Research

INTRODUCTION

Social ecological systems comprise humans and their interaction with the natural environment. These systems have been studied extensively, particularly among smallholder farmers cultivating less than two hectares across sub-Saharan Africa (SSA). In some instances, a cropping system or a farming practice is considered as an adopted technology. In one instance, adoption rates are affected by social factors, such as the interaction between adopters and non-adopters in a political and economic setting. In another, ecological factors affect sustained adoption overtime, such as climate variability, limiting the efficacy of farming practices. The interaction between these two factors represents system behavior (Van Strien et. al. 2019), which in many respects has remained elusive to practitioners disseminating ecological practices to improve farmer livelihoods and food security.

Despite countless attempts to disseminate ecological farming practices (e.g., intercropping), the cultivation of maize (Zea mays) as a monoculture has continued to be the dominant cropping system across SSA. Its cultivation has shown to lower soil fertility (Kureh et al. 2006), limit soil-phosphorous (P) availability (Abdul et al. 2012), and ultimately, perpetuate the spread of the parasitic weed Striga (Striga asiatica). As a parasite, Striga attaches to the root system of maize, sapping nutrients and causing yield losses, sometimes up to 100% (Pfunye et al. 2021). Although many farmers are aware of the relationship between monocultures and the spread of Striga, longstanding social and ecological factors have encouraged them to continue cultivating monocultures. For example, farmers tend to believe that a Striga Control Practice (SCP) like intercropping will provide lower maize yields than a monoculture; thus, this social factor prevails over the ecological benefits it can provide in the long term.

Intercropping maize with legumes, particularly cowpea (Vigna unguiculata L.), has shown to be an effective SCP across multiple settings in SSA (Khan et al. 2008). Various factors, however, can discourage the cultivation of maize-cowpea intercropping systems, including negative word of mouth (between farmers) or general disappointment with the practice (Masvaya et al. 2017). Multiple research methods have been used to study these factors (Silberg et al. 2017, 2020); however, they are difficult to combine within a single social-ecological framework (Meadows 2008). Methods, such as system dynamics modeling, can be used to understand why adoption rates vary overtime with respect to environmental factors (Oyo and Kalema 2016). More importantly though, system dynamics models can serve as management tools, indicating participatory strategies to manage environmental issues (Schlüter et al. 2019). With these tools, policy makers may avoid implementing traditional interventions that only focus on a single factor to resolve wider complex issues, but rather, develop interventions that work across scales to sustain ecologically balanced systems with responsible agricultural practices (Siman and Niewiarowksi 2023).

Our objective with this paper is two-fold. The first is to offer a novel framework to build models that reflect social-ecological systems. The framework fills a critical gap in the modeling and policy literature, whereby smallholder farmer perspectives are excluded from developing tools to evaluate system resiliency and policy options (Paas et al. 2021). The second objective is to develop a model for evaluating policies, specifically their effect on the adoption rates of ecological farming practices (in this case, intercropping) and their effect on the spread of pests across a country, like Malawi. Developing policies that foster system resilience via participatory approaches are critical (Epstein et al. 2021) given increasing climate variability and the call to support country-wide strategies for tackling food insecurity across SSA. We attempt to respond to this call for discovering policies to sustain adoption of ecological practices and tailoring them to withstand extreme events.

BACKGROUND

The state of Striga in Malawi

Prior to 2000, cereals were commonly intercropped with legumes across Malawi and the continent (Vandermeer 1992). Today, far less fields are intercropped. Some suggest agricultural policies in the past 20 years have discouraged these practices, incentivizing farmers to cultivate maize for export (Snapp and Fisher 2015). As a result, in the past two decades, Striga has emerged more frequently in smallholder fields across Malawi (Atera et al. 2013). The recent emergence of Striga may be attributed to reduced land per capita and augmented losses of soil fertility (via successive maize cultivation), which are drivers of parasitic weed emergence (Shackleton et al. 2009). Striga seeds are microscopic and can transfer to neighboring fields via wind, water, roaming livestock, farming tools, and maize seed (Wan and Wang 2019). The spread of Striga seeds is common across developing countries that rely on agriculture as a livelihood (Munthali et al. 2019, Fatch et al. 2020). The spread of Striga is amplified by its fecundity, as conservative estimates posit that one Striga plant can produce between 36,308 and 45,729 seeds (Abdul et al. 2012).

Multiple Malawian ministries have not specifically composed strategies to address the spread of Striga, but four policy frameworks have promoted practices (including intercropping) to address yield losses associated with the weed (Nyagumbo et al. 2016). These include the National Agricultural Policy (NAP) launched in 2016, the Adaptive Research Strategy (ARS) launched in 2015, the Malawi Growth and Development Strategy (MGDS) launched in 2012, and the Agriculture Sector Wide Approach (ASWAp) launched in 2010 (Stadler et al. 2017). The NAP, for instance, intended to increase legume and oilseed production via export markets along with the support of agricultural extension and value chain development. In another case, the ASWAp aimed to increase crop diversification by extending a Farm Input Subsidy Program (FISP) to synthetic fertilizer and legumes. Essentially, these two policies promoted the use of intercropping as a strategy to increase household food security and soil fertility.

Various programs and projects have been funded (not exclusively) under these four policy frameworks to increase agricultural diversity, profitability, and productivity. For example, the Soil Fertility Research Network for Smallholder Maize-Based Cropping Systems (Soil-Fert Net) promoted intercropping as a “Best Bet” soil fertility management practice (Mekuria et al. 2004). Soil-Fert Net also distributed small grants to fund events where collective learning could take place between farmers. With this network, scientists conducted experiments in smallholder fields, evaluating intercropping schemes while promoting them as SCPs. In other instances, farmer groups were created to hold demonstrations of how to properly cook legumes (Mhango et al. 2013). Other organizations created Early Childhood Development centers, acknowledging gender affected adoption (Mponela et al. 2021), and provided women access to demonstration plots to learn how intercropping systems could improve household nutrition (Gelli et al. 2018). Programs, such as Africa RISING (Research In Sustainable Intensification for the Next Generation), combined multiple aspects (e.g., best bet technology, farmer cooperatives) to increase seed access, extension, and visibility of demonstration plots (Timler et al. 2014)

Cultivation of cowpea as an intercrop

Cultivating maize-cowpea intercropping systems is a relatively new technology (to conserve soil and control Striga) in Malawi, as recent studies have found farmers are still learning about its benefits (Ngwira et al. 2020). However, planting cowpea with maize is not unheard of, as some extension planning areas have a long history of relay-intercropping the legume with maize. For example, in the Central and Southern regions of Malawi (Thyolo, Mulaje, and Chiradzulu) cowpeas are sown three weeks to two months after maize has been planted (Ngwira et al. 1990, Masvaya et al. 2017). In these extension planning areas, farmers plant the legume between rows several weeks after maize is planted, or once maize reaches physiological maturity, so cowpea does not compete for resources.

There are three primary barriers to cultivating maize-cowpea intercropping systems. In the first barrier, maize yield can decrease from 6% to 25% relative to sole maize cropping systems, depending on the spatial design (i.e., additive or replacement) of a field (Rusinamhodzi et al. 2012). These yield penalties can occur when maize and cowpea compete for nutrients (Lunduka et al. 2012, Snapp et al. 2018) and water in marginal environments (Silberg et al. 2020). In the second barrier, the price of legume seed is costly for a financially constrained smallholder farmer (Place et al. 2003). In addition, access to farming inputs and export markets are often limited to smallholder farmers, decreasing their chances of receiving a profit after adopting intercropping (Chinsinga and Poulton 2014). In the third barrier, when potential adopters lack an understanding of how intercropping controls weeds (e.g., allelopathy), they are less likely to adopt the practice (Silberg et al. 2019).

APPROACH AND METHODS

The conceptual framework of this paper was structured around an objective to understand the dynamics between agricultural policies and adoption rates of ecological practices. We draw from different methodological frameworks, which outline the critical variables to include in a social ecological model, the relationships between variables in the model, and their underlying feedback behavior. In the context of Malawi, we investigate the adoption of maize-cowpea intercropping practices as a Striga control measure among smallholder farmers (from this point forward in the text, any mention of SCP refers to the cultivation of a maize-cowpea intercropping system). We combine qualitative and quantitative methods to first construct the model and then incorporate data and apply inputs; second, validate the model to run policy scenarios; and third, discuss the most effective intervention for sustaining SCPs. This comprehensive framework lays a foundation in understanding the intricate interplay between adoption and Striga emergence rates, but specifically, contributes to the development of research-informed policies aiming to promote sustainable agricultural practices tackling food insecurity.

Methodological framework

A mixed methods approach often integrates two or more methods simultaneously or in a sequential manner (Maxcy 2003). Our first set of methods was informed by the theory of resource-pool dynamics (see “Striga Spread Process” in Fig. 1), which explains how the spread of an invasive species (via wind, rain, human, or livestock) increases exponentially across an agroecosystem, only slowing once most of the landscape is saturated (Leffler and Ryel 2012). Then, our second set of methods was informed by innovation diffusion theory (Rogers 2003). From this theory, we draw from the Bass model of technological diffusion (Bass et al. 2000), which explains how information is shared between farmers via word of mouth until reaching market saturation (see “Intercropping Adoption Process” in Fig. 1). In combining theories from both the social and natural sciences, we created a social-ecological framework to assess the interaction between SCP adoption and Striga spread, and ultimately, its effect on food security in Malawi (see “Social-Ecological Dynamics” in Fig. 1).

Systems modeling is one research method used to develop simplified representations of agroecosystems and the diffusion processes (Reinker and Gralla 2018). These models are dynamic, as they highlight direct (and indirect) relationships between humans and nature (Beall and Zeoli 2008). Each relationship can create reinforcing (or balancing) behavior, guiding the fluctuation of a specific stock or rendering it unchanged. For example, social connectivity (e.g., sharing of negative information) can override the utility a smallholder farmer could gain from adopting an agricultural practice, regardless of its profit (Knowler and Bradshaw 2007, Kopainsky et al. 2012). High connectedness in systems, particularly between social factors, can create resiliency, but also resistance to change (Downing et al. 2014). Previous studies have used systems modeling to estimate the influence of social factors on adoption rates of soil fertility management technologies like intercropping (Tambang 2010, Grabowski et al. 2019). In this study, we created a system model based on data gathered from focus group discussions, discrete choice experiments, mediated modeling, and crop modeling (see Table 1).

Study area

The study was conducted over a one-year period in 2017 across two Central districts of Malawi, Dedza and Ntcheu, which are located in the Kasungu Lilongwe Plain (14.1667°S, 34.3333°E) and Rift Valley Escarpment (14.7500°S, 34.7500°E), respectively. Within these districts, four extension-planning areas were selected for data collection, namely Linthipe, Kandeu, Nsipe, and Golomoti. These extension planning areas were specifically chosen based on the growing challenge of Striga reported by farmers in the past decade (Silberg et al. 2020).

Focus group discussions

Three focus group discussions were facilitated in July 2017 across three extension planning areas to determine what practices farmers were aware of to control Striga and the sources that informed them about these practices. Participants were purposefully selected from a roster of smallholder farmers affiliated with the Africa RISING program. The program conducted action-based research with smallholder farmers in the region beginning in 2013 and ended in 2019. Each focus group discussion consisted of 12–15 participants. Discussion groups included lead farmers, managing demonstration plots or experimenting with different SCPs as well as those not affiliated with Africa RISING. In each group, an equal number of male and female participants were present to ensure one gender did not dominate the discussion (Fern 1982). Facilitators were extension agents and ensured all member opinions were heard.

During focus group discussions, smallholders were asked about the attributes of SCPs (e.g., maize yield return, intensity of labor) that influenced their decision to adopt them (refer to Appendix 1 for more description on questions asked). After, they were asked about the individuals and organizations that informed them about the SCP. A narrative analysis was conducted by uploading transcripts into MAXQDA® (VERBI GmbH 2019). The analysis informed the design of the following data instrument- a discrete choice experiment.

Discrete choice experiments

Following focus group discussions, we conducted discrete choice experiments. The method examines trade-offs that farmers are willing to make for one practice over another based on its attributes (Ortega et al. 2016, Waldman et al. 2017). In this study, the trade-off we determined was the quantity of maize (kg/ha) farmers were willing to sacrifice to lower Striga via intercropping (Silberg et al. 2020). In the experiment, 10 smallholder farmers together were presented six hypothetical scenarios (often referred to as choice tasks), one scenario at a time. In each scenario, smallholder farmers chose one of three options (i.e., two different SCPs or opt out). They indicated their choice privately by pointing to a card behind their back so as not to influence one another’s choices (refer to Appendix 9 for more description about the steps of the discrete choice experiment). Each option was presented as physical props representing five attributes with one of three levels (Silberg et al. 2023). The attributes included soil fertility benefit, Striga spread, labor requirement, legume yield, and maize yield.

A stratified sample of 215 smallholder farmers (n = 215) was taken from a roster (N = 298) to select participants from the Africa RISING program across four extension planning areas (Linthipe, Golomoti, Nsipe, Kandeu). From August to September 2017, 125 participants were selected to participate in the discrete choice experiment who were affiliated with farming households that expressed Striga as a primary challenge to maize production. The remaining 90 smallholder farmers were selected alphabetically by the last name of the household head until the total for each extension planning area amounted to 50–60 participants.

After the discrete choice experiments, the participants answered a questionnaire to reveal the sources that informed about the SCP (refer to Appendix 2 and 3 for more description about the sources). Then, to estimate the time it took to adopt intercropping, participants stated a range (in years) it would take them to cultivate an intercropping system across their entire field after learning about the SCP. These sources included word of mouth, observation, or other communication channels (Silberg et al. 2020). Then, the questionnaire asked if they adopted the SCP (based on that source), what outcomes occurred from adopting the SCP (e.g., increased maize yield), and finally, who they shared the results with.

Mediated modeling

Mediated modeling has been used to understand social ecosystems (Beall and Zeoli 2008) and the processes behind their longstanding issues such as failed adoption of sustainable practices (Pahl-Wostl and Hare 2004). In September 2017, we facilitated a mediated modeling workshop to exchange perspectives between three stakeholder groups (n = 12) about Striga to catalyze social learning (Van den Belt 2004, Schmitt Olabisi et al. 2016). Participants were purposefully selected and separated into three gender-balanced groups including (1) lead farmers from Africa RISING and agricultural extension development officers; (2) private sector employees (e.g., fertilizer salesperson); (3) local agricultural researchers and district level agricultural policy makers. Participants were first asked to define Striga emergence in a smallholder farmer field as a problem and the primary drivers of its spread across a wider social-ecological system. Then, they were asked to convert these problem statements into causal loop diagrams (Inam et al. 2015). In this sense, statements are broken down into several interrelated causes, one feeding into another. A detailed photo of the causal loop diagrams can be found in Appendix 7.

Causal loop diagrams are the basis of stock and flow models, represented as arrows connected to blocks, feeding/decreasing populations (Andersen et al. 2007). Participants converted diagrams into three models with the assistance of systems modeling researchers (illustrations of these models are in Appendix 8). Facilitators ensured all members contributed to the models by drawing three boxes within one another to represent scales. The inner most box represented a field, the next an extension area, and the outer most being the Central Region of Malawi. Variables were added to each scale and linked. As the workshop closed, each group’s model was connected to form a single model of stocks and flows. The lead investigator inquired where to get inputs to apply to the stocks/flows so as to run the model at a later date. Participants indicated inputs for social variables could be sourced from the discrete choice experiment and questionnaire data, but variables related to nature required data outside of the study (e.g., meteorological data).

Crop modeling

A crop model was developed (Silberg et al. 2021) from October to December 2017 using variables cited in the scientific literature. The crop model evaluated the extent intercropping controlled Striga emergence across a smallholder field. Model variables were applied with inputs found in local studies that investigated germination, flowering, seed dynamics, as well as other phases of the Striga lifecycle. The model allowed us to analyze Striga emergence in a one-hectare maize-based field managed by a smallholder farmer, and then, extrapolate the results of the weed spreading across multiple fields. Different smallholder practices affected specific stages of the Striga lifecycle (see Fig. 1 in Silberg et al. 2021). Smallholder knowledge about their effects on each phase varied (see section 4.1 in Silberg et al. 2020). One result from the model that was necessary to operationalize our wider social-ecological model was the length of time it took to eradicate Striga from a field by cultivating an intercropping system and/or applying fertilizer.

Developing a model to represent a social ecological system

Stock and flow model

The data collected from the four previous methods informed the variables to include and build a stock and flow model, named the Striga Emergence and Intercropping Adoption Model (SEIAM). The SEIAM was built using Vensim® (see Fig. 2) and its purpose was to serve as a decision support tool for evaluating hypothetical interventions intended to control the spread of Striga (across 20 years). The boxes in the SEIAM represent stocks, which act as outputs. These stocks are integral equations, simulating how a population fluctuates according to interacting flows (Forrester 2007). Flows are represented in Figure 2 by arrows pointing in and out toward the stock. Variables are connected to the flows (i.e., arrows), mediating how fast populations feed into or leave a stock (Kopainsky et al. 2012). The green arrows represent a positive event that would support the adoption of intercropping, while the red arrows represent a negative event that would deter adoption. The blue arrows represent a neutral event. If more than one arrow is connected to a variable, this indicates the variable is calculated by two or more combined inputs. A more detailed version of the model is presented in Appendix 6.

In the SEIAM, farming households from the general population can choose to adopt a SCP, making them “Striga Control Practice Adopters.” Then, they can remain with the SCP, discontinue (making them “Striga Control Practice Disadopters”), take up the SCP again (making them “Striga Control Practice Readopters”), or abandon the SCP (making them “Abandoners”). The adoption or disadoption of a SCP is driven by numerous variables, most notably maize yield (see variables connected to “SCP Adopters Received Post Outcome”). The percent of households that receive positive or negative outcomes is based on whether the maize yield they receive exceeds what they are willing or unwilling to sacrifice for intercropping. Maize yield is a function of SCP adoption, climatic conditions (rainfall and temperature), and fertilizer application (Ngwira et al. 2013, 2014).

Different variables in the SEIAM influence the percent of positive and negative outcomes (colored in green and red, respectively) households receive from having a SCP in their field. Adoption is driven by the four variables that begin with “Adoption rate via,” including word-of-mouth, observation, extension agent (Kabambe et al. 2013), and advertising (e.g., pamphlet, radio). As more fields with SCPs are seen, if optimal maize yields are met, more potential adopters can see these positive results and be encouraged to adopt. Growth of adopters increases the population of fields with SCPs, decreasing the cost of inputs as more legume seed become available locally. Oppositely, if more fields have Striga, weed emergence will also increase due to the spread of seed from neighboring fields. These paradigms are examples of feedback behavior. Feedback behavior is indicated in the SEIAM with pink and turquoise circular arrows with “R” and “B” located in the middle, respectively. Feedback loops are largely responsible for system behavior (Forrester 2007).

Variable specifications

All orange stocks are considered as farming households cultivating two fields each. Their initial populations were set according to summary statistics calculated from household questionnaire data. These statistics were then extrapolated across the Central Region. For example, the questionnaire found that 65.0% of households intercropped legumes in some form with their maize; therefore, the starting value for “Striga Control Practice Adopters” stock was calculated by multiplying 0.65 to 987,815 farming households. The same order of calculations was conducted to apply inputs for “Fields where Striga Emerged” and “Fields With Striga Control Practices” (i.e., yellow stocks). We acknowledge this percentage may exceed intercropping rates (44%) across the region (Katengeza et al. 2019), as the questionnaire was conducted in extension planning areas where various maize-legume intercropping systems have widely been promoted for +5 years (Waldman et al. 2017).

Rainfall data (from 1996 to 2016) was critical to calibrate the model. Competition for water between cowpea and maize (in an intercropping system) can increase during periods of drought (Rusinamhodzi et al. 2012), resulting in yield losses (e.g., 8–50%) and discouraging adoption (Silberg et al. 2019). Average rainfall in the past 40 years during the growing season (from November to April) was 1421 mm. If rainfall fell below 1065 mm, maize yield losses began to occur in the SEIAM. More specifically, if there were 12 consecutive days where rainfall did not reach 3 mm per day, fields with SCPs experienced higher yield losses (22.3%) than sole-maize (Arndt et al. 2016). Rainfall data used in the SEIAM indicated years 7–15 had favorable rainfall while the other years did not. Fertilizer application buffered maize yields (6.0%) in fields with SCPs (versus those without) against drought (Rusinamhodzi et al. 2012). Table 2 summarizes the sources used for each input. Equations and values for each input are stated in Appendix 4 and 5.

Model initiation

The SEIAM simulates Striga emergence with respect to SCP adoption across maize-based fields, beginning in 2016 and ending in 2036. These fields are confined to the Central Region, where 55,720km² are arable (Leete et al. 2013) and 38% percent (21,173.6 km²) is currently allocated to agriculture (SNDP 1998). We estimate that there are 1,192,139 households in the Central Region (NSO 2016, World Bank 2017), 85% which rely on agriculture as a primary livelihood. On average each household rents or owns between 1.96 and 2.00 ha of land, allocating 0.87 ha to maize cultivation (Katungi et al. 2017).

Typically, smallholder households cultivate two maize fields; a low and high fertility field where they apply or allocate less and more inputs to, respectively (Mhango et al. 2013). These fields range in size, but recent household questionnaires suggest they are 0.365 ± 0.052 ha/field (Li et al. 2021). Based on the following information, the SEIAM assumes one household consisting of 5–6 members (World Bank 2017) cultivates two maize fields that are 0.417 ha each, amounting to 823,838 ha (1,975,630 fields) of maize already being cultivated in the Central Region, leaving 528,658 ha (or 1,267,764 fields) to be cultivated with an intercropping system. Field population with SCPs, therefore, cannot exceed 1,352,496 ha (3,243,395 fields).

Model test and runs

There are different tests that can validate a model. We used the extreme-conditions test to assess and discover any flaws in our model structure (Barlas 1989). Afterward we conducted a sensitivity test to specify hypothetical policies to run scenarios in the SEIAM.

Validation and sensitivity

To conduct an extreme test, maximum/minimum values are inputted across several auxiliary variables to see if the result in the model (i.e., the output) demonstrates an S-curve. If the model demonstrates an unlikely output, then its structure, connections, and/or input values may not be an accurate reflection of a social-ecological system (Senge and Forrester 1980). For example, if the stock “Fields with Striga Control Practices” exceeds the “Total Maize Fields” (3,243,395 fields), it would be an inaccurate reflection of the system. We conducted several extreme conditions tests, modifying “Field Size,” “Probability of Eradication with a Striga Control Practice,” and “Climate” (specifically rainfall). After changing the inputs of these variables to their maximum or minimum values, output graphs illustrated an “S” curve. This showed an exponential increase in “Total Maize Fields,” before slowing to a plateau, thus, validating model output.

After validation, we conducted a sensitivity analysis. A sensitivity analysis is employed to determine resistance and/or resilience to model output against changes in inputs (Aivazidou and Tsolakis 2021). In social-ecological models, an empirical sensitivity analysis is recommended when variables do not have fixed inputs. One by one, we modified the inputs of three variables: “Adoption Rate,” “Disadoption Rate,” “Striga Emergence Increases,” and “Striga Emergence Decreases.” Inputs were adjusted by +/- 50% from their original values. We chose these variables because of their direct influence to the stocks “Fields where Striga Emerged” and “Fields WITH Striga Control Practices.” Then, we evaluated stock outputs against their business as usual (BAU) output. Input values were based on literature or by calculating an upper/lower bound for a confidence interval (see below in Table 3).

Scenarios run in model

We determined which hypothetical scenarios to run based on noticeable changes in stock outputs when modifying inputs. We conducted a scenario analysis to determine whether increased fertilizer subsidies, agricultural extension, demonstration plots, or a combination of the three showed noticeable changes in the model. We were focused on changes in “Fields with Striga Control Practices” and “Fields where Striga Emerged.” In a sequential manner, we ran one scenario, assessing its output, and then added others, keeping the previous scenario in place. Scenarios were run for 20 consecutive years (1996–2016). Each scenario is explained in Table 4 and based on past policies mentioned in the subsection “The state of Striga in Malawi.”

RESULTS

Summary statistics

Questionnaire data highlighted how different information sources affected the adoption, disadoption, readoption, and abandonment of SCPs (see Table 5). Socioeconomic characteristics were heterogeneous across participants (Silberg et al. 2020). Data applied to the SEIAM variables not only pertained to the adoption of maize-cowpea intercropping, but also the adoption of similar SCPs. This was done to gain a greater sample size for the study. The SCPs included manure, ash, maize bran, and fertilizer application, which are also referred to as soil fertility management practices.

The difference between the percentages for “Received information” and “Implemented SCP after” was smaller for agricultural extension (via agent) when compared to observation. This finding indicates that extension is a more effective strategy for increasing the adoption of intercropping. Households seemed to place higher trust in agricultural extension than field observation. More trust in extension when compared to observation is not consistent with the literature (Stone 2007); however, the finding indicates that adoption associated with observation can appreciate (and depreciate) quickly, which is consistent with literature (Kopainsky et al. 2012). The highest drop in trust occurred with advertisement (43.6%), resulting in a less effective strategy to disseminate information about intercropping. This may be due to the education level of smallholders or because no one is there to explain the intricacies of the practice. Maize yield resulted in the highest percentage of positive and negative outcomes reported by smallholders after adopting SCPs; thus, maize yield was the leading reason for disadoption (see Table 6). Of the 82.3% of participants who received a positive outcome from adopting a SCP, only 20.0% reported outcomes not related to maize yield gains. Of the 17.5% that received a negative outcome from adopting a SCP, only 5.0% reported outcomes not related to maize yield loss.

Farmers shared positive and negative information with peers differently via word of mouth. Positive outcomes (46.0%) were mentioned more frequently about all SCPs than negative outcomes (20.0%). Smallholders may have shared positive outcomes more frequently than negative outcomes with neighbors out of fear of seed landing on their farm from adjacent fields, thus, encouraging their neighbors to prevent Striga emergence via intercropping. Farmers tend to share negative outcomes about SCPs more than positive outcomes (Friedlander et al. 2013), although in this study, negative outcomes may have been shared less given the apprehension of losing agricultural extension (e.g., Africa RISING).

Mediated modeling results

Three causal loop diagrams and three stock and flow models were created at the mediated modeling workshop. These steps are outlined in Figure 3. The model created in Step 4 of Figure 3 was used as the base to build the more comprehensive SEIAM. The remaining diagrams and models are attached in Appendix 6. During Step 1 at the workshop, participants discussed how emergence decreased maize yield, which obligated farmers to increase sowing density to compensate for crop loss. As a result, soil fertility decreased because of continuous maize cultivation without replenishing land with nutrients, favoring Striga emergence. This cycle is presented in Step 2. In Step 3, participants created three smaller models; the first illustrating how Striga emergence was attributed to growing household size (e.g., more family members to feed); thus, shrinking landholdings and increasing land degradation. The second model shows how population growth among adopters decreased seed costs because smallholders could exchange more seed amongst themselves. The third model presents how observation of intercropping fields both drove and discouraged adoption, leaving farmers to discuss the maize yield gains or losses as a result from adopting SCPs. The three models were connected in Step 4, which were later expanded upon to create the SEIM, adding other variables to develop a full representation of the social-ecological system.

Model scenarios

We ran several policy scenarios in the SEIAM to determine how to increase SCP adoption over a 20-year period and whether a population of adopters could be reached to eradicate the invasive weed.

Social variables in the SEIAM

The Baseline Scenario demonstrated realistic results, as the population of intercropping fields rose and fell according to positive and negative trends of word of mouth/climate, respectively. For example, in Figure 4, sharp drops in intercropping fields occurred during Years 1, 3, 5, 15, and 16, which were years when abandonment was high because of unfavorable climatic conditions. According to current programs and growing conditions, intercropped fields will increase by 300,000 fields, representing only 9.25% of the 3.24 million fields available for intercropping in the Central Region. Negative word of mouth/observation strongly discouraged potential adopters to intercrop in later years, despite seeing 8 consecutive years of positive outcomes (i.e., Year 7 to 15). These overriding social factors indicate that without an intervening program, the long-term opinion and implementation of intercropping is jeopardized.

Scenario 1 illustrates that more demonstration plots in the Central Region will likely have little effect on the intercropping population, illustrating < 5.0% difference between the scenario and the Baseline. With the addition of agricultural extension (Scenario 2), there were several thousand additional fields when compared to the Baseline Scenario on any given year with favorable climatic conditions. More noticeable changes occurred when demonstration plots were combined with fertilizer subsidies (Scenario 3), as fertilizer compounded the positive effects SCPs had during years with favorable climatic conditions. This resulted in approximately 600,000 more fields with SCPs than the result in the Baseline Scenario in Year 20. If demonstration plots were replaced by agricultural extension (Scenario 4), this resulted in approximately 500,000 to one million more fields with SCPs in any given year. The reason for the large difference between scenarios with fertilizer subsidies and scenarios without, was that fertilizer increased the adoption rate of SCPs by as much as 29.0% in years with favorable climatic conditions and 17.0% in years without favorable climatic conditions. This effect buffered adoption against negative word of mouth. The result agrees with adoption rates observed in past programs involving fertilizer such as FISP (Koppmair e al. 2017). Extension (without fertilizer subsidies), on the other hand, had little effect on negative word of mouth during years with unfavorable climatic conditions, increasing the adoption rate of SCPs by only 6.0% (when compared to the Baseline Scenario).

Ecological variables in the SEIAM

Under the Baseline Scenario, the population of fields with Striga will grow from 1.14 to 1.47 million across the 20-year period (see Figure 5). The result indicates both fields with Striga and fields with SCPs will grow at similar rates. The reason for this equal growth is that the population of fields with SCPs is more sensitive to disadoption than fields with Striga. For example, disadoption can occur when a farmer experiences just one year of drought; however, Striga emergence in a field slows after 3 consecutive years (at a minimum). Even at peak of adoption in Year 15, where the population of fields with SCPs reaches 2.1 million, 1.13 million fields are experiencing a decreasing trend of Striga emergence, but still spreading seed to neighboring fields.

An intervention of solely demonstration plots (Scenario 1) only led to 6000 less fields with Striga when compared to the Baseline Scenario. With the addition of agricultural extension (Scenario 2), the difference between the Baseline Scenario was more pronounced, leading to 350,000 less fields with Striga. More noticeable changes occurred when demonstration plots were combined with fertilizer subsidies (Scenario 3), as synthetic inputs augmented the control effect cowpeas had over Striga, resulting in approximately 1.1 million less fields with Striga when compared to the Baseline Scenario in Year 20. If demonstration plots are replaced with agricultural extension (Scenario 4), the difference would be roughly the same as Scenario 3. Fertilizer inputs, decreased the time a field took to eradicate Striga while increasing maize yield, compounding the positive outcomes adopters received and spoke about to potential adopters. Scenarios 3 and 4 suggest that to decrease premature disadoption, safety nets, like fertilizer subsidies, should be provided to food-insecure households that generally drop a SCP after experiencing one year of negative outcomes (Holden and Mangisoni 2013).

Summary of scenarios

The results of the SEIAM illustrated that even under favorable climatic conditions, which would support intercropping, the spread of Striga emergence will be sustained across a 20-year period. Each Policy Scenario addressed the spread of Striga across the Central Region differently (see Table 7). Although Scenarios 1 and 2 increased the population of fields with SCPs, emergence still prevailed. Scenario 3 demonstrated a slight increase in population of fields with SCPs. Intercropping decreased the population of fields with Striga, but the population rebounded in later years. Scenario 4 echoed similar populations of fields with Striga across the 20-year time period, but increased the population of fields with SCPs to a threshold where Striga could not return. The finding argues that increased agricultural extension alone will not address the spread of Striga without providing some form of agricultural inputs to support intercropping.

Sensitivity

Variables indirectly connected to initial adoption were modified to find which one influenced the population of Fields with SCPs and Field with Striga most. Furthermore, we investigated which variables we could change marginally to have the most impact on the two stocks of interest. The social variable that caused the largest change in Fields with SCPs with the smallest change in its original value was “Probability of Receiving Agricultural Extension.” The ecological variables that caused the largest change in Fields with Striga with the smallest change in their original values were “Probability of Emergence with Striga Control Practice” and “Probability of Emergence with Striga Control Practice.” These social and ecological variables are illustrated in the more comprehensive model that can be found in Appendix 6.

Social variables of the SEIAM

Initial behavior observed in the SEIAM from our sensitivity analysis was that farmers shared both positive and negative aspects about intercropping, whereas agricultural extension development officers only shared positive aspects. Despite all the positive aspects agricultural extension agents share about intercropping, their effect on adoption could be cancelled out by one season of bad maize yields (see year 6 and 17 in Fig. 6 part “a”). Changes in the variable “Probability of Receiving Agricultural Extension” had little effect on “Fields with Striga” because of the persistent spread of Striga from neighboring fields. These fields came from the growing population of readopters that abandoned intercropping after receiving a second negative outcome (see part “b” in Fig. 6). Aggregately, these two outcomes in the SEIAM sustained a disadopter population cultivating enough fields without SCPs that could spread more Striga, no matter how large the population of the stock “Fields with Striga Control Practices” grew. The behavior of the SEIAM aligns with pest invasive theories, where a minimal population of weeds can override the influence of an intervention given their high fecundity (With 2002).

Ecological variables of the SEIAM

Augmented control of Striga emergence via intercropping decreased seed dispersion in the SEIAM (see part “a” in Fig. 7), but had little effect (if any) on adoption (see part “b” in Fig. 7). Little change in adoption was attributed to endogenous variables including “Adoption via Word of Mouth” and “Adoption via Observation.” The inputs of both variables comprise the several benefits received by implementing a SCP. The benefit of receiving lower Striga emergence was marginal when compared to the other benefits in the SEIAM, such as receiving higher maize yield. Maize yield will increase with intercropping, but only under favorable conditions and once Striga has been eradicated from a field (which takes up to five years). In a five-year time period drought can occur, leading to disadoption.

DISCUSSION

The SEIAM highlights several critical results for food security and weed management researchers and practitioners. Study insights regarding control and adoption rates can be applied to other weeds and control practices (respectively) elsewhere across Africa; however, model inputs would have to be modified. Under current dissemination and adoption rates in the Central Region of Malawi, Striga rates will persist over a 20-year period, even with six consecutive years of optimal climatic conditions that deliver sufficient maize yield with the SCP.

Without a sustained effort of limiting emergence to a threshold of < 500,000 fields, S. asiatica seeds will continue to spread across the Central Region of Malawi, input costs will persuade households to discontinue, and/or erratic rainfall will reduce yields enough where households will abandon the SCP. In terms of increasing adoption and managing Striga emergence, model results indicate that more economical interventions, such as fertilizer subsidies bundled with demonstration plots, are as effective as more costly interventions, such as fertilizer subsidies bundled with agricultural extension. This finding aligns with adoption studies stating that inorganic inputs are needed to sustain maize yields within intercropping systems during initial years of adoption (Denning et al. 2009, Jayanthi et al. 2013), but can be tapered off in later years.

The SEIAM differed from previous dynamic adoption models in five ways (see Grabowski et al. 2019, for example). First, the model was built by the very smallholder households it intended to simulate. Second, input values were calculated from adoption data collected from the same smallholder households. Third, it used choice model data to create yield thresholds to specify what households were (un)willing to accept from adopting SCPs. Fourth, maize yield response was verified with crop model data to estimate Striga spread with respect to SCP adoption. Fifth, the SEIAM estimated disadoption rates based on yield thresholds being met (or not), its subsequent effect on Striga spread as well as implications for food insecurity. These five processes were used to model the interactions between adoption rates and intercropping performance (as opposed to analyzing them separately), therefore better assessing the future efficacy of agricultural interventions (Marenya and Barrett 2007).

Although the study provided valuable insights for addressing Striga while considering factors related to food security, it had limitations. First, the SEIAM may have been too sensitive to drought. Generally, after a cowpea-maize intercropping system is implemented across multiple seasons, it can buffer maize yield against drought (Nyagumbo et al. 2016, Boillat et al. 2019) because legumes can improve soil structure, which is associated with higher water retention (Lunduka et al. 2012) and fertilizer response (Snapp et al. 2018). Hence, future models should include two different stocks, one as short-term adopters (1–3 years) and the other as long-term adopters (> 3 years), where the latter does not abandon abruptly when a drought event occurs because intercropping can make cropping systems more resilient to climate change. Although the SEIAM was not spatially parametrized (e.g., weed seed transfer per ha) to perform like longstanding crop models (see Masere and Worth 2016), it highlights key social ecological interactions.

CONCLUSIONS

This study contributes a novel mixed-methods approach to understand the dynamic behavior of social-ecological systems in Malawi and across Africa. By employing this social-ecological systems framework, we highlight feedback behavior between social and ecological factors across 20 years (1996–2016), which may override future policy interventions (Van Strein et al. 2019) related to the spread of Striga. The feedback loop between maize yield and farmer satisfaction with intercropping outweighed any other benefit the SCP could deliver. Policy interventions, therefore, only focusing on Striga control as a benefit of intercropping are unlikely to sustain adoption rates if they do not support the practice via agricultural inputs (Schulz et al. 2003).

The feedback between a small population of disadopter fields and seed spread supersedes a high population of adopters in terms of emergence across the region. Policy interventions that aim to manage the spread of Striga across a country must be cognizant of the population of fields with Striga emerging relative to the fields that have no SCP. They should not focus on an adopter population alone. The time needed for a SCP to eradicate a weed with respect to the population of fields needed to remain weed-free should be considered before fully investing in an intervention. Given that implementation rates differ according to information source, “trust development” should be a topic of discussion as new extension efforts are developed. Additionally, given that implementation rates differed according to information source, “trust development” should be a topic of discussion as new extension efforts are developed.

We recommend researchers and practitioners to assess interventions with multiple stakeholders more frequently using models that represent their social-ecological systems (Paas et al. 2021). If these interventions are effective, they can be presented as scenarios to policy makers and funders (Schlüter et al. 2019). Scenarios not only illustrate projected outcomes based on future investment, but also underlying factors that sustain (or threaten) food security. System modelers should consider increased climate variability because this will likely increase the volatility of adoption rates. The intention of the SEIAM was not to estimate exact outcomes of variables, but demonstrate behavioral trends between variables across space and time. Additional discrete choice experiments may be needed to recalibrate the SEIAM as population grows. The maize yield smallholders will sacrifice to adopt intercropping is likely to decrease as land per capita shrinks (Mponela et al. 2021). New theories can be used to address issues related to scale such as item response theory (Borges et al. 2023).

The structure and values applied to the SEIAM were robust in the eyes of the stakeholders (i.e., the farmers). Maize yield and adoption trends are responsive to extreme climatic events, adding to model credibility. Predicted outcomes should not be treated as exact figures, as parameterization of the model was based on basic processes behind crop and weed physiology. The objective of the study was to provide a credible framework and proof of concept for developing dynamic models that capture social-ecological interactions. Despite several shortcomings, a favorable view of the structure and results of the SEIAM by local stakeholders may be the most important result given their proximity to Striga emergence and its interventions. They were the ones that created multiple causal loop diagrams, connected diagrams across social and ecological boundaries, and finally, responded to questionnaires to provide data for the purpose of running scenarios. Had several causal loops been incorrect, model scenarios would have likely illustrated false results. The results of the SEIAM adds legitimacy to the collaborative approach employed to construct social-ecological systems. This approach can better position food security practitioners to create interventions, evaluate them, and determine if they are as dynamic as the weeds they wish to control and the farmers they wish to assist.

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