. Quemada. 2017. Using cover crops to mitigate and adapt to climate change. A review. Agronomy for Sustainable Development 37(1):4. https://doi.org/10.1007/s13593-016-0410-x
Kaye-Blake, W., C. Schilling, R. Monaghan, R. Vibart, S. Dennis, and E. Post. 2019. Quantification of environmental-economic trade-offs in nutrient management policies. Agricultural Systems 173:458-468. https://doi.org/10.1016/j.agsy.2019.03.013
Kramer, B., and F. Ceballos. 2018. Enhancing adaptive capacity through climate-smart insurance: theory and evidence from India. Presented at the 2018 International Conference of Agricultural Economists, Vancouver, British Columbia, July 28-August 2, 2018. http://ageconsearch.umn.edu/record/275926>
Kramer, B., J. Hellin, J. Hansen, A. Rose, and M. Braun. 2019. Building resilience through climate risk insurance: insights from agricultural research for development. Working Paper, CGIAR, Wageningen, the Netherlands. [online] URL: https://hdl.handle.net/10568/106171
Kremmydas, D., I. N. Athanasiadis, and S. Rozakis. 2018. A review of Agent Based Modeling for agricultural policy evaluation. Agricultural Systems 164:95-106. https://doi.org/10.1016/j.agsy.2018.03.010
Lamine, C., and S. Bellon. 2009. Conversion to organic farming: a multidimensional research object at the crossroads of agricultural and social sciences. A review. Agronomy for Sustainable Development 29(1):97-112. https://doi.org/10.1051/agro:2008007
Lempert, R. J. 2002. A new decision sciences for complex systems. Proceedings of the National Academy of Sciences 99 Suppl3(May):7309-7313. https://doi.org/10.1073/pnas.082081699
Lin, B. B. 2011. Resilience in agriculture through crop diversification: adaptive management for environmental change. Bioscience 61(3):183-193. https://doi.org/10.1525/bio.2011.61.3.4
Lybbert, T. J., and M. R. Carter. 2015. Bundling drought tolerance and index insurance to reduce rural household vulnerability to drought. Pages 401-414 in A. M. Balisacan, U. Chakravorty, and M.-L. V. Ravago, editors. Sustainable economic development. Academic, San Diego, California, USA. https://doi.org/10.1016/B978-0-12-800347-3.00022-4
Magliocca, N. R., D. G. Brown, and E. C. Ellis. 2013. Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making. PLoS One 8(9):e73241. https://doi.org/10.1371/journal.pone.0073241
Martini, E. A., J. S. Buyer, D. C. Bryant, T. K. Hartz, and R. F. Denison. 2004. Yield increases during the organic transition: improving soil quality or increasing experience? Field Crops Research 86(2):255-266. https://doi.org/10.1016/j.fcr.2003.09.002
Mobarak, A. M., and M. R. Rosenzweig. 2013. Informal risk sharing, index Insurance, and risk taking in developing countries. American Economic Review 103(3):375-380. https://doi.org/10.1257/aer.103.3.375
Morduch, J. 1995. Income smoothing and consumption smoothing. Journal of Economic Perspectives 9(3):103-114. https://doi.org/10.1257/jep.9.3.103
Moyo, S., and F. J. C. Swanepoel. 2010. Multifunctionality of livestock in developing communities. Pages 1-13 in F. Swanepoel, A. Stroebel, and S. Moyo, editors. Role of livestock in developing communities: enhancing multifunctionality. Technical Centre for Agricultural and Rural Cooperation, Wageningen, The Netherlands; University of the Free State, Bloemfontein, South Africa; International Livestock Research Institute, Nairobi, Kenya.
Müller, B., F. Bohn, G. Dreßler, J. Groeneveld, C. Klassert, R. Martin, M. Schlüter, J. Schulze, H. Weise, and N. Schwarz. 2013. Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol. Environmental Modelling and Software 48:37-48. https://doi.org/10.1016/j.envsoft.2013.06.003
Müller, B., L. Johnson, and D. Kreuer. 2017. Maladaptive outcomes of climate insurance in agriculture. Global Environmental Change 46:23-33. https://doi.org/10.1016/j.gloenvcha.2017.06.010
Müller, B., and D. Kreuer. 2016. Ecologists should care about insurance, too. Trends in Ecology and Evolution 31(1):1-2. https://doi.org/10.1016/j.tree.2015.10.006
Müller, B., M. F. Quaas, K. Frank, and S. Baumgärtner. 2011. Pitfalls and potential of institutional change: rain-index insurance and the sustainability of rangeland management. Ecological Economics 70(11):2137-2144. https://doi.org/10.1016/j.ecolecon.2011.06.011
O’Connor, C. 2013. Soil matters: how the federal crop insurance program should be reformed to encourage low-risk farming methods with high-reward environmental outcomes. Natural Resources Defense Council, New York, New York, USA. [online] URL: https://www.nrdc.org/sites/default/files/soil-matters-IP.pdf
O’Hare, P., I. White, and A. Connelly. 2016. Insurance as maladaptation: resilience and the ‘business as usual’ paradox. Environment and Planning. C, Government and Policy 34(6):1175-1193. https://doi.org/10.1177/0263774x15602022
Powell, J. M., R. A. Pearson, and P. H. Hiernaux. 2004. Crop-livestock onteractions in the West African drylands. Agronomy Journal 96:469-483. https://doi.org/10.2134/agronj2004.4690
Quaas, M., S. Baumgärtner, and M. De Lara. 2019. Insurance value of natural capital. Ecological Economics 165:106388. https://doi.org/10.1016/j.ecolecon.2019.106388
Reeves, D. W. 1997. The role of soil organic matter in maintaining soil quality in continuous cropping systems. Soil and Tillage Research 43(1):131-167. https://doi.org/10.1016/S0167-1987(97)00038-X
Robertson, G. P., and P. M. Vitousek. 2009. Nitrogen in agriculture: balancing the cost of an essential resource. Annual Review of Environment and Resources 34(1):97-125. https://doi.org/10.1146/annurev.environ.032108.105046
Rosa-Schleich, J., J. Loos, O. Mußhoff, and T. Tscharntke. 2019. Ecological-economic trade-offs of diversified farming systems - a review. Ecological Economics 160:251-263. https://doi.org/10.1016/j.ecolecon.2019.03.002
Schaub, S., N. Buchmann, A. Lüscher, and R. Finger. 2020. Economic benefits from plant species diversity in intensively managed grasslands. Ecological Economics 168:106488. https://doi.org/10.1016/j.ecolecon.2019.106488
Schreinemachers, P., T. Berger, and J. B. Aune. 2007. Simulating soil fertility and poverty dynamics in Uganda: a bio-economic multi-agent systems approach. Ecological Economics 64(2):387-401. https://doi.org/10.1016/j.ecolecon.2007.07.018
Serraj, R., T. R. Sinclair, and L. C. Purcell. 1999. Symbiotic N2 fixation response to drought. Journal of Experimental Botany 50(331):143-155. https://doi.org/10.1093/jxb/50.331.143
Shankarnarayan, K. A., L. N. Harsh, and S. Kathju. 1987. Agroforestry in the arid zones of India. Agroforestry Systems 5(1):69-88. https://doi.org/10.1007/BF00046414
Shennan, C. 2008. Biotic interactions, ecological knowledge and agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences 363(1492):717-739. https://doi.org/10.1098/rstb.2007.2180
Snapp, S. S., S. M. Swinton, R. Labarta, D. Mutch, J. R. Black, R. Leep, J. Nyiraneza, and K. O’Neil. 2005. Evaluating cover crops for benefits, costs and performance within cropping system niches. Agronomy Journal 97:322-332.
Stephens, E. C., C. F. Nicholson, D. R. Brown, D. Parsons, C. B. Barrett, J. Lehmann, D. Mbugua, S. Ngoze, A. N. Pell, and S. J. Riha. 2012. Modeling the impact of natural resource-based poverty traps on food security in Kenya: the crops, livestock and soils in smallholder economic systems (CLASSES) model. Food Security 4(3):423-439. https://doi.org/10.2139/ssrn.1844623
Stratton, A. E., L. Kuhl, and J. Blesh. 2020. Ecological and nutritional functions of agroecosystems as indicators of smallholder resilience. Frontiers in Sustainable Food Systems 4:173. https://doi.org/10.3389/fsufs.2020.543914
Stratton, A. E., H. Wittman, and J. Blesh. 2021. Diversification supports farm income and improved working conditions during agroecological transitions in southern Brazil. Agronomy for Sustainable Development 41:35(2021). https://doi.org/10.1007/s13593-021-00688-x
Sun, Z., I. Lorscheid, J. D. Millington, S. Lauf, N. R. Magliocca, J. Groeneveld, S. Balbi, H. Nolzen, B. Müller, J. Schulze, and C. M. Buchmann. 2016. Simple or complicated agent-based models? A complicated issue. Environmental Modelling and Software 86:56-67. https://doi.org/10.1016/j.envsoft.2016.09.006
SwissRe. 2013. Partnering for food security in emerging markets. Swiss Reinsurance Company, Zurich, Switzerland. [online] URL: https://www.swissre.com/dam/jcr:dbafcfed-d5c7-406b-a2e1-d2ea4ad020e0/sigma1_2013_en.pdf
Thornton, P. K., and M. Herrero. 2015. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nature Climate Change 5(9):830-836. https://doi.org/10.1038/nclimate2754
Tittonell, P. 2014. Livelihood strategies, resilience and transformability in African agroecosystems. Agricultural Systems 126:3-14. https://doi.org/10.1016/j.agsy.2013.10.010
Tittonell, P., and K. E. Giller. 2013. When yield gaps are poverty traps: the paradigm of ecological intensification in African smallholder agriculture. Field Crops Research 143:76-90. https://doi.org/10.1016/j.fcr.2012.10.007
Tomich, T. P., P. Lidder, M. Coley, D. Gollin, R. Meinzen-Dick, P. Webb, and P. Carberry. 2019a. Food and agricultural innovation pathways for prosperity. Agricultural Systems 172:1-15. https://doi.org/10.1016/j.agsy.2018.01.002
Tomich, T. P., P. Lidder, J. Dijkman, M. Coley, P. Webb, and M. Gill. 2019b. Agri-food systems in international research for development: ten theses regarding impact pathways, partnerships, program design, and priority-setting for rural prosperity. Agricultural Systems 172:101-109. https://doi.org/10.1016/j.agsy.2018.12.004
Unger, P. W., and M. F. Vigil. 1998. Cover crop effects on soil water relationships. Journal of Soil and Water Conservation 53(3):200-207.
Valbuena, D., O. Erenstein, S. Homann-Kee Tui, T. Abdoulaye, L. Claessens, A. J. Duncan, B. Gérard, M. C. Rufino, N. Teufel, A. van Rooyen, and M. T. van Wijk. 2012. Conservation agriculture in mixed crop-livestock systems: scoping crop residue trade-offs in Sub-Saharan Africa and South Asia. Field Crops Research 132:175-184. https://doi.org/10.1016/j.fcr.2012.02.022
Valente, D., P. P. Miglietta, D. Porrini, M. R. Pasimeni, G. Zurlini, and I. Petrosillo. 2019. A first analysis on the need to integrate ecological aspects into financial insurance. Ecological Modelling 392:117-127. https://doi.org/10.1016/j.ecolmodel.2018.11.009
Walker, B. H. 2020. Resilience: what it is and is not. Ecology and Society 25(2):11. https://doi.org/10.5751/ES-11647-250211
Weise, H., H. Auge, C. Baessler, I. Bärlund, E. M. Bennett, U. Berger, F. Bohn, A. Bonn, D. Borchardt, F. Brand, A. Chatzinotas, R. Corstanje, F. De Laender, P. Dietrich, S. Dunker, W. Durka, I. Fazey, J. Groeneveld, C. S. E. Guilbaud, H. Harms, S. Harpole, J. Harris, K. Jax, F. Jeltsch, K. Johst, J. Joshi, S. Klotz, I. Kühn, C. Kuhlicke, B. Müller, V. Radchuk, H. Reuter, K. Rinke, M. Schmitt-Jansen, R. Seppelt, A. Singer, R. J. Standish, H. Thulke, B. Tietjen, M. Weitere, C. Wirth, C. Wolf, and V. Grimm. 2020. Resilience trinity: safeguarding ecosystem functioning and services across three different time horizons and decision contexts. Oikos 129:445-456. https://doi.org/10.1111/oik.07213
Williams, T. G., S. D. Guikema, D. G. Brown, and A. Agrawal. 2020. Resilience and equity: quantifying the distributional effects of resilience-enhancing strategies in a smallholder agricultural system. Agricultural Systems 182:102832. https://doi.org/10.1016/j.agsy.2020.102832
Wittwer, R. A., B. Dorn, W. Jossi, and M. G. A. van der Heijden. 2017. Cover crops support ecological intensification of arable cropping systems. Scientific Reports 7:41911. https://doi.org/10.1038/srep41911
Wong, H. L., X. Wei, H. B. Kahsay, Z. Gebreegziabher, C. Gardebroek, D. E. Osgood, and R. Diro. 2020. Effects of input vouchers and rainfall insurance on agricultural production and household welfare: experimental evidence from northern Ethiopia. World Development 135:105074. https://doi.org/10.1016/j.worlddev.2020.105074