Terrorism and land use in agriculture: The case of Boko Haram in Nigeria

Land Use Policy 88 (2019) 104116

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Terrorism and land use in agriculture: The case of Boko Haram in Nigeria ⁎

T

Adesoji Adelaja , Justin George Department of Agricultural, Food and Resource Economics, Michigan State University, 204 Morrill Hall of Agriculture, 446 West Circle Drive, East Lansing, MI 48824, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Land use Terrorist attacks Farm households Land values Mixed cropping

In many developing countries, subsistence agriculture is the mainstay of the rural economy, improved land access and efficient land use are critical to short-term livelihoods and long-term economic transformation, and major shocks to land ownership, utilization and arrangements have far-reaching implications for farm families. In many such countries, armed conflict is emerging as a significant source of shock to agricultural and food systems, but its effects on land use are not well understood. This paper conceptualizes and estimates the causal effects of exposure to attacks on plot ownership, cultivated land, rented land, land values and cropping patterns while controlling for other factors. Using data on Nigerian agricultural households affected by Boko Haram, we find that an increase in the intensity of terrorist attacks results in an increase in the amount of land owned due to the abandonment of farms by neighbors and family members, increases the percentage of land left fallow, increases the average size of plots farmed, increases the average distance between plots farmed and the homestead, discourages mono cropping and encourages mixed cropping. Farmers’ expectations about the values of their lands also decrease with exposure to terrorism.

1. Introduction Agriculture is the predominant economic land-use in rural areas of most developing countries, especially countries in Africa (Pretty, 1999). About 80 percent of African farmers are small-holder farmers (SHFs) farming less than two hectares (Lowder et al., 2016) and their numbers are increasing (Headey, 2016). Slow economic growth in many African countries is consequently blamed on the slow rate of growth of SHFs. This slow growth rate is largely attributed to unfavorable land tenure systems, prohibitive land acquisition costs, gender-related barriers to ownership, limited access to complimentary technical inputs and lack of access to financial capital, amongst other factors (Pollakowski and Wachter, 1990). Among the constraints to small holder farms, land-related challenges are perhaps the most limiting to the transformation of agriculture. A basic tenet in economic development is that increased use and intensity of land in agricultural production will translate into improved production, rural incomes and livelihoods, and reduced poverty (Christiaensen and Demery, 2007). The expansion of land devoted to agriculture is difficult in the short-run. Therefore, much of the potential gains in agricultural production will come from increased use of irrigation and modern technology, as well as greater intensity of agricultural land use. However, losing land already devoted to farming is a

⁎

serious development setback, as the myriad of existing barriers make it difficult to recover from such losses. Farmland losses can be gradual (systematic) or sudden (shocks). Systematic losses result from (a) out-migration (Vesterby and Heimlich, 1991), (b) urbanization (Lopez et al., 1988), (c) economic slowdown (Reginster and Rounsevell, 2006), (d) environmental factors (Sekizawa et al., 2015), and (e) climate change (Pielke, 2005), amongst several other factors. Sudden losses, however, result from (a) policy changes (Mertens et al., 2000), (b) economic crises (Sunderlin et al., 2000), (c) unexpected political developments (Hostert et al., 2011), (d) transformational technological progress (Lambin and Geist, 2006) and (e) conflicts (Baumann and Kuemmerle, 2016). For households living in places which experience shocks, the magnitude of the impact will depend on the nature of the shock, lessons learned from previous experiences in dealing with such shocks, mitigating strategies provided through local, regional and national authorities, and assistance from active support systems and institutions. Because it is important to protect land already in production, shocks that disengage farmers from agricultural land and production or create massive losses in farmland should be seen as major development challenges. Better understanding of how such shocks affect individual land use decisions is crucial in building resilience against future shocks. In many developing countries, armed conflict is perhaps the most

Corresponding author. E-mail address: [email protected] (A. Adelaja).

https://doi.org/10.1016/j.landusepol.2019.104116 Received 18 February 2019; Received in revised form 18 June 2019; Accepted 25 July 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.

Land Use Policy 88 (2019) 104116

A. Adelaja and J. George

2. Background

damaging shock to land under cultivation (Eklund et al., 2017). Previous studies suggest that conflicts drive land-use changes in many ways, including the abandonment of agricultural land by internally displaced persons (IDPs) and refugees (Gorsevski et al., 2011); reduced utilization of land due to safety concerns (Eklund et al., 2017); lower agricultural intensity resulting from a dearth of labor and other inputs (Bozzoli and Brück, 2009), reduced investments in irrigation infrastructure due to fears about capital loss in areas affected by conflict (Arias et al., 2018); and increased forest loss due to inhabitation by non-state actors (Hecht et al., 2006; Ordway, 2015). However, most of these studies use cross sectional data to arrive at their findings and do not consider the temporal aspects of conflict-related shocks and may therefore not be sufficient to establish causality. There are very few existing empirical panel studies which analyze the impacts of conflict on land use decisions and the etiology of such decisions at the household level. Understanding how farm households make land adjustments in conflict zones is important as it helps in identifying policies to achieve resilience against shocks and in implementing post-conflict rehabilitation, resurgence and reconstruction strategies and projects. In this study, we attempt to fill the gap in knowledge about the land use impacts of conflict by quantifying the causal effects of terrorist attacks by Boko Haram (BH) on key agricultural household land-use decisions. Terrorism is one of the most lethal forms of armed conflict. Boko Haram is designated as a Foreign Terrorist Organization (FTO) by the U.S Department of State. Although Boko Haram’s attacks can be classified as acts of terrorism, the group’s methods, especially its need to control physical territories, makes it different from many other terrorist groups. For the same reason, the agriculture and land use implications of the Boko Haram insurgency is directly comparable to the impacts of armed conflicts in general. In our analysis, we question whether a household’s exposure to conflict, measured by the number of casualties in its Local Government Area (LGA) of residence, significantly affects its land use choices using a panel database from a sample of Nigerian households across three time periods (2010–11, 2012–13, 2015–16). Indicators of land use choices that we evaluate include (a) the size of agricultural land owned, (b) the number of agricultural plots owned, (c) the percentage of agricultural land cultivated, (d) the percentage of land left fallow, (e) the ways by which land is acquired, (f) the expected value of land and (g) cropping patterns. We also examine the effects of other control variables such as rainfall, household size, distance to the nearest market, and road access on land use decisions. Our findings suggest that households living in the Boko Haram conflict zone gain access to more land possibly due to free transfers by farmers abandoning their fields. However, the lack of access to agricultural inputs, absence of support systems and disruption of output markets seem to force these farmers to keep their land idle during the farming season. We also find evidence of households engaging in more diversified cropping patterns as a response to the conflict related shocks. This is consistent with previous findings that during conflicts, households increase the relative shares of low-risk, low-return activities. In addition, we contribute to the relatively scarce literature which use longitudinal data to analyze the household level impacts of conflicts. We organize the rest of this paper as follows. In section 2, we explain the range of conditions that may prevail on the landscape when an area faces conflict using the case study of Boko Haram as an illustration. In section 3, we conceptualize a wide range of effects of conflict on production choices, land ownership arrangements and land values and present a theoretical model of the effect of conflict intensity on land use. In section 4, we present an empirical framework section where the data, methodology, analytical models and empirical strategy are discussed. A results section follows in section 5 and the paper ends with a section on conclusions and policy suggestions.

Terrorism, in general, can be defined as “the pre-meditated use or threat to use violence by individuals or subnational groups to obtain political or social objectives through the intimidation of a large audience beyond the immediate victims”(Enders and Sandler, 2012). Terrorism falls within the general class of armed conflicts and can take several forms, including territorial terrorism. Territorial terrorism can be defined as acts of violence by terrorist organizations which possess actual control over territories or with stated goals or ambitions challenging the territorial status quo of a state or polity (Castan Pinos and Radil, 2018). In many cases, a territorial terrorist group seeks to create a parallel state, with physical control of land, and tend to operate under a code of law which is distinct from the host country. Armed conflicts can lead to various land use and land administration issues in postconflict societies (Todorovski et al., 2016; UN-HABITAT, 2007). Since territorial terrorism is similar to other forms of persistent armed conflicts such as civil wars due to the tendency of terrorists to control land and resources, we expect the adverse impacts of armed conflicts on agriculture and land use to be relevant in regions affected by territorial terrorism. Earlier academic narratives about terrorism concentrated largely on a “deterritorialized discourse” about terrorism (Castan Pinos and Radil, 2018). Sloan et al. (1978) claimed that terrorist groups were self-evidently non-territorial as their actions were “not confined to clearly delineated geographical areas.” There are suggestions that territorial ambitions would constitute nothing but a hindrance to terrorist groups, a liability that would make them more vulnerable to counterterrorist measures (Knoke, 2015). In support of the territorial terrorism narrative, Elden (2009) argues that for many modern Islamist groups, imagined territories are motivating goals and they are willing to test models of such territories in certain physical spaces at their disposal. Also, for terrorist organizations such as the Islamic State (IS), Euskadi Ta Askatasuna (ETA), BH and the Revolutionary Armed Forces of Colombia (FARC-EP), territorial control opens up a wide range of possibilities in terms of tactics, allowing them to diversify their strategies, including engaging in guerrilla-oriented actions (Castan Pinos and Radil, 2018; de la Calle and Sánchez-Cuenca, 2015). Boko Haram is a good example of a territorial terrorist organization. The term Boko Haram is a combination of two Hausa words, “Boko” and “Haram”, meaning non-Islamic education is forbidden (Adelaja and George, 2019). BH ideologically places itself against a Western-based cultural intrusion through education, which they believe threatens traditional values, customs and life style among Muslims in northeastern Nigeria. At the height of the BH insurgency, 26 of the 27 local government areas of Borno State were either fully or partially occupied by them(Cooke et al., 2016). Using this territorial base, the group carried out several attacks on police stations and military bases; local, state and national level politicians; religious leaders; ordinary civilians and children. Much of the territory occupied by BH were rural areas where agriculture is the predominant industry and where the economies had been compromised for years. Although BH’s stated objectives do not include the destruction of the agricultural sector and food security, their attacks directly and indirectly affected agriculture, rural households and their livelihood activities and increased food insecurity. The micro-economic impacts of territorial terrorism are likely to be larger than those of other forms of terrorism. The continued presence of a terrorist organization that is staging attacks in an area would have significant effects on the livelihoods of people and their daily activities. The micro-level impacts of terrorist attacks on individual households in terror-prone regions could be long-term and persistent, as economic activities shift from terrorism-prone regions to safer regions within the country (Sandler and Enders, 2004). Even when the acute conflict situation recedes, the continued presence of military and other counterterrorism forces makes routine economic activities more complicated. 2

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about possible exposure to future attacks and associated travelling risks may encourage household members to stay close together and close to home. This could lead to reduced application of labor in planting, weeding, harvesting, marketing and sales in conflict-affected regions and result in unrealized production (when conflict arises after planting) or the idling of land (when conflict arises before planting). Hence, the anticipated effect of conflict on productive land use activities, through effort reduction or stoppage, is negative. Farmers may decide to stay in the attack vicinity or run away to other communities, depending on their tolerance for Boko Haram and their capacity to migrate. Because farmers must restrict their movement to areas that are deemed safe, they face reduced access to input markets and difficulties in selling harvested crops to high bidders. This economic burden is in addition to the levies and taxes imposed by Boko Haram in exchange for not burning farmers’ crops. For example, in the Chwawa area of Madagali, Boko Haram imposed levies on farmers, ranging between one to three million naira or $6000 to $18,000 (IRIN, 2015).

The post-terror social, political and economic environment is likely to be different from the past for affected households, as they search for a new normal. Since 2009, the Northeastern Nigerian states of Borno, Adamawa and Yobe (BAY States) have struggled with continuous terrorist attacks from BH as it aimed to disrupt the country’s democracy. The other northeastern states of Bauchi, Gombe and Taraba (BGT) have also been occasionally hit, but have had to deal with major problems associated with the high population of Internally Displaced Persons (IDPs) who stress their resources, infrastructure and budgets. In the Northeastern Nigeria, agriculture is the predominant land use, agriculture is highly dependent on land, and many attacks have been in rural areas. These factors make it a region to test the impact of conflict on household land use decisions. It is widely believed that the BH insurgency compromised agriculture (see Adelaja and George, 2019; Kah, 2017). Anecdotal claims made about how the BH insurgency led to the destruction of the agricultural sector include claims about the complete and partial abandonment of fields, uncertainty about how much area is to be cultivated, shifts in cropping patterns, and constraints to acquiring land for agricultural purposes. For example, Kah (2017) specifically reports that vast areas of land in Northeast Nigeria were under-cultivated or underharvested as a result of attacks and conflict-related fears. Other anecdotal claims about the influence of conflict include increased prices of major commodities (Awodola and Oboshi, 2015). In conclusion, despite growing concerns about the impact of conflict on agriculture, empirical evidence of the effects of Boko Haram insurgency on land use is limited.

3.1.3. Abandonment of farm fields A major pathway through which violent conflicts could affect land use is through population displacement and the resulting abandonment of farms. In northeastern Nigeria, the center of the Boko Haram insurgency, at least 70 percent of all displaced people are farmers (Haruna, 2018). Farmers are forced to flee their communities and fields, even during the cropping season, in the fear of imminent attacks. For example, in 2013, 19,000 rice farmers were forced to abandon their fertile fields near the Sahel region, leaving behind around 24,700 acres of rice paddies unharvested (Associated Press, 2013). Moreover, because the migrating population includes not only farmers, but also food sellers and transporters, the effects of conflict through abandonment is exacerbated. We therefore expect that the effect of violent conflict on land use, through the abandonment of farm fields, is negative. However, it is also possible that farmers who decide to stay in a conflictaffected region may have access to more land from free transfers from friends and relatives who flee the conflict zone.

3. Conceptualizing the effects of violent conflict on land use 3.1. Hypothesized effects Consider the case of a region that previously had no armed conflict experience. The performance of the agriculture sector in the region will be subject to normal market (demand and supply), human capital (labor availability and productivity), locational (access to roads, input and output markets), environmental (soil quality, rainfall) and technological (irrigation, equipment, expertise) factors. Now assume that this stable system is perturbed by terrorist attacks. We conceptualize the following ways in which such conflict would impact on the agricultural system and, therefore, on land use choices, based on anecdotal information and theoretically based expectations.

3.1.4. Takeover of farm control One of the unique aspects of territorial terrorism, is the strong interest by the terrorist organization in controlling a territory. This is tantamount to controlling land. Terrorist organizations with territorial ambition often provide food and cash to operatives to induce them to participate in terrorist activities. When existing farmers stay on their land, they are threatened to contribute their production towards the needs of the terrorist organization (Adelaja and George, 2019). When such farmers leave, however, operatives are often used as farm operators to produce food to meet the food security needs of the terrorist organization. Hence, the relationship with Boko Haram might be an important factor which determines a farmer’s decision to stay or leave the affected region (Agbiboa, 2013). Whether or not this leads to expanded use of land or increased productivity is partly an empirical matter.

3.1.1. Human casualty, disability and injury Some of the immediate effects of terrorist attacks include human casualties, injuries and disabilities. From 2010–2016, through their attacks, Boko Haram killed over 35,000 people (Council on Foreign Relations, 2018) many of whom were farmers. Hospital facilities in the Northeast are overloaded with injury cases that require treatment and surgery. These must have resulted in reduced labor supply in the agricultural sector. Deaths can throw families in disarray, especially when those killed are key family decision makers on land and farming. The death of a male household member can change the dynamics of responsibility in the family. Young people or school age children may have to provide stop-gap or permanent support on the farm. Death also creates a situation where land ownership can become more contestable. Injuries and disabilities, on the other hand, mean that less than full time equivalent effort is provided by injured or disable workers. With respect to disability, debilitating effects on agriculture have been examined by Kidd et al. (2000); Field and Jones (2006) and Deboy et al. (2008). The anticipated ultimate effect of deaths, disability and injuries is the reduced application of land to agriculture through reduced manpower.

3.1.5. Returning farmers Farmers who return to their fields when the insurgency temporarily or permanently recedes are met with hostile conditions which prevent them from readily resuming normal farming operations. Fields are often deliberately laced with landmines, creating unsafe conditions for farmers to cultivate crops (Isuwa and Searcey, 2016). Since agriculture is not always considered as high a priority as schools, health clinics, water systems, home reconstruction and roads, security and military agencies may not always want to devote large numbers of troops to the detonation of landmines, at least not immediately. In addition, even if farmers resume cultivation, support systems such as agricultural infrastructure and extension services will take much more time to be established. In the case of northeast Nigeria, the FAO and other

3.1.2. Reduction or stoppage of agricultural activities A key choice facing a farm household is whether or not to compensate for the loss of labor hours due to reduced availability of manpower from the human casualty, injury and disability effects. Fears 3

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where L¯ is the labor endowment, A¯ is the land endowment, P is the output price, and at and wt are the rental rate for land and the wage rate, respectively. Denoting the farm profit as πt , Eq. 3 can now be expressed as follows:

multilateral and international organizations, the Presidential Initiative for the Northeast (PINE), the Presidential Committee on the Northeast Initiative (PCNI), and the National Emergency Management Agency (NEMA) have stepped up their efforts to rehabilitate returning farmers by providing seeds and other agricultural inputs. However, these efforts fall short of their targets due to money shortage, bureaucratic corruption and lack of cooperation by local officials.

It = πt + (1 + r ) bt .

The asset holdings, b , changes over time as a function of income (It ) and consumption, (Ct ) as follows.

3.1.6. Other effects Land rental prices are certainly not expected to rise. Reduced demand is expected to result in reduced rent. Land values are likewise expected to decline, considering the population flight and the loss of markets. Finally, the impacts on cropping patterns are expected to favor mono-cropping, rather than mixed cropping. Chauveau and Richards (2008) showed that crops like Cassava that are easier to manage can be left unattended for a while are more popular in conflict zones. In the Northeast, mixed cropping is usually targeted toward a robust market. When such markets do not exist, one expects a concentration of efforts on chronically needed crops for survival and nutrition.

bt + 1 = It − Ct

max λ t = u (x t ) + βE (V t + 1 (Qt + 1, bt + 1, εt ))

subject to Qt + 1 = f (Lt , At , εt )

(8)

(9)

Note that E (.) denotes expected value. From the first order conditions for optimal input choice (see Appendix A), we can obtain the input demand functions for At and Lt as follows:

Lt = L (a, w, p , ε )

(10)

and

At = A (a, w, p , ε )

(11)

Eq.s (10) and (11) suggest that actual land and labor used in production, At and Lt are both functions of the rental rate for land, the wage rate, product prices and conflict-related shocks. However, land actually used in production can be lower than total land owned or operated by the farmer because the farmer can choose to idle some of the land. Furthermore, the farm household may have access to free land or land abandoned by neighbors and relatives. Therefore,

(1)

At = A¯ − AIt

where x t denotes the goods consumed by the household, β is the discount factor, b denotes all assets held by the household, including savings, and δ denotes the rate of return from such assets. Assume that the household’s farm production function as continuous, strictly concave and increasing in input arguments. That is,

(12)

where A¯ is the family’s ownership of land and AIt is the amount of land idled. Note that in Eq. (12), it is assumed that no land is rented in or rented out and no land is acquired or given away for free. However, Eq. 12 is easily adjusted to account for these possibilities. Since A¯ is purely fixed in the short term, AIt can be represented as:

(2)

where Qt , Lt and At are, respectively, the output produced, labor used and land actively farmed or in agricultural production in time period t. Also assume that the elements of εt are independently and identically distributed vectors of random variables representing conflict-related shocks. We assume that each shock variable is purely exogenous and its manifestation is dependent on its causes and type of conflict. For example, we assume that a terrorist attack is planned and perpetrated by a terrorist organization and its occurrence is totally exogenous to the farm household. Similarly, while the farm household may indeed participate in a riot, it can be safely assumed that the implementation of the riot itself is outside its control. Denote the full income of the farmer as It . The income constraint facing the household can be expressed as the sum of the farm profits and the return from all the assets owned or operated by the household. That is,

It = wL¯ + aA¯ + PQ (Lt , At , εt ) − at At − wt Lt + (1 + r ) bt

(7)

bt + 1 = (1 + r ) bt + PQ − wLt − aAt − Px t

t=T

Qt = Q (Lt , At , εt )

xt, At , Lt

and

To guide our empirical analysis, we present a simple intertemporal utility maximization model for agricultural households to explain how conflict-related shocks may impact on land allocation between agriculture and other purposes. Consider the case of a representative agricultural household with a planning horizon of t = 0, 1, …, T where T is the perceived terminal period. The utility function of the household is represented as follows:

t=1

(6)

Based on Eq.s (1)–(6), we define V t (Qt , bt , εt ), as a value function for the household’s problem for time-period t. V t (.) is the maximum expected present value of the utility derived from time period t to T and represents the dynamic equivalent of the household’s indirect utility function. The optimal choice of x t , At and Lt can be obtained by applying the implicit function theorem to the following problem:

3.2. Conceptual model

∑ βu (xt ) + βT+1 δ (bt+1).

(5)

From Eq.s (4) and (5),

bt + 1 = πt + (1 + r ) bt − Ct

3.1.7. Overall land effect From the above, it appears that most, if not all of the anticipated effects of conflict on land use parameters are negative. We expect the size of agricultural plots farmed to decline due to fear, migration, deaths and injury; and the number and size of agricultural plots owned to increase as abandoned land is placed in the hands of those who remain. Also, we do not expect significant land sale transactions in the short-run. For example, the destruction of livelihoods may make it difficult to find buyers, even when sellers are eager to sell.

U (x t , bt ) =

(4)

AIt = A (a, w, p , ε )

(13)

In the conceptualization of the household’s problem above, we have introduced conflict-related shocks as production shocks. However, it is also possible that input prices are also affected because of conflicts. In such cases, conflict affects the demand for agricultural land mainly through the effects on the price of land and directly through ε . That is, taking the derivative of At with respect to conflict shocks, we obtain

∂f ∂f ∂a ∂At . + = ∂ε ∂a ∂ε ∂ε

(14)

The first term on the right hand side represents change in demand for land due to the effects through land prices. In areas experiencing severe conflicts, the value of land could approach zero due to abandonment of farms following forced displacements of farmers. For households which choose to remain in conflict zones, this may mean greater availability of land due to free transfers, and temporary

(3) 4

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Table 1 Summary statistics. Variable

Obs

Mean

Std. Dev

Min

Max

Percentage of land cultivated Percentage of land left fallow Total area owned/operated Number of plots Average plot size Average distance of plots from household Percentage of land rented in Percentage of land purchased Percentage of land acquired for free Percentage of land acquired by family inheritance Average rent paid per sq.mt Average land value per sq.mt Percentage of land with mono cropping Percentage of land with inter cropping Percentage of land with relay cropping Percentage of land with mixed cropping Percentage of land with alley cropping Percentage of land with strip cropping Number of casualties

8,151 8,151 8,158 8,158 8,158 7,951 8,158 8,158 8,158 8,158 8,158 8,106 7,679 7,679 7,679 7,679 7,679 7,679 8,072

98.9 1.3 10184.6 1.9 5821.3 2.5 7.6 5.8 11.0 75.6 0.8 2664.2 28.5 3.2 1.2 66.5 0.4 0.1 1.9

14.4 9.3 17852.0 1.0 11867.5 3.7 25.8 22.1 30.8 41.8 28.2 80456.7 40.4 15.9 9.8 43.0 6.1 2.9 16.0

0 0 0.2 1 0.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

100 100 639600 11 639600 145 100 100 100 100 2430 5350000 100 100 100 100 100 100 318

per acre variables are measured in naira per square meter. Rental value is calculated as the total amount of money paid by the household during one year preceding the interview date to acquire the management rights of the rented land. The land value variable is measured as the price for which the person managing the plot was willing to sell as reported in the questionnaire. Since very few actual sales take place in the conflict-affected areas, we use the expected sales value reported by the household head as a proxy for actual sales value. To explore how conflict affects cropping practices, we also construct six variables to measure the types of cropping patterns employed on the plot: (1) mono-cropping, (2) inter-cropping, (3) relay cropping, (4) alley-cropping, (5) mixed cropping and (6) strip cropping. Each is measured as the percentage of land devoted to each cropping practice. Mono-cropping is the agricultural practice of growing the same crop year after year on the same land, without crop rotation through other crops. It allows specialization in equipment and crop production. Intercropping is the practice of sowing a fast growing crop with a slow growing crop so that the fast-growing crop is harvested before the slow growing crop starts to mature. Relay cropping refer to the agricultural practice of cultivating two crops where the second crop is planted following the harvest of the first crop to get more benefits. Mixed cropping is the most basic form of cropping. The component crops are totally mixed in the available space. Alley cropping involves the crops arranged in alternate rows e.g. in a ridge where groundnut is planted in alternate rows with Melon. Finally, Strip cropping is a variation of row cropping where multiple rows (or a strip) of one crop are alternated with multiple rows of another crop. The Armed Conflict Location & Event Data (ACLED) provides information on Boko Haram’s insurgency incidents in Nigeria during the 2010–2016 period (ACLED, 2017; Raleigh et al., 2010). ACLED reports a range of political violence events, demonstrations and relevant nonviolent avtivities by agents such as governments, rebels, militias and communal groups from 1997-2016. According to ACLED, from 2009 to 2016, a total of 2002 Boko Haram incidents were recorded. Borno, Adamawa and Yobe states accounted for 80% of these attacks. Over the same period, these attacks resulted in 13,273 casualties and a significant volume of other damages. The geocoded data allows spatial matching at the household level. For the purpose of the current analysis, we spatially join the two datasets at a Local Government Area (LGA) level. Table 1 presents the summary statistics for all variables used in our analysis.

management arrangements and caretaking opportunities. The second term shows the production shock inflicted by conflicts through various inputs. Hence, the overall effects of conflict on agricultural land holdings depends on the signs and relative magnitudes of both terms. 4. Empirical framework 4.1. Data We utilize two main data sources in our empirical analysis. The data on land use outcomes and other household level control variables are from the General Household Survey (GHS) by the Nigerian Bureau of Statistics. The GHS was collected as a part of the Living Standards Measurement Survey (LSMS). It extends across three waves, 2010–2011, 2012-13, 2015-16 and for both post-planting and postharvesting seasons. The survey is nationally representative and covers all 36 states in Nigeria and the Federal Capital Territory (FCT) of Abuja, including the northeastern states of Borno, Adamawa and Yobe. The survey participants were identified prior to the outbreak of the conflict. Out of the 4916 total households interviewed in the first wave, 90% were included in the two subsequent survey rounds. The information was collected at three different levels: household, farm (or plot) and community. Our basic unit of analysis is the household level. That is, we aggregate different farm level variables to the household level. We acknowledge the possible effects of a 10 percent attrition rate and its implications for a potential bias. This secondary data, however, has been used in several conflict-related studies (see Adelaja and George, 2019 and Bertoni et al., 2018). We explore a variety of land use outcomes as dependent variables in our empirical analysis. These are described next. The total acreage of land owned or managed by the household is measured in square meters. During each wave of survey, the enumerators collected accurate information on the number and size of plots managed by the household. Each household was also required to report the amount of land allotted for cultivation and left fallow separetely. Using this information, we construct two variables: 1) the percentage of land used for agricultural purposes and 2) the percentage of land left fallow. The average distance of plots from households is measured in kilometres. We also use data on the ways in which each plot was acquired by the household to construct four percentage measures to capture the mode of land acquistion: 1) land rented, 2) land purchased, 3) land obtained for free and 4) family inherited land. Participants were specifically asked about their perception of the value of their lands. Data on the rentals came out of the structured part of the questionnaire. The rental value and land value 5

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household members for farm labor. Since conflict mainly affects hired labor, small holder farmers can still retain the land under cultivation by substituting family labor for hired labor. The finding that agricultural land in use is not significantly impacted by conflict is also consistent with previous studies which suggests that the adverse effects of conflict on agriculture is mainly realized through a reduction in productivity (Adelaja and George, 2019). One implication of this result is that in an environment of subsistence farming, there is some degree of resilience exhibited by farmers in terms of land actually farmed. As shown in models 4–6, an increase in conflict intensity significantly increases the percentage of land left fallow by the household. With land cultivated not reduced, one expects such fallowed land to come from abandoned lands by family, friends and neighbors. In northeast Nigeria, millions of farm household members fled rural areas for safer havens in nearby cities. For example, up to 2.5 million people fled to Internally Displaced Persons (IDP) camps and host communities. Those that fled are more likely to be those who were directly attacked or those whose farms were mined by terrorists. Next, we summarize the findings regarding the control variables. In pooled OLS models, the age of household head and annual rainfall are inversely related to the percentage of land cultivated whereas an increase in the distance to the nearest administration center increases land under cultivation. However, when we control for the household and survey year fixed effects, these results are not significant. The distance to the nearest market and to an administration center are inversely related to the percentage of fallow land. This suggests that farmers in remote areas take farming more seriously while farmers that are near cities and population centers are mostly “gentleman farmers”. In Table 3, we present the estimated effects of the Boko Haram conflict on the total area of land and the number of plots managed by the household. Conflict has a significant and positive effect on the total area of land managed by the household. The finding that the amount of land holdings owned or managed by households does not decrease in the presence of conflicts combined with results from Table 2, suggests that an increase in conflict intensity cause households to manage more land, most of which are kept fallow due to barriers to expanded farming. Such barriers may include the lack of agricultural inputs such as seeds, fertilizers and herbicides; loss of markets for products; decrease in labor availability; absence of support systems; difficulties in gaining access to credit and safety and security related concerns. However, the number of plots were not significantly impacted by conflicts. In Table 3, the positive relationship between land size and distance to population centers and border crossings suggest that naturally, market proximity translates into larger land holdings. The effect of rainfall on land owned is negative, suggesting that the higher productivity from higher rainfall results in reduced land holdings. In Table 4, we examine the effect of conflict on the average plot size managed by the households and the distance between such plots and the household’s residence. As shown in models 1 to 3, an increase in conflict intensity significantly increases the average plot size for sample households, suggesting households’ preferences for larger plots in conflict-affected regions. The economies of scale from using larger plots may help households to cushion the negative impacts of conflict-related shocks. In models 4–6, the effect on average distance of plots from households is examined. Surprisingly, the results suggest that the higher the conflict intensity, the farther away the location of the farmer from their plots. This result is contrary to the conventional wisdom that farmers will prefer plots closer to their place of residence due to security risks associated with travelling in conflict zones. However, previous findings suggest that conflict intensity significantly affects the availability of inputs including seeds, fertilizers and labor. Hence, for households to gain better access to farming inputs and continue safe farming, the agricultural plots need to be located farther away from places with high conflict intensity. This could potentially explain the positive relationship between conflict and the farm-household distance.

4.2. Empirical Strategy To operationalize the conceptual model presented above, we use two different estimation techniques to quantify the effects of the Boko Haram insurgency on land-related variables. First, we used pooled OLS regressions, where multiple land use variables are used as the dependent variables. The empirical model is expressed as

Yist = α + Θattacksist + ФXist + £ist

(18)

where Yist represents the land use indicators such as the number of agricultural plots owned, size of agricultural land, land rental practices, land valuation and cropping patterns. In Eq. 18, α is the intercept term and the attacksist variable is the number of Boko Haram insurgency incidents occurred in the respective LGA of household i in time period t. Xist is a vector of household level control variables which potentially affect land use decisions. These variables include age and educational level of the household head, rainfall and road access in the region sorrounding the household. The £ist term is the idiosyncratic error term. Second, we also estimate a household level fixed effects model in order to utilize the panel nature of the data. Pivovarova and Swee (2015) suggests that individual heterogeneity is an important, but often neglected factor in empirical studies involving the impact of conflict. It is plausible that the likelihood of conflict happening in the household’s geographical location and the land use decisions of a household are jointly determined by some unobservable household level preferences and characteristics. In addition, the mobility of owned assets and skill levels of income earning members of the family help determine the migration ability of households from high to low conflict intensity areas during a conflict. However, in most cross sectional studies involving post-conflict surveys, the selection bias due to non-random conflict displacement is assumed away (Pivovarova and Swee, 2015). This hints at the presence of a sample correlation between conflict intensity and the land use decisions of the affected households, independent of the effects of attacks on land use (Swee, 2015). Hence, we introduce effects models to control for the “unobserved traits” that may influence the propensity of displacement and the land use. The resulting empirical model can be represented as follows:

Yist = α + Θattacksist + ФXist + μ i + £ist

(19)

where μ i represents the household level fixed effects and £ is the error term. 5. Empirical results Tables 2 to 7 report the results for the pooled OLS and fixed effects regressions where household level land ownership, land size, land acquisition, land use, land value and cropping pattern measures are regressed on the household’s exposure to conflict and various control variables. We prefer to base our findings on the results from fixed effects specification models as they control for the unobserved heterogeneities. However, we also report the pooled OLS results for comparison purposes. 5.1. Effects of conflict on land use In Table 2, we report the results from the regressions where we use the percentage of land allocated for agricultural purposes and the percentage kept fallows as dependent variables. In Table 2, models 1 and 4 report pooled OLS regression results, our baseline specifications. Models 2, 3, 5 and 6 include household and survey year fixed effects and LGA specific time trend variables. As shown in models 1–3, conflict intensity does not significantly affect the percentage of land under cultivation. This intriguing result suggests that in conflict-affected regions, households do not necessarily reduce the land under cultivation owing perhaps to their food needs. Many households in conflict-affected regions in Nigeria are subsistence farmers, who depend on their 6

Land Use Policy 88 (2019) 104116

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Table 2 Effects of conflict intensity on agricultural land holdings. Percentage of land under cultivation

Casualties Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

Percentage of fallow land

(1)

(2)

(3)

(4)

(5)

(6)

−0.016 (-1.54) −0.060 (-1.21) −0.020** (-2.08) −0.001** (-2.33) 0.014 (0.75) 0.004 (1.25) 0.004* (1.66) 0.001 (0.59) 100.851*** (141.40) NO NO NO 8037 0.004

0.046 (0.55)

0.042 (0.51) 0.251 (1.31) 0.060 (0.98) −0.005 (-0.31) 0.046 (1.43) −0.033 (-0.58) 0.002 (0.20) 0.016 (0.37) 97.680*** (7.98) YES YES YES 8037 0.064

0.013 (1.30) 0.022 (0.78) 0.022*** (3.15) 0.001** (2.01) 0.001 (0.22) −0.006*** (-2.68) −0.004** (-2.30) −0.001 (-0.77) −0.268 (-0.63) NO NO NO 8037 0.006

0.037* (1.93)

0.039** (2.01) −0.092 (-0.84) 0.001 (0.07) −0.012 (-1.30) −0.021* (-1.67) 0.035 (0.55) −0.010 (-0.84) 0.022 (1.08) 8.280 (0.76) YES YES YES 8037 0.087

99.930*** (175.61) YES YES YES 8065 0.062

0.952*** (4.17) YES YES YES 8065 0.086

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01.

land rented in, 2) percentage of land purchased, 3) percentage of free land acquired and 4) percentage of family inherited land. According to models 1–4, a change in conflict intensity has no significant effect on the percentage of land rented or purchased. This is not surprising, given the near absence of active land markets in areas affected by conflicts. Due to safety concerns, market demand for land is almost zero, thereby suspending any sales or rentals of land. However, as seen in models 5 and 6, an increase in conflict intensity increases free land acquired as a percentage of total land holdings. The estimated positive effect remains significant even in the presence of control variables. This result suggests that forced displacements may cause farmers to temporarily transfer

Another explanation is that since terrorists like to attack places with homesteads because of their desire to kill and maim, more distanced farms may be safer than close to home. The results in Table 4 are consistent with those of Table 3 in terms of the signs of distance variables with respect to average plot size and distance of plots to households. That is, farm households that are distant from markets have larger plots but those closer to administration center have smaller plots. Next, we examine the ways in which increased conflict intensity affects the nature of land ownership. As shown in Table 5, we investigate the effects on four main dependent variables: 1) percentage of Table 3 Effects of conflict intensity on land owned and number of plots. Land owned (sq. meters)

Casualties Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

No. of plots owned

(1)

(2)

(3)

(4)

(5)

(6)

3.009 (0.50) 642.930*** (10.06) 9.837 (0.70) −5.032*** (-5.68) 94.305*** (8.40) 42.559*** (7.41) 36.582*** (9.51) 1.888 (1.02) 2996.459** (2.49) NO NO NO 8044 0.114

43.455** (2.05)

41.005* (1.94) 598.081*** (2.93) −6.451 (-0.20) −20.837 (-1.48) 38.253* (1.92) 138.414 (1.23) −129.891** (-2.18) 78.796** (2.31) 9318.218 (0.48) YES YES YES 8044 0.164

0.003*** (4.12) 0.049*** (12.71) 0.001 (1.18) −0.000* (-1.90) 0.005*** (7.29) 0.000 (0.72) 0.000 (1.51) 0.001*** (9.05) 1.078*** (16.36) NO NO NO 8044 0.062

−0.001 (-0.63)

−0.001 (-0.85) 0.020* (1.91) 0.003 (1.54) −0.001 (-1.18) 0.001 (1.10) −0.001 (-0.10) −0.001 (-0.41) −0.006*** (-3.14) 5.122*** (3.32) YES YES YES 8044 0.193

11,528.392*** (22.98) YES YES YES 8072 0.157

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01. 7

1.906*** (84.84) YES YES YES 8072 0.189

Land Use Policy 88 (2019) 104116

A. Adelaja and J. George

Table 4 Effects of conflict intensity on the average plot size and distance of plots from household. Average plot size

Casualties Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

Average distance of plots from households

(1)

(2)

(3)

(4)

(5)

(6)

−12.644*** (-4.94) 150.157*** (4.61) 6.480 (0.89) −2.510*** (-5.49) 43.725*** (5.99) 26.904*** (8.46) 24.995*** (10.65) −3.491*** (-3.53) 2996.459** (2.49) NO NO NO 8044 0.114

34.215*** (2.70)

33.906*** (2.70) 206.087** (2.18) −19.603 (-1.12) −10.909 (-1.51) 19.257* (1.67) 103.416 (1.62) −92.243*** (-2.73) 72.442*** (3.32) 9318.218 (0.48) YES YES YES 8044 0.164

0.020** (2.00) −0.046*** (-4.02) 0.006** (2.15) 0.001*** (2.67) −0.014*** (-4.45) 0.014*** (13.08) 0.000 (0.40) 0.000 (0.26) 1.078*** (16.36) NO NO NO 8044 0.062

0.025** (2.17)

0.026** (2.19) 0.010 (0.35) 0.008 (1.24) −0.010 (-0.98) 0.002 (0.17) −0.021 (-0.38) 0.043 (1.46) 0.040 (0.90) 5.122*** (3.32) YES YES YES 8044 0.193

11,528.392*** (22.98) YES YES YES 8072 0.157

1.906*** (84.84) YES YES YES 8072 0.189

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01.

tenure problems.

their land to relatives, friends or others. In addition, as explained in the conceptual framework, in conflict zones, the value of land may approach zero. In models 7 and 8, an increase in conflict intensity reduces the percentage of land acquired by family inheritance. From Table 5, the distance variables that capture proximity to economic activities (distance to nearest markets, administration centers and population centers) are directly related to percentage of free land, except the distance to administration center variable. Inherited family land is directly related to rainfall, which suggests that intergenerational transfer is more likely with land with good rain. Inheritance, however, is compromised in remote areas possibly due to land ownership or land

5.2. Effects of conflict intensity on land values and cropping patterns In Table 6, we report results for regressions where the rental value and the expected market value of land are regressed against the household’s exposure to conflict. Conflict intensity, measured by the number of casualties does not significantly impact land rental rates. This result can be attributed to the lack of market demand for land and the limited transactions expected in conflict zones. That is, excess supply of land due to massive population displacements and lack of

Table 5 Effects of conflict intensity on types of land acquisition.

Casualties

Percentage of land rented

Percentage of land purchased

Percentage of free land acquired

Percentage of family inherited land

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.024 (0.57)

0.021 (0.49) 0.123 (0.48) −0.050 (-1.21) −0.008 (-0.34) 0.047* (1.75) −0.401 (-1.58) 0.008 (0.09) −0.055 (-1.23) 63.149* (1.74) YES YES YES 8044 0.131

−0.007 (-0.21)

−0.006 (-0.18) −0.092 (-0.37) 0.070 (1.44) −0.023 (-1.30) −0.013 (-0.42) 0.025 (0.20) 0.054 (1.15) −0.074 (-1.46) 51.217** (2.19) YES YES YES 8044 0.090

0.096*** (3.10)

0.098*** (3.18) −0.411 (-1.44) 0.045 (0.83) −0.037 (-1.15) 0.117** (2.25) 0.659* (1.67) −0.215*** (-2.93) 0.591*** (5.45) −157.726*** (-2.65) YES YES YES 8044 0.161

−0.113** (-2.07)

−0.113** (-2.04) 0.380 (0.92) −0.065 (-0.88) 0.067* (1.80) −0.151*** (-2.65) −0.283 (-0.63) 0.152 (1.55) −0.463*** (-4.14) 143.360** (2.20) YES YES YES 8044 0.139

Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

9.136*** (16.91) YES YES YES 8072 0.127

7.747*** (13.12) YES YES YES 8072 0.087

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01. 8

10.309*** (14.05) YES YES YES 8072 0.146

72.808*** (77.43) YES YES YES 8072 0.132

Land Use Policy 88 (2019) 104116

A. Adelaja and J. George

Table 6 Effects of conflict intensity on Expected land value. Rent paid per square meter

Casualties Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

Value of land per square meter

(1)

(2)

(3)

(4)

(5)

(6)

−0.002 (−0.85) −0.061 (-1.19) −0.047 (−1.49) 0.003 (1.63) 0.004 (0.76) −0.005** (−2.12) −0.004*** (−2.94) −0.003 (−1.32) 1.229* (1.72) NO NO NO 8044 0.002

−0.007 (−1.16)

0.000 (0.10) −0.230 (-0.82) −0.331 (−1.11) 0.018 (1.26) −0.049 (−1.16) −0.144 (−1.01) −0.003 (−0.24) 0.016 (0.80) 1.668 (0.14) YES YES YES 8044 0.120

−9.149** (−2.48) 102.152 (0.60) −21.825 (−0.48) 9.778** (2.20) 22.310 (1.17) −37.406*** (−3.20) −22.442 (−1.59) −7.896* (−1.87) −2655.754 (−0.91) NO NO NO 7992 0.002

−33.336** (−2.20)

−24.469** (−2.13) 1039.689 (0.65) −84.341 (−1.02) 35.894 (1.16) −107.676* (−1.80) −131.778 (−0.60) −10.509 (−0.38) 99.169* (1.93) −57288.387 (−1.40) YES YES YES 7992 0.105

−0.137 (−0.15) YES YES YES 8072 0.115

8302.245*** (2.63) YES YES YES 8020 0.104

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01.

6. Conclusions and policy suggestions

infrastructure support for agricultural activities can result in collapse of the land rental market. The insignificance of coefficient of the casualties variable is consistent across all three specification (models 1–3). During the surveys, the persons who manage the plots were asked about their expectations regarding the current market valuation of their land. By using the expected valuation variable as a dependent variable, we essentially examine whether the individual’s anticipation of risk due to possibilities of past and future attacks influence his/her valuation of land. Our results suggest that the land value per square meter decreases with increased conflict intensity. Each household factors in the risk and uncertainty associated with an exposure to conflict in calculating the land value. Hence, in areas affected with conflict, households expect the land values to depreciate significantly possibly due to various factors such as the lack of access to inputs, absence of output markets and agricultural value chains and safety concerns. In addition, we expect the destruction of bridges and roads, communication facilities and other physical infrastructure by Boko Haram to depreciate the value for land in areas directly affected by insurgency and in surrounding places. From Table 6, the value of land falls as the distance to population center increases. This is consistent with Van Thunen’s theory and the findings from most empirical enquiries of land (see Lopez et. al. 1989). The positive sign of the coefficient of distance to nearest border crossing is hard to explain, but we suspect that it is relate to excessive exposure of locations near borders. In Table 7, we examine the effects of conflict on cropping practices. We find that conflict significantly decreases the percentage of acreage farmed using mono-cropping and inter-cropping while the percentage of acreage farmed using relay-cropping and mixed-cropping increases in response to conflict. Mono-cropping practices mean large dependency on a single crop. A shock to output or input markets or unanticipated price fluctuations is more likely to wipe out the entire profits of farmer who is engaged in mono cropping than it is for a more diversified farmer. In mixed farming, the risk is spread across several crops. This is consistent with findings in conflict-affected regions that households respond to shocks inflicted by conflicts by increasing the share of low-risk, low-return activities.

Agriculture is the mainstay and backbone of the economy in most rural areas in significantly underdeveloped economies where development indices show high levels of poverty, low incomes and low levels of food security. Such places are also the venues for many terrorist activities, especially in the case of territorial terrorist organizations whose destruction goals are combined with the need to capture territory. Agriculture can become caught up in the vicious struggle to control land and minds. On one hand, it can be targeted for destruction in an attempt to create greater incidence of IDPs for visibility. On the other hand, it can be targeted for territory capture to enable terrorist organizations to support their members with food. There are several scenarios in between these extremes, including the launching of a weak enough attack on an agricultural area to enable farming activities to continue so that terrorist organizations can reserve the opportunity to raid the area in the future for much needed agricultural products. Regardless, the desirability of rural areas for terrorist attacks cannot be ignored in planning for meaningful counter terrorism and de-radicalization and post-crisis recovery strategies. When terrorists do strike in or near agricultural production areas, they instill fear on farm households, with various noteworthy consequences. This study suggests that farmers indeed perceive greater risk associated with farming and therefore make land use adjustments to mitigate such risks. First, they manage greater acreage possibly due to the abandonment of farmland by neighbors, family and friends as they flee to safer havens. Second, the percentage of land left fallow or idle increases as a response to conflict-related shocks. Third, there are indicators that land market is compromised as land values fall. Fourth, they neither rent more or less, probably because many of the households they can rent from have fled to safer ground. Fifth, the expected values of land declined, probably because of the increased risk associated with conflicts. Finally, they lean more in the direction of crop diversification. Several policy implications of our findings are palpable, but we note a few. First, conflict-affected areas where the farming activities continue have probably become more specialized in crops that are more in tune with subsistence needs. These areas may require assistance to 9

Land Use Policy 88 (2019) 104116

A. Adelaja and J. George

Table 7 Effects of conflict intensity on cropping types.

Casualties Household size Age of household head Rainfall Distance to pop center Distance to market Distance to administration center Distance to nearest border crossing Constant Household Fixed Effects Survey Year fixed effects LGA-specific time trends N R2

Mono (1)

Inter (2)

Relay (3)

Mixed (4)

Alley (5)

Strip (6)

−0.144*** (−2.68) −0.654 (−1.51) −0.056 (−0.71) −0.046 (−1.10) −0.011 (−0.19) 0.028 (0.10) −0.036 (−0.49) −0.127 (−1.37) 136.205** (2.16) YES YES YES 7588 0.156

−0.076*** (−2.72) 0.014 (0.08) 0.036 (0.95) 0.003 (0.16) −0.021 (−1.01) 0.183 (1.23) −0.041* (−1.77) 0.040 (0.91) −21.684 (−0.88) YES YES YES 7588 0.163

0.013* (1.69) 0.130 (0.92) 0.022 (0.81) 0.003 (0.42) 0.005 (0.29) −0.096* (-1.87) 0.032** (2.16) −0.051*** (−3.02) 14.282 (1.21) YES YES YES 7588 0.120

0.195*** (3.18) 0.556 (1.21) −0.028 (−0.34) 0.034 (0.80) 0.040 (0.70) −0.123 (-0.37) 0.033 (0.47) 0.094 (0.95) −5.324 (-0.08) YES YES YES 7588 0.155

0.004 (1.56) −0.053 (−0.61) 0.015 (0.82) 0.003 (0.48) −0.009 (−0.55) −0.012 (-0.24) 0.015 (0.74) 0.042* (1.66) −16.903 (−1.38) YES YES YES 7588 0.120

0.006 (1.14) 0.004 (0.13) 0.012 (1.62) 0.003* (1.86) 0.002 (0.29) 0.020 (0.74) −0.003 (−0.66) −0.001 (−0.19) −4.753 (−1.12) YES YES YES 7588 0.047

Notes: t-statistics are in parenthesis. *p < .10; **p < 0.5; ***p < 0.01.

Fourth, help may be needed to strengthen the functionality of land markets so that the use value of land can go beyond subsistence farming determined levels. Finally, given the large number of IDPs in Nigeria in particular, it could be difficult to expand agricultural production unless IDPs return home or grant use permits to remaining or returning farmers. The possibility that many farmers may never return home may pose major challenges in revitalizing the farming economy.

diversify their products and to connect to markets more meaningfully, post-conflict. Second, help may be needed early to reclaim lands that farmers have abandoned due to the perception of greater danger. These may be the more distant farms from homesteads. Third, it may be difficult to motivate farmers to move out of their comfort zones unless they are well assured that the distant farm plots are safe again. Therefore, commitment of security related agencies to protect farmers and maintain peace may well be a central tool for reassuring farmers. Appendix A The first-order conditions (FOCs) are obtained as follows:

∂V t + 1 ⎞ ∂λt ∂u =0 : − βP E ⎛ ∂x t ∂x ⎝ ∂bt + 1 ⎠ ⎜

⎟

∂V t + 1 ⎞ ∂λt ∂V t + 1 ∂f ⎞ =0 : βE ⎛ ε − wt β E ⎛ ∂Lt ∂ Q ∂ L + t 1 t ⎝ ∂bt + 1 ⎠ ⎝ ⎠ ⎜

⎟

⎜

⎟

∂V t + 1 ⎞ ∂λt ∂V t + 1 ⎞ ∂f =0 : βE ⎛ ε − at β E ⎛ ∂At ⎝ ∂bt + 1 ⎠ ⎝ ∂Qt + 1 ⎠ ∂At ⎜

⎟

⎜

⎟

in Maiduguri. Available at: http://www.mcser.org/journal/index.php/mjss/article/ view/6455 [Accessed April 29, 2018]. . Baumann, M., Kuemmerle, T., 2016. The impacts of warfare and armed conflict on land systems. J. Land Use Sci. 11 (6), 672–688. Available at: https://www.tandfonline. com/doi/full/10.1080/1747423X.2016.1241317 [Accessed February 17, 2019]. Bertoni, E., Di Maio, M., Molini, V., Nisticò, R., 2018. Education is forbidden: the effect of the Boko Haram conflict on education in North-East Nigeria. J. Dev. Econ. https:// doi.org/10.1016/J.JDEVECO.2018.06.007. Bozzoli, C., Brück, T., 2009. Agriculture, poverty, and postwar reconstruction: micro-level evidence from Northern Mozambique” P. Verwimp, P. Justino, and T. Brück. J. Peace Res. 46 (3), 377–397. Available at: http://journals.sagepub.com/doi/10.1177/ 0022343309102658 [Accessed February 17, 2019]. Castan Pinos, J., Radil, S.M., 2018. The territorial contours of terrorism: a conceptual model of territory for non-state violence. Terror. Political Violence 1–20. Available at: https://www.tandfonline.com/doi/full/10.1080/09546553.2018.1442328 [Accessed April 26, 2018]. Chauveau, J., Richards, P., 2008. West african insurgencies in agrarian perspective: côte d’ivoire and Sierra Leone compared. J. Agrar. Chang. 8 (4), 515–552. Christiaensen, L., Demery, L., 2007. Down to Earth Agriculture and Poverty Reduction in Africa. Available at: http://siteresources.worldbank.org/INTPOVERTY/Resources/ 335642-1130251872237/DownToEarth_final.pdf [Accessed February 17, 2019]. .

References ACLED, 2017. Armed Conflict Location & Event Data Project (ACLED) Codebook. Version8. Available at: https://www.acleddata.com/wp-content/uploads/2017/12/ ACLED_Codebook_2017FINAL.pdf [Accessed April 29, 2018]. . Adelaja, A., George, J., 2019. Effects of conflict on agriculture: evidence from the Boko Haram insurgency. World Dev. 117, 184–195. Available at: https://www. sciencedirect.com/science/article/pii/S0305750X19300166 [Accessed February 17, 2019]. Agbiboa, D.E., 2013. The ongoing campaign of terror in Nigeria: boko Haram versus the state. Stab. Int. J. Secur. Dev. 2 (3), 1–18. Arias, M.A., Ibáñez, A.M., Zambrano, A., 2018. Agricultural production amid conflict: separating the effects of conflict into shocks and uncertainty. World Dev. Available at: https://www.sciencedirect.com/science/article/pii/S0305750X1730373X [Accessed December 3, 2018]. Associated Press, 2013. Islamic Militants Drive 19,000 Rice Farmers Off Land in Northeast Nigeria - CBS News. Available at: https://www.cbsnews.com/news/islamicmilitants-drive-19000-rice-farmers-off-land-in-northeast-nigeria/ [Accessed August 12, 2018]. . Awodola, B., Oboshi, A., 2015. Terrorism in Northern Nigeria: A Threat to Food Security

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agriculture. Am. J. Agric. Econ. 70 (2), 346. Available at: https://academic.oup.com/ ajae/article-lookup/doi/10.2307/1242075 [Accessed February 17, 2019]. Lowder, S.K., Skoet, J., Raney, T., 2016. The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Dev. 87, 16–29. Available at: https://www.sciencedirect.com/science/article/pii/S0305750X15002703 [Accessed February 17, 2019]. Mertens, B., Sunderlin, W.D., Ndoye, O., Lambin, E.F., 2000. Impact of macroeconomic change on deforestation in South Cameroon: integration of household survey and remotely-sensed data. World Dev. 28 (6), 983–999. Available at: https://www. sciencedirect.com/science/article/pii/S0305750X00000073 [Accessed February 17, 2019]. Ordway, E.M., 2015. Political shifts and changing forests: effects of armed conflict on forest conservation in Rwanda. Glob. Ecol. Conserv. 3, 448–460. Available at: https://www.sciencedirect.com/science/article/pii/S2351989415000141 [Accessed February 17, 2019]. Pivovarova, M., Swee, E.L., 2015. Quantifying the microeconomic effects of war using panel data: evidence from Nepal. World Dev. 66, 308–321. Available at: http:// linkinghub.elsevier.com/retrieve/pii/S0305750X14002642 [Accessed April 26, 2018]. Pielke, R.A., 2005. Misdefining “climate change”: consequences for science and action. Environ. Sci. Policy 8 (6), 548–561. Available at: https://www.sciencedirect.com/ science/article/pii/S1462901105001048?via%3Dihubhttps://www.sciencedirect. com/science/article/pii/S1462901105001048?via%3Dihub. Pollakowski, H.O., Wachter, S.M., 1990. The effects of land-use constraints on housing prices. Land Econ. 66 (3), 315. Available at: http://www.jstor.org/stable/3146732? origin=crossref [Accessed February 17, 2019]. Pretty, J., 1999. Can sustainable agriculture feed africa? new evidence on progress, processes and impacts. Environ. Dev. Sustain. 1 (3-4), 253–274. Available at: https:// link.springer.com/article/10.1023/A:1010039224868. Raleigh, C., Linke, A., Hegre, H., Karlsen, J., 2010. Introducing ACLED: an armed conflict location and event dataset. J. Peace Res. 47 (5), 651–660. Available at: http:// journals.sagepub.com/doi/10.1177/0022343310378914 [Accessed April 29, 2018]. Reginster, I., Rounsevell, M., 2006. Scenarios of future urban land use in Europe. Environ. Plann. B Plann. Des. 33 (4), 619–636. Available at: http://journals.sagepub.com/doi/ 10.1068/b31079 [Accessed February 17, 2019]. Sandler, T., Enders, W., 2004. An economic perspective on transnational terrorism. Eur. J. Polit. Econ. 20 (2), 301–316. Available at: https://www.sciencedirect.com/science/ article/pii/S0176268003001046 [Accessed April 26, 2018]. Sekizawa, R., Ichii, K., Kondo, M., 2015. Satellite-based detection of evacuation-induced land cover changes following the Fukushima Daiichi nuclear disaster. Remote. Sens. Lett. 6 (11), 824–833. Available at: http://www.tandfonline.com/doi/full/10.1080/ 2150704X.2015.1076207 [Accessed February 17, 2019]. Sloan, S., Kearney, R., Wise, C., 1978. Learning about terrorism: analysis, simulations, and future directions. Terrorism 1 (3–4), 315–329. Available at: http://www. tandfonline.com/doi/abs/10.1080/10576107808435417 [Accessed April 26, 2018]. Sunderlin, W., Ndoye, O., Biki, H., Laporte, N., Mertens, B., Pokam, J., 2000. Economic crisis, small-scale agriculture, and forest cover change in southern Cameroon. Environ. Conserv. 27 (3), 284–290. Available at: https://www.cambridge.org/core/ terms [Accessed February 17, 2019]. Swee, E.L., 2015. On war intensity and schooling attainment: The case of Bosnia and Herzegovina. Eur. J. Polit. Econ. 40, 158–172. Available at: https://www. sciencedirect.com/science/article/pii/S0176268015000786 [Accessed April 26, 2018]. Todorovski, D., Zevenbergen, J., van der Molen, P., 2016. Conflict and post-conflict land administration – the case of Kosovo. Surv. Rev. 48, 316–328. UN-HABITAT. 2007. Enhancing Urban Safety and Security. Global Report on Human Settlements. London Sterling, VA. Vesterby, M., Heimlich, R.E., 1991. Land use and demographic change: results from fastgrowth counties. Land Econ. 67 (3), 279. Available at: http://www.jstor.org/stable/ 3146423?origin=crossref [Accessed February 17, 2019].

Cooke, J.G., Sanderson, T.M., Johnson, J.C., Hubner, B., 2016. Militancy and the Arc of Instability: Violent Extremism in the Sahel. Available at: https://csis-prod.s3. amazonaws.com/s3fs-public/publication/160922_Sanderson_ MilitancyArcInstabilitySahel_Web.pdf [Accessed April 26, 2018]. . Council on Foreign Relations, 2018. Nigeria Security Tracker. Available at: https:// www.cfr.org/nigeria/nigeria-security-tracker/p29483 [Accessed April 29, 2018]. . Deboy, G.R., Jones, P.J., Field, W.E., Metcalf, J.M., Tormoehlen, R.L., 2008. Estimating the prevalence of disability within the U.S. Farm and ranch population. J. Agromedicine 13 (3), 175–190. Available at: https://www.tandfonline.com/doi/full/ 10.1080/10599240802371763 [Accessed February 17, 2019]. de la Calle, L., Sánchez-Cuenca, I., 2015. How armed groups fight: territorial control and violent tactics. Stud. Confl. Terror. 38 (10), 795–813. Available at: http://www. tandfonline.com/doi/full/10.1080/1057610X.2015.1059103 [Accessed April 26, 2018]. Eklund, L., Degerald, M., Brandt, M., Prishchepov, A.V., Pilesjö, P., 2017. How conflict affects land use: agricultural activity in areas seized by the Islamic State. Environ. Res. Lett. 12 (5), 054004. Available at: http://stacks.iop.org/1748-9326/12/i=5/ a=054004?key=crossref.2959ffc7a460213515e077d9efc0bf18 [Accessed February 17, 2019]. Elden, S., 2009. Terror and Territory: the Spatial Extent of Sovereignty. University of Minnesota Press. Enders, W., Sandler, T., 2012. The Political Economy of Terrorism. Cambridge University Press. Available at: https://econpapers.repec.org/bookchap/cupcbooks/ 9781107004566.htm [Accessed April 26, 2018]. Field, W.E., Jones, P., 2006. Disability in agriculture. Agricultural Medicine. SpringerVerlag, New York, pp. 70–80. Available at: http://link.springer.com/10.1007/0-38730105-4_7 [Accessed February 17, 2019]. Gorsevski, V., Kasischke, E., Dempewolf, J., Loboda, T., Grossmann, F., 2011. Analysis of the Impacts of armed conflict on the Eastern Afromontane forest region on the South Sudan — uganda border using multitemporal Landsat imagery. Remote Sens. Environ. 118. Available at: http://www.c-r.org/accord [Accessed February 17, 2019]. Haruna, A., 2018. FAO Distributes Seeds, Fertiliser to Farmers Displaced by Boko Haram. Premium Times. Available at: https://www.premiumtimesng.com/regional/nnortheast/273955-fao-distributes-seeds-fertiliser-to-farmers-displaced-by-boko-haram. html [Accessed August 12, 2018]. Headey, D.D., 2016. The evolution of global farming land: facts and interpretations. Agric. Econ. 47 (S1), 185–196. Available at: http://doi.wiley.com/10.1111/agec. 12313 [Accessed February 17, 2019]. Hecht, S.B., Kandel, S., Gomes, I., Cuellar, N., Rosa, H., 2006. Globalization, forest resurgence, and environmental politics in El Salvador. World Dev. 34 (2), 308–323. Available at: https://www.sciencedirect.com/science/article/pii/ S0305750X05002068 [Accessed February 17, 2019]. Hostert, P., Kuemmerle, T., Prishchepov, A., Sieber, A., Lambin, E.F., Radeloff, V.C., 2011. Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl. Environ. Res. Lett. 6 (4), 045201. Available at: http:// stacks.iop.org/1748-9326/6/i=4/a=045201?key=crossref. 6a66326dcac92f8a460ec05756c42d43 [Accessed February 17, 2019]. IRIN, 2015. IRIN | How Boko Haram Is Killing Off Farms. Available at: http://www. irinnews.org/analysis/2015/12/17 [Accessed August 12, 2018]. . Isuwa, S., Searcey, D., 2016. Boko Haram Burns Villages in Northeastern Nigeria - the New York Times. New York Times. Available at: https://www.nytimes.com/2016/ 02/01/world/africa/boko-haram-kills-dozens-in-northern-nigeria-military-says.html [Accessed February 17, 2019]. Kah, H.K., 2017. Boko Haram is losing, but so is food production’: conflict and food insecurity in Nigeria and Cameroon. Afr. Dev. 42 (3), 177–196. Available at: https:// www.jstor.org/stable/90018140 [Accessed April 26, 2018]. Kidd, M.P., Sloane, P.J., Ferko, I., 2000. Disability and the labour market: an analysis of British males. J. Health Econ. 19 (6), 961–981. Available at: https://www. sciencedirect.com/science/article/pii/S0167629600000436 [Accessed February 17, 2019]. Knoke, D., 2015. Emerging trends in social network analysis of terrorism and counterterrorism. Emerging Trends in the Social and Behavioral Sciences. John Wiley & Sons, Inc., Hoboken, NJ, USA, pp. 1–15. Available at: http://doi.wiley.com/10.1002/ 9781118900772.etrds0106 [Accessed April 26, 2018]. Lambin, E.F., Geist, H., 2006. Land-use and Land-cover Change: Local Processes and Global Impacts. Springer. Lopez, R.A., Adelaja, A.O., Andrews, M.S., 1988. The effects of suburbanization on

Adesoji Adelaja is the John A. Hannah Distinguished Professor in Land Policy, Department of Agricultural, Food and Resource Economics (AFRE), Michigan State University (MSU). Justin George is Post-doctoral Research Associate at AFRE. Funds from the John A. Hannah Distinguished Professor Endowment at Michigan State University provided partial support for this research. The authors take full responsibility for any errors or omissions that remain in this paper.

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