G42 1 PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016 Land Cover Change and Rural Livelihoods: A Spatial Analysis on Northern Ghana Beliyou Haile, Sara Signorelli, Carlo Azzarri, Zhe Guo International Food Policy Research Institute 2033 K Street, N.W. Washington, D.C. 20006, USA Corresponding author: Beliyou Haile ([email protected]) DOI: 10.1481/icasVII.2016.g42d ABSTRACT Land use and land cover change accounts for about 80% of the global cost of land degradation, and one fifth of the land degradation that took place globally between 1982 and 2006 occurred in Africa south of the Sahara. We map the land cover of the three Savannah Regions of Northern Ghana twenty years apart (1994 – 2014) using remote sensing data, and subsequently employ spatial regression analysis to examine the relationship between long-term land cover change and current total value of harvest and maize productivity. Through the construction of a unique dataset combining information from georeferenced household surveys, remote sensing, and other secondary data sources, we propose a method to observe the relationship between landscape-level transformation and household-level outcomes. We find that areas that are currently cultivated or urbanized but were green vegetation in 1994 show higher agricultural production and productivity than areas currently cultivated or urbanized that were previously bare land. This suggests that the expansion of crop land into degraded areas with poor quality soils may not yield the expected positive gains, thus threatening the sustainability of agricultural production upon on which millions of poor smallholders rely. Keywords: Land cover change, agricultural productivity, spatial regression, spectral analysis.
Transcript
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1PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
Land Cover Change and Rural Livelihoods: A Spatial Analysis on Northern Ghana
Beliyou Haile, Sara Signorelli, Carlo Azzarri, Zhe Guo International Food Policy Research Institute 2033 K Street, N.W. Washington, D.C. 20006, USA Corresponding author: Beliyou Haile ([email protected]) DOI: 10.1481/icasVII.2016.g42d
ABSTRACT
Land use and land cover change accounts for about 80% of the global cost of land degradation, and one fifth of the land degradation that took place globally between 1982 and 2006 occurred in Africa south of the Sahara. We map the land cover of the three Savannah Regions of Northern Ghana twenty years apart (1994 – 2014) using remote sensing data, and subsequently employ spatial regression analysis to examine the relationship between long-term land cover change and current total value of harvest and maize productivity. Through the construction of a unique dataset combining information from georeferenced household surveys, remote sensing, and other secondary data sources, we propose a method to observe the relationship between landscape-level transformation and household-level outcomes. We find that areas that are currently cultivated or urbanized but were green vegetation in 1994 show higher agricultural production and productivity than areas currently cultivated or urbanized that were previously bare land. This suggests that the expansion of crop land into degraded areas with poor quality soils may not yield the expected positive gains, thus threatening the sustainability of agricultural production upon on which millions of poor smallholders rely.
Keywords: Land cover change, agricultural productivity, spatial regression, spectral analysis.
2PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
1. Introduction
The world population is expected to reach nine billion people by 2050 (United Nations 2009), requiring a steady growth in the production of food, feed and bioenergy sources for its subsistence (FAO 2011). This evolution is expected to put significant pressure on the natural vegetation and cropland on which millions of poor people rely heavily for their livelihood, therefore increasing the vulnerability of households living in rural areas (Biggelaar et al. 2004; Berry et al. 2003). Poverty, in turn, prevents farmers from investing in the measures necessary to protect the natural resource base, creating a poverty-environment vicious circle (Bremner et al. 2010; Bhattacharya et al. 2006; MEA, 2005; Perrings 1989; IPCC 2000; Wood et al. 2004). This negative spiral of poor livelihoods and resources degradation is further magnified by factors such as imperfect market and institutional failures (Duraiappah 1996).
About 40% of the global land surface is devoted to cropland and pastures, and in sub-Saharan Africa the rate of agricultural expansion has been steadily increasing over the period 1961-2005 (Foley et al. 2005; Nkonya et al. 2012). At the same time, about 65% of Africa’s arable land is too degraded for sustainable food production, posing serious challenges for supporting the growing population that depends on it (Montpellier Panel 2014). This threat could be especially severe in arid and semi-arid environments, such as the semi-arid savannah region of West Africa (Pinstrup-Andersen and Watson 2010).
Existing literature on household welfare shows a strong spatial dimension in livelihood strategies, whose understanding is crucial to properly design effective poverty-alleviating policy interventions (Ayadi et al. 2009; Benson et al. 2005; Minot et al. 2003; Okwi et al. 2007). Yan et al. (2009) show how the transition of cultivated land into infertile soil, taking place in China following the dramatic urban expansion, significantly reduced overall agricultural productivity. Wiebe (2003) further discusses how the negative effects of land degradation on crop yields tend to be particularly important in areas with already high poverty rates, thus underlining the strong interdependence between household welfare, agricultural activity, and land cover dynamics.
Spatial analysis has been previously proposed to study these linkages (Bremner et al. 2010; Berry et al. 2003; Dasgupta et al. 2003; Dang 2014; Gyawali et al. 2004; Nkonya et al. 2008). Nevertheless, while the understanding of the spatial patterns of land cover changes and welfare has significantly improved during the last decade (Ayadi and Amara 2008; Okwi et al. 2007; Paraguas and Kamil 2005), empirical evidence on this relationship remains limited and seldom complemented with socioeconomic analysis (Barrett and Carter 2013; Galford et al. 2013; Mali 1998; Turner 2002).
The focus area of this paper is northern Ghana, comprising o Upper East, Upper West, and Northern regions. This area covers 40% of the nation’s surface, while 80% of its population relies primarily on agriculture. These three regions also show the lowest levels of welfare in the country and, in contrast with the rest of the country, poverty rates have been increasing over the past years (Diao et al. 2007; World Bank 2011). Finally, the area is also highly affected by land degradation as a result of unsustainable farming practices due to the dominant bush-fallow rotation system, the removal of natural vegetation cover, and urbanization (Braimoh and Vlek 2005; Diao and Sarpong 2007; FAO 2000; World Bank 2007). These characteristics render the three regions under study particularly vulnerable to the poverty-environment vicious cycle, therefore understanding the underlying dynamics at play would be crucial to suggest interventions to break the cycle. The paper starts by describing the changes in land cover that occurred between 1994 and 2014. Next, it shows a spatial regression analysis that examines the link between long-term land cover change and current rural livelihoods, after
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controlling for regional trends in poverty and population over the period. Results show that households living in natural cover areas in 1994 that turned into productive areas in 2014 are associated to higher harvest value and maize yield than households currently living in productive areas that were previously bare land. This finding suggests that expanding crop cultivation to relatively low fertile soils may not lead to sustainable agricultural production, and that once land becomes degraded it is very difficult to reverse the process. While this study shows that crop cultivation in areas previously covered by vegetation bear positive effects on production, the loss of natural resources is likely to be detrimental in the longer term that goes beyond what is captured by this analysis. Results bear important policy implications calling for enhanced investments in land conservation practices in the regions - including reforestation -, and for sustainable intensification in areas already under cultivation.
The novelty of the study lies in the ability to combine household-level analysis with geo-spatial information at the landscape level to examine the interdependence between land cover change and rural livelihoods. The methodology used to derive the land classification is also new. First, it takes advantage of the georeferenced boundaries of 278 plots collected at the household level to precisely derive the 2014 cropland distribution for the entire northern Ghana. Second, it uses spectral prediction out of sample to recover the complete land cover distribution two decades ago (1994).
The rest of the paper is organized as follows. Section 2 outlines the conceptual framework; Section 3 describes and summarizes the data. Section 4 outlines our identification strategy and Section 5 discusses regression results. Finally, Section 6 concludes.
2. Conceptual Framework
There is a vast literature describing the spatial concentration of poverty in clusters of indigence (Ayadi and Amara 2009; Benson et al. 2005; Demombynes et al. 2002; Lanjouw et al. 2013; Minot and Baulch 2005), which – especially in Sub-Saharan Africa - are often located in rural areas where subsistence farming constitutes the main livelihood strategy (Amarasinghe et al. 2005). While transient poverty is mainly caused by temporary shocks, chronic poverty depends on the scarcity of productive assets (Barrett 2005), which in the case of subsistence farmers are mostly constituted by the natural capital embedded in the land they cultivate (Dasgupta and Deichmann 2003; Okwi et al. 2007). Therefore, the characteristics of the land-cover define the potential for agricultural production and productivity of the communities that inhabit it, and their evolution influences the dynamics of household welfare (Ikefuji and Horii 2005). In order to study these linkages, our framework considers three main land-cover classes on the base of their contribution to livelihoods. The first class is defined as the natural cover and is composed of forest, water sheds, shrubs, and savannah. It represents the area that has not been transformed by human action. The natural cover is not only a source of fodder, fuel, food, and timber for the adjacent communities, but has also an important role in the farming systems’ regenerative processes as well as constituting a stock of genetic resources for future agricultural needs (Alavalapati 2003; Sunderlin et al. 2008). Natural resources therein are non-exclusive and all the members of the community can benefit from them (Shackleton et al. 2007). The second class is defined as the productive cover and is composed of cropland and urban areas. This land type is shaped by human action and constitutes the location where most of the economic activity takes place. Cropland is devoted to agricultural production and livestock breeding while urban areas host the industry and services activities.
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Finally, the third class is defined as the degraded cover and is composed of bare soil. The process of land degradation describes the dissipation of soil nutrient content that can either be the product of human activities such as overexploitation, or the result of natural phenomena such as climate change. Degraded land is neither productive nor a source of natural capital and thus do not supply any resource for livelihoods (De Sherbinin 2002; Reynolds 2001).
The three land cover classes create nine possible land cover trajectories between 1994 and 2014. Table 1 summarizes them and reports their expected effect on household livelihoods.
Table 1: Land cover changes between 1994 and 2014 and expected impact Land cover in 1994
Land cover in 2014
Livelihood trend
Natural Natural Same Natural Productive (+/-) Natural Degraded (-) Productive Productive Same Productive Natural (-/+) Productive Degraded (-) Degraded Degraded Same Degraded Productive (+) Degraded Natural (+)
We expect the likelihood of the changes to be heterogeneous, with certain trajectories observed more frequently than others (Lambin et al. 2003; Mustard et al. 2004; Vitousek et al. 1997). In particular, we expect two trajectories to be dominant in northern Ghana. The first is the change from natural cover to productive cover and subsequently to degraded land (mostly caused by human action) (Braimoh and Vlek 2005; Braimoh 2009; Wood et al. 2004). The second is the change from natural cover to degraded land directly (mostly caused by natural factors). On the other hand, we expect the emergence of natural cover from the other two classes to be quite rare, especially in the absence of appropriate conservation practices in place. In terms of the relation between land cover changes and livelihoods, we expect the shift from natural cover to productive cover to improve household production and productivity up to a certain point, beyond which the increased scarcity of natural resources will start to impact livelihoods negatively (Coomes et al. 2011). For this trajectory to be sustainable, there needs to be a positive tradeoff between the loss of natural capital (biodiversity stock, carbon stock, etc.) and the economic effects of enlarged productive areas (Chhabra et al. 2006). Indeed, the objective of sustainable intensification is to create a balance between productive land and natural stock in order to allow the system to regenerate itself and to avoid overexploitation leading to land degradation (Cao et al. 2012; United Nations Department of Economic and Social Affairs 2012). The change from natural vegetation or productive cover to degraded land is expected to cause a deterioration of livelihoods (Berry et al. 2003; Bhattacharya and Innes 2006; Diao and Sarpong 2011), while the cultivation of land that was previously degraded is expected to improve livelihoods to a certain extent but still result in low productivity, because of the infertile nature of the soils. (Yan et al. 2009). It is also worth noting that these relations could be non-linear and be influenced by a number of economic, environmental, and institutional factors (Wiebe 2003). Finally, since the analysis is based on just two data points, we are not able to account for possible trend reversals that may have happened in-between.
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3. Data
3.1 Land Classification The analysis is based on a unique dataset combining primary data from a georeferenced
household survey, micro-data from secondary sources (Ghana Living Standards Surveys -GLSS-), satellite imageries, and census information.
First, we produced two land cover classification (for 1994 and 2014) using Landsat images from USGS (Landsat 5 and Landsat 8) that cover the entire northern Ghana1. The year 1994 is selected as the baseline because prior to the 1990s the quality of satellite imageries was significantly lower, making it difficult to identify different land cover types at an adequate resolution. Furthermore, while 1991/92 and 1995 were characterized by some abnormally intense floods (Codjoe and Owusu 2011), 1994 can be considered as a normal year in terms of climatic conditions such as rainfall and temperature. This year is also chosen to match the household survey data year.
The classification is obtained by assigning one of the seven land cover classed defined by FAO to each 30 meter by 30 meter pixel within the images (Campbell 1987). The seven classes are bare soil, crop land, forest, savannah, shrubs, urban settlements, and water bodies. However, the land cover types can look very different from the satellite imageries depending on the time of the year when the picture was taken. For example, cropland changes drastically between the growing and the harvest season while waterbody varies significantly in size between the rainy and dry season. For this reason, each classification is based on four images that capture seasonal variation2 and, within each image, on several spectral bands that are sensitive to the detection of the different spectral properties of all the seven land cover types.
To produce the land cover maps, the algorithm used calibrates the spectral properties of each land type in comparison to the others. For this reason, the images have to be overlaid with a sufficient number of external ground-truthing points identifying the exact locations where each type can be found. This allows to define and calibrate seven spectral profiles and to apply them to the entire surface. Figure 1 summarizes the identification process.
Figure 1: Diagram for the identification of land cover types
The ground-truthing points for cropland are identified with the georeferenced boundaries of 278 plots collected in the household survey, while the ones of the remaining six land cover types are identified
1 The code of the selected tiles is the following: Path194, Row53; and Path195, Row 53 2 Since cloud-free good quality images are unavailable for the four seasons in both years, one year time lag (before and after the selected year) has been also considered.
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through Google Earth. In total, about 200 ground-truthing points for each land cover type are collected across the three administrative regions, of which two thirds are used to train the system while the remaining one third is used for validation of the final products.
The maximum likelihood classification algorithm is applied to produce the 2014 classification (Johnson and Dean 1988). The latter considers both variance and covariance of the classes across the ground-truthing points and apply them to the remaining pixels. Under normality assumption, a class can be characterized by mean and covariance. Given these two characteristics, the probability of each cell to belong to any class is computed and each cell is assigned to the class showing the highest probability. The final classification is then validated and cross checked with Google Earth as well as shared with local partners for feedback.
This methodology, however, cannot be directly applied to the 1994 images because historical ground-truth data are not available (Mosteller 1977; Richards 1986). For this reason, the 1994 images are analyzed through unsupervised classification methods using the IsoData classification algorithm. The latter clusters pixel observations into groups based on their reflectance values of the multiple spectral bands (Tou and Gonzalez 1974). Since different land covers exhibits their own unique spectral properties, the IsoData unsupervised classification algorithm investigates each pixel’s spectral properties and groups them based on similarities. In a second step, the Maximum Likelihood Classification matches the spectral profiles identified in the 2014 with the spectral properties of 1994, to identify the same classes. Appendix Figure A1 and Figure A2 summarize the results of this land classification exercise, suggesting a reduction in shrubs and grassland and an increase in crop and bare land across the three regions.
In order to evaluate the quality of the 2014 land cover map, an accuracy assessment of the prediction is needed. We thus take the 1/3 ground-truthing points that were not used to train the algorithm to perform a statistical accuracy assessment for the entire classification as well as for the assignment of the individual classes. The accuracy matrix is provided in Table 2. The land cover classification results shows satisfactory accuracy rates3. The overall accuracy is over 70%. The urban, forest and waterbodies have the highest accuracy among all the land cover types (above 90%), while the rest of the classes have accuracy around 60%. Although cropland is usually recognized as one of the most difficult classes to identify, in our case it has higher accuracy rates than other vegetation types. This could be explained by the higher precision of the ground-truthing points from plot GPS coordinates than the ones based on the visual interpretation of Google Earth photos. Given the relatively high degree of land cover seasonality in northern Ghana and the possibility of land cover misclassification, we considered two years and grouped land cover types into homogeneous clusters.
Table 2: Accuracy Matrix of the 2014 Land Classification
3 The producer accuracy indicates the probability of the reference pixel being correctly classified and is a measure of omission error. It is used to indicate how well a certain area can be classified. The user accuracy is a measure of commission error. It indicates the probability that a pixel classified on the map actually represents that class on the ground.
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3.2 Household DataWe take advantage of the detailed geo-referenced, socioeconomic household and community
Ghana Africa RISING Baseline Evaluation Survey (GARBES)4 data collected in northern Ghana in 2014 to construct two measures of household livelihoods – maize yield and value of harvest - as well as several socioeconomic controls, including land holding, soil quality on-farm, and two indices of non-agricultural wealth and access to basic services5. Furthermore, spatial data matched to the household GPS coordinates were also included among the controls: market access (measured by travel time to the nearest town of 50 thousand people) and elevation, proxies for agricultural potential.
Finally, we construct region-level welfare indicators based on two waves of GLSS (1998/99 and 2012/13) to control for regional-level poverty trends as well as district-level population changes based on two waves of census data6 (2000 and 2008) to capture broad economic trends.
3.3 Matched Spatial and Household Data Regarding household-land cover mapping, a land cover type can be assigned to the geo-
referenced sample households in a number of ways. One way is to assign a class based on the class of the 30 meter by 30 meter pixel in which the household is located. Alternatively, different buffer zones can be defined around the pixel in which the household is located. When using a buffer zone, as we did in this paper, a decision needs to be made about the size of the buffer zone as well as how to assign a land class to households when there are multiple classes within the buffer zone. A wider buffer zone has the advantage of better representing the environment affecting the livelihoods of the household, especially given that the average walking distance to the closest plot reported in the surveys was of 30 minutes.
On the other hand, the wider the buffer zone chosen, the larger the number of overlapping pixels among households (and villages), and the smaller the variation in land cover changes in our sample. For comparability, we considered four buffer zones based on 9 pixels (p) (3 pixels wide and 3 pixels high around the pixel in which the household is located), 25p (5px5p), 49p (7x7), and 81p (9x9). A summary of the share of each land cover class is shown in Appendix Figure A3. Cropland, savannah/shrubs, and bare land were the three most dominant land cover types in 1994, while, cropland, savannah/shrubs and urban are the three dominant land cover types in 2014.7 These patterns are consistent regardless the buffer zone considered. Therefore, the empirical analysis is restricted to the 25 pixels buffer zone. We then regroup the initial 7 land cover types into the three defined classes (natural, productive and degraded land) resulting in nine land cover changes (summarized in Table 1). A summary of the re-classified land cover types and changes is provided in Figure 2.
4 Detailed about the program can be found here. Africa Research In Sustainable Intensification for the Next Generation (Africa RISING) Baseline Evaluation Survey in Ghana (GARBES) gathered detailed socioeconomic data from 1285 households residing in 50 communities in the Upper East, Upper West, and Northern regions. 5 The indices are constructed using factor analysis (principal-component factor method) following Filmer and Pritchett (2001). The non-agricultural wealth index is constructed based ownership of various non-agricultural household durable assets and dwelling condition while access to basic services index is constructed based on self-reported travel time to get to selected infrastructure (such as asphalt and all-weather roads) and various services (such as weekly and daily market places and bus stops). 6 Data are available from IPUMS International (Minnesota Population Center 2015) https://international.ipums.org/international/ 7 Shrubs and savanna are difficult to distinguish from google earth resulting in possible measurement errors.
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Figure 2: Re-classified land cover types (level and change) (25 pixels)
Productive cover accounts for more than 50% of land area in 1994, and reaches 70% in 2014. On the contrary, both natural cover and degraded land appears to have declined during the reference period, more so in the former case (Figure 2, panel A). Panel B summarizes the share of households in each of the nine possible land cover trajectories. About half of the households surveyed in 2014 reside in an area that that was crop land or urban in both 1994 and 2014 (Productive-Productive). Dominant are also land cover changes from natural cover to productive land (Natural-Productive) and from bare land to productive land (Degraded-Productive) (Figure 2, panel B).These patterns are indicative of the rapid expansion of extensive agriculture that is becoming more and more dominant in the landscape, to the point that farmers are starting to cultivate on less fertile soils that were previously bare land.
Given the relatively small sample size for some of the trajectories, and in the interest of improving precision of the estimates, we made further re-grouping of the land cover trajectories for the subsequent analysis. First, Natural-Natural and Natural-Degraded are grouped into one class (Natural-Other) that represents about 14% of the sample. Second, Productive-Natural and Productive-Degraded are grouped into one class (Productive-Other) representing about 7% of the study sample. Third, Degraded-Degraded and Degraded-Natural are grouped into one class (Degraded-Other) associated with 2% of the study sample. Figure 3 summarize our two measures of livelihood in 2014 - value of harvest and maize yield - by land cover trajectories for the 25 pixels buffer zone.
Setting aside the Degraded-Other trajectory (given the small sample size of 2%), households who live in areas with Productive-Productive and Natural-Other trajectories have the highest harvest and maize yield while those who live in areas that used to be bare land but turned into cropland or urban land scored the lowest harvest value and maize yield. A descriptive summary of the other socioeconomic variables used in the empirical analysis is shown in Appendix Table A1.
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of land cover
2014
1994
Panel A
Productive Natural Degraded
0 .1 .2 .3 .4 .5Percent of households
Natural-Natural
Natural-Productive
Natural-Degraded
Productive-Productive
Productive-Natural
Productive-Degraded
Degraded-Degraded
Degraded-Natural
Degraded-Productive
Panel B
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Figure 3: Land cover change and welfare (25 pixels)
4. Identification and Spatial Correlation
Agriculture is the main source of livelihood for more than 80% of the population in northern Ghana. As such, household welfare is highly correlated with agricultural production, which in turn depends on the characteristics of the landscape. Since our outcome variables (harvest value and maize yield) are likely to have a spatial dimension, modeling them without accounting for it would produce biased and inconsistent estimates (Paraguas and Kamil 2005). In addition, the spherical disturbances assumption of the ordinary least squares (OLS) would be violated if the model disturbances are spatially correlated. Finally, our pixel-based indicators of land cover change are expected to be spatially correlated as well, since by construction the buffer zones of neighboring households partially overlap.
To empirically test the existence of spatial correlation, we perform the Moran’s I test (Moran 1950) of positive correlation between each of the suspect variables (the two outcome measures and land cover change variables) and its spatial lag8. The value of Moran’s statistic ranges between –1 and + 1, with
8 For a given (𝑛 x1) vector X, its spatial lag is computed by averaging the values of the variable for “neighboring” units and
Moran’s I (I) statistic is given by 𝑛
∑𝑖 ∑𝑗 𝑤𝑖𝑗]
�̅�∗
∑𝑖 ∑𝑗 𝑤𝑖𝑗(X𝑖− )(X𝑗−�̅�)
�̅�∑𝑖(X𝑖− )^2where 𝑛 is the number of observations, 𝑤𝑖𝑗 parameterizes
the distance between 𝑖 and 𝑗, ∀ 𝑖, 𝑗 and 𝑤𝑖𝑗 = 0 if 𝑖 = 𝑗 and 𝑋 ̅ is the mean of X. In this paper, 𝑤𝑖𝑗 is given by 1/𝑑𝑖𝑗 , where 𝑑is the haversine distance between households 𝑖 and 𝑗, defined based on the latitude and longitude coordinates of 𝑖 and 𝑗.
4.15
4.37
2.71
3.48
2.26
5.10
01
23
45
Panel A
0.890.92
0.780.74
0.63
0.69
0.2
.4.6
.81
Ma
ize
yie
ld (
ton
/ha
)
Panel B
Natural-Other
Productive-Productive
Degraded-Productive
Natural-Productive
Productive-Other
Degraded-Other
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values near -1 (1) suggesting high negative (positive) spatial autocorrelation and values near zero implying minimal spatial autocorrelation. Under the null of no spatial autocorrelation, Morain’s I has an asymptotically normal distribution with expectation (−1/𝑛 − 1).9 A summary of Moran’s tests (Jeanty 2010) in Appendix Figure A4 (for the two outcome indicators of rural livelihood) and Figure A5.a - Figure A5.c (for the land cover trajectories) shows the presence of positive spatial autocorrelation. Autocorrelation coefficients for the outcome variables range from 0.05 to 0.07, while those of the land cover change measures range from 0.08 to 0.19.
To explore the link between land cover changes and livelihoods while accounting for these identification challenges, we follow a nested approach and specify a spatial first-order autoregressive model (Drukker et al. 2013; Drukker and Prucha 2013; Kelejian and Prucha 1998; LeSage 1999), which also adds spatially-lagged values of land cover change as follows.
𝐲 =ρ𝐖𝐲 + ∑ β𝑘𝑳𝑪𝑪𝑘
𝟓
𝒌=𝟏
+ ∑ ϕ𝑘𝐖𝑳𝑪𝑪𝑘
𝟓
𝒌=𝟏
+ 𝚲′𝐙 + 𝛆 (1)
𝛆 =λ𝐌𝛆 + 𝐮 (2)
𝒏
Equation 1 is a cross-sectional model of livelihoods (measured by harvest value and maize yield) with 𝐲 representing an 𝑛𝑥1 vector and 𝑛 representing the number of study households in GARBES; 𝑳𝑪𝑪𝒌 isa column vector for one of the five land cover trajectories summarized in Section 2, with the sixth one - Degraded-Productive - used as a reference category; 𝐙 is an 𝑛𝑥𝑝 matrix of conditioning variables where we control for progressively increasing number of household- and landscape-level covariates such as household demography, regional-level poverty trends and district-level population trends; 𝛆 is an 𝑛𝑥1 vector of error terms allowed to be spatially correlated (as shown in Equation 2); 𝒁 is an 𝑛𝑥𝑘 matrix of household-level socioeconomic covariates; 𝐖 is an 𝑛𝑥𝑛 spatial weighting matrix with elements 𝑤𝑖𝑗, parameterizing the distance between households 𝑖 and 𝑗, ∀ 𝑖, 𝑗 and 𝑤𝑖𝑗 = 0 if 𝑖 = 𝑗; 𝐖𝐲
and 𝑾𝑳𝑪𝑪𝑘 are the (first order) spatial lags of the column vectors 𝐲 (= ∑𝒋=𝟏 𝑤𝑖𝑗𝑦𝑗) and 𝑳𝑪𝑪𝑘 (=∑𝒋 𝑤𝑖𝑗𝐿𝐶𝐶𝑖𝑗
𝑘𝒏=𝟏 ), respectively.
Equation 2 models the disturbance terms as a spatially weighted average of the disturbances of the other study households; 𝐌 is an 𝑛𝑥𝑛 spatial weighting matrix with elements 𝑚𝑖𝑗 with 𝑚𝑖𝑗 = 0 if 𝑖 = 𝑗10, 𝐌𝛆 is a spatial lag of 𝛆; and 𝐮 is an nx1 vector of errors assumed to be independently and identically distributed. The parameters ρ and λ are the spatial lag and spatial error terms to be estimated11, along with 𝛃 (the main parameters of interest), 𝚽 and 𝚲. A statistically significant ρ̂ suggests lag dependence in poverty while a significant λ̂ indicates spatial error correlation. Weestimate Equations 1 and 2 using maximum likelihood (ML) (Drukker et al. 2013; Jeanty 2010) and, based on Lagrange Multiplier tests (Paraguas and Kamil 2015), we reject (could not reject) the null that ρ̂ (λ̂ ) is zero. In the next section, therefore, we present ML estimates of from Equation 1, and reportHuber-White standard errors.
9 See Wont and Lee (2005) for additional details on Moran’s I test. 10 𝐖 and 𝐌 can be the same or different (for example, 𝐌 = 𝐖2). The analysis herein uses the same spatial weighting matrix(𝐖 ) for 𝐲 and 𝛆. As is commonly done (for the sake of comparability of spatial weighting matrices with different parametrization of distance), 𝐖 a row-standardized (𝐖∗) such that 𝐖∗x𝑻 = 𝑻, where 𝑻 is an 𝑛𝑥1 vector with elements 𝑡𝑖𝑗 =1 ∀ 𝑖, 𝑗. 11 See Kelejian and Prucha (2004, 1998) for assumptions and conditions about the spatial weighting matrix, the estimated autoregressive parameters, and the spatial lag variables and Drukker et al. (2013) for applications in Stata software.
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5. Results
Table 2 shows estimates of the effect of land cover change on harvest value based on 25 pixels buffer zone. We use different specifications, where we first control for just land cover change and progressively control for household socioeconomic variables, agricultural inputs, region-level population and poverty (trends as well as initial levels), and finally elevation and market access (as measures of agricultural potential). Households living in areas that changed from natural cover in 1994 to productive cover in 2014 are associated higher harvest value than those who lived on a degraded land that turned into productive. The difference amounts to about 1,000 GHC ($950 in 2011 PPP terms). As mentioned, a shift from natural cover to productive cover is expected, at least in the short term, to improve the wellbeing of smallholder farmers who primarily rely on agriculture for their livelihood. The welfare of households associated to the Degraded-Productive change could be relatively low given the initial infertility of the soil, particularly unsuitable for agricultural production.
Similar effects appear when looking at the relationship between land cover changes and maize yield (Table 3). Households who reside in areas characterized by Natural-Productive change, with a Natural cover in both years, or with a Natural cover in 1994 and Bare land in 2014 (Natural-Other change) all appear to attain higher maize yield, relative to households in areas characterized by Degraded-Productive change, although the latter association is not significant in the more parsimonious specification (Table 3, column 6). These results seem to confirm that initial conditions matter, and that the land cover twenty years ago can be a good proxy for soil fertility today. Areas covered by vegetation in 1994 are generally suitable for agricultural production, while areas initially degraded are hard to restore.
Parameters of control variables show the expected sign, are generally significant, and consistent across different specifications. Total land area operated by the household is positively associated with harvest value, while it does not seem to be associated with maize yield. This results is consistent with several studies that have documented inverse land size-productivity relationship (Sen 1966; Deolalikar 1981; Carter 1984; Barrett 1996). Female headship, distance to basic services, and soil erosion are all negatively associated with agricultural production and productivity, while household size, non-agricultural wealth, and agricultural inputs (use of hired labor and irrigation) show a positive correlation with the outcome variables.
G42
12PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
se 0 . 4 6 8
0 . 4 5 7
0 . 2 6 8
0 . 4 7 5
0 . 8 1 4
0 . 0 2 8
0 . 2 1 0
0 . 0 3 5
0 . 1 0 1
0 . 0 6 7
0 . 1 2 9
0 . 0 7 1
0 . 6 4 1
0 . 1 8 4
0 . 5 1 5
0 . 3 5 4
0 . 1 9 0
1 . 1 0 9
0 . 0 4 5
3 . 5 5 4
0 . 0 3 0
6 . 5 4 2
0 . 0 0 1
0 . 0 0 7
2 . 7 2 9
0 . 1 0 5
0 . 1 2 9
Tab
le 2
: Effe
cts o
f lan
dcov
er c
hang
e on
har
vest
val
ue (t
hous
ands
of G
HC
)
coef
seco
efse
coef
seco
efse
coef
seco
efse
0.65
40.
441
0.02
30.
400
0.01
60.
408
-0.1
070.
420
-0.1
140.
425
0.16
60.
468
1.25
6***
0.46
00.
878*
*0.
430
0.92
5**
0.43
70.
848*
0.44
10.
843*
0.44
40.
985*
*0.
457
-0.0
900.
299
-0.1
000.
269
-0.1
050.
268
-0.0
590.
267
-0.0
710.
272
-0.0
400.
268
0.50
60.
493
0.40
30.
465
0.37
30.
464
0.42
40.
466
0.41
60.
467
0.55
10.
475
1.58
3*0.
933
1.84
6**
0.85
51.
786*
*0.
839
1.75
4**
0.83
51.
756*
*0.
835
1.46
1*0.
814
0.02
10.
029
0.02
90.
028
0.03
30.
029
0.03
30.
029
0.03
20.
028
-0.7
36**
*0.
204
-0.7
54**
*0.
205
-0.7
59**
*0.
205
-0.7
62**
*0.
206
-0.6
85**
*0.
210
-0.0
72**
0.03
3-0
.086
**0.
034
-0.0
93**
*0.
034
-0.0
94**
*0.
035
-0.0
89**
0.03
5-0
.099
0.10
3-0
.064
0.10
3-0
.073
0.10
3-0
.073
0.10
3-0
.087
0.10
10.
458*
**0.
064
0.44
4***
0.06
40.
435*
**0.
064
0.43
7***
0.06
50.
477*
**0.
067
0.74
8***
0.12
50.
693*
**0.
127
0.68
9***
0.12
70.
690*
**0.
127
0.65
1***
0.12
9-0
.294
***
0.07
1-0
.333
***
0.07
10.
071
-0.3
36**
*0.
072
-0.3
28**
*0.
071
2.20
9***
0.62
80.
635
2.14
3***
0.64
41.
913*
**0.
641
0.72
3***
0.18
0
-0.3
33**
* 2.16
3***
0.
692*
**0.
182
0.69
3***
0.18
30.
748*
**0.
184
0.45
70.
514
0.45
50.
512
0.44
80.
514
0.61
90.
515
0.59
0*0.
358
0.57
50.
358
0.56
40.
348
0.47
60.
354
0.18
50.
186
0.19
0-0
.570
***
0.19
0-0
.590
***
0.23
80.
826
-0.6
03**
* 0.
366
0.84
0-0
.608
***
0.33
60.
875
1.10
90.
067
0.04
20.
066
0.04
30.
045
-0.1
640.
794
-1.9
97*
0.00
2 2.
956
3.55
40.
110*
**0.
030
3.97
66.
542
0.00
00.
001
-0.0
080.
007
Nat
ural
-Oth
erN
atur
al-P
rodu
ctiv
ePr
oduc
tive-
Prod
uctiv
ePr
oduc
tive-
Oth
erD
egra
ded-
Oth
erH
ouse
hold
siz
eFe
mal
e ho
useh
old
head
Mea
n ye
ars
of e
duca
tion
in th
e ho
useh
old
Tot
al d
epen
denc
y ra
tioT
otal
ope
rate
d la
nd (
ha)
Non
-agr
icul
tura
l wea
lth (
inde
x)D
ista
nce
to b
asic
ser
vice
s in
dex
The
hou
seho
ld u
ses
irri
gatio
nH
H u
ses
hire
d la
bor
Prac
tices
mix
ed (
crop
-liv
esto
ck) a
gric
ultu
re
Shar
e of
par
cels
with
incr
uste
d so
il A
ffec
ted
by s
oil e
rosi
onSo
il ni
trog
en c
onte
nt (g
/kg)
Cha
nge
in d
istr
ict p
opul
atio
n (2
010-
2000
) C
hang
e in
reg
iona
l pov
erty
rat
e (2
012-
1998
) D
istr
ict p
opul
atio
n in
200
0 ('0
000
cens
us)
Reg
ion
pove
rty
rate
in 1
998
(GL
SS)
Tra
vel t
ime
to n
eare
st to
wn
of 5
0K (m
inut
es)
Ele
vatio
n of
hou
seho
ld's
resi
denc
e (m
eter
s)
Con
stan
t0.
832
0.58
7-0
.315
0.59
5-1
.120
0.97
1-1
.348
0.98
2-1
.299
1.02
0-2
.637
2.72
90.
433*
**0.
086
0.41
5***
0.09
10.
368*
**0.
096
0.36
5***
0.09
60.
366*
**0.
097
0.33
1***
0.10
53.
393*
**0.
137
3.11
3***
0.13
63.
063*
**0.
134
3.06
0***
0.13
43.
060*
**0.
133
3.02
8***
0.12
9/r
ho/s
igm
aN
umbe
r of o
bser
vatio
ns
Log
-Lik
elih
ood
chi2
p Wal
d**
* p<
0.01
, **
p<0.
05, *
p<0
.1. R
epor
ted
are
hete
rosc
edas
ticity
-rob
ust s
tand
ard
erro
rs. C
oeff
icie
nt e
stim
ates
of l
agge
d la
nd c
over
cha
nge
vari
able
s no
t sho
wn.
1,28
0
m1
m2
m3
m4
m5
m6
-3,2
39.0
2 30
8.05
8 0.
000
9.99
8
-3,3
88.3
3 33
.253
0.
000
25.4
07
-3,2
77.3
5 23
4.51
0 0.
000
20.8
55
-3,2
54.8
6 28
3.65
7 0.
000
14.8
18
-3,2
53.7
4 28
3.48
8 0.
000
14.4
94
-3,2
53.7
2 28
5.65
3 0.
000
14.1
79
G42
13PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
Tab
le 3
:. Ef
fect
s of l
andc
over
cha
nge
on m
aize
yie
ld (t
on/h
ecta
re)
coef
seco
efse
coef
seco
efse
coef
seco
efse
0.20
8**
0.09
20.
176*
0.09
00.
183*
*0.
091
0.19
2**
0.09
10.
184*
*0.
091
0.13
10.
092
0.22
8**
0.09
00.
211*
*0.
084
0.22
7***
0.08
30.
232*
**0.
082
0.22
7***
0.08
10.
192*
*0.
079
0.07
8*0.
045
0.07
8*0.
044
0.07
8*0.
045
0.07
6*0.
045
0.05
70.
046
0.03
50.
047
0.04
70.
072
0.03
40.
069
0.03
60.
070
0.03
20.
070
0.01
90.
070
-0.0
350.
074
0.05
00.
106
0.04
90.
110
0.04
30.
111
0.04
50.
111
0.05
30.
112
0.06
30.
116
0.01
9**
0.00
80.
020*
*0.
008
0.02
0**
0.00
80.
020*
*0.
008
0.01
9**
0.00
8-0
.111
**0.
045
-0.1
13**
0.04
60.
046
-0.1
17**
0.04
6-0
.119
***
0.04
6-0
.001
0.00
9-0
.000
0.00
90.
009
-0.0
010.
009
-0.0
010.
009
0.03
80.
029
0.04
30.
029
-0.1
13**
0.
000
0.04
40.
029
0.04
50.
029
0.04
20.
029
-0.0
25*
0.01
5-0
.025
0.01
5-0
.024
0.01
6-0
.022
0.01
6-0
.025
0.01
60.
071*
*0.
030
0.06
7**
0.03
00.
067*
*0.
030
0.06
8**
0.03
00.
070*
*0.
031
-0.0
44**
0.01
70.
017
0.01
70.
018
0.01
8-0
.046
***
0.31
0*0.
171
-0.0
46**
* 0.
313*
0.17
1-0
.051
***
0.27
50.
171
-0.0
51**
* 0.
260
0.16
80.
047
0.04
50.
049
0.04
40.
051
0.04
40.
055
0.04
70.
032
0.12
40.
033
0.12
40.
019
0.12
40.
023
0.12
60.
072
0.08
10.
073
0.08
10.
053
0.08
30.
038
0.08
3-0
.138
***
0.04
3-0
.138
***
0.04
3-0
.146
***
0.04
4-0
.147
***
0.04
4-0
.034
0.19
3-0
.040
0.19
5-0
.099
0.20
1-0
.142
0.25
6-0
.004
0.00
9-0
.007
0.00
9-0
.010
0.00
9-0
.289
0.18
20.
229
0.82
8-0
.005
0.00
50.
799
1.54
7-0
.000
0.00
0-0
.001
0.00
2
Nat
ural
-Oth
erN
atur
al-P
rodu
ctiv
ePr
oduc
tive-
Prod
uctiv
ePr
oduc
tive-
Oth
erD
egra
ded-
Oth
erH
ouse
hold
siz
eFe
mal
e ho
useh
old
head
Mea
n ye
ars
of e
duca
tion
in th
e ho
useh
old
Tot
al d
epen
denc
y ra
tioT
otal
ope
rate
d la
nd (
ha)
Non
-agr
icul
tura
l wea
lth (
inde
x)D
ista
nce
to b
asic
ser
vice
s in
dex
The
hou
seho
ld u
ses
irri
gatio
nH
H u
ses
hire
d la
bor
Prac
tices
mix
ed (
crop
-liv
esto
ck) a
gric
ultu
re
Shar
e of
par
cels
with
incr
uste
d so
il A
ffec
ted
by s
oil e
rosi
onSo
il ni
trog
en c
onte
nt (g
/kg)
Cha
nge
in d
istr
ict p
opul
atio
n (2
010-
2000
) C
hang
e in
reg
iona
l pov
erty
rat
e (2
012-
1998
) D
istr
ict p
opul
atio
n in
200
0 ('0
000
cens
us)
Reg
ion
pove
rty
rate
in 1
998
(GL
SS)
Tra
vel t
ime
to n
eare
st to
wn
of 5
0K (m
inut
es)
Ele
vatio
n of
hou
seho
ld's
resi
denc
e (m
eter
s)
Con
stan
t0.
380*
**0.
096
0.27
1**
0.11
80.
244
0.21
40.
260
0.22
30.
356
0.23
20.
432
0.69
00.
249*
**0.
070
0.24
6***
0.06
80.
225*
**0.
067
0.22
3***
0.06
7`
0.06
70.
217*
**0.
068
0.68
1***
0.05
30.
667*
**0.
052
0.66
3***
0.05
00.
663*
**0.
051
0.66
2***
0.05
00.
661*
**0.
050
/rho
/sig
ma
Num
ber o
f obs
erva
tions
L
og-L
ikel
ihoo
dch
i2p W
ald
***
p<0.
01, *
* p<
0.05
, * p
<0.1
. Rep
orte
d ar
e he
tero
sced
astic
ity-r
obus
t sta
ndar
d er
rors
. Coe
ffic
ient
est
imat
es o
f lag
ged
land
cov
er c
hang
e va
riab
les
not s
how
n.
-1,1
38.7
6 72
.073
0.
000
10.1
22
-1,1
72.9
3 17
.091
0.
072
12.7
56
-1,1
50.7
4 42
.364
0.
001
12.9
54
-1,1
42.4
7 53
.032
0.
000
11.3
60
-1,1
42.3
9 59
.403
0.
000
10.9
59
-1,1
41.5
1 60
.933
0.
000
11.1
18
1,13
2
m1
m2
m3
m4
m5
m6
G42
14PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
6. Conclusion
The livelihoods of households living in northern Ghana are strongly interdependent with landscape characteristics. Population and welfare dynamics in these regions are affected by the changes in land cover that modify availability and ecosystem services of natural capital, while in turn exerting pressure on the natural resource base. Therefore, understanding these linkages is crucial to design welfare-improving policies for local communities aimed at restoring the environment over the long term.
By combining remote sensing data with geo-referenced household surveys we propose an innovative methodology to observe the interlinkages between changes in landscape and household livelihoods. We show how GPS information on parcels’ location collected in household surveys can contribute to refine land cover classification procedure compared to more traditional ground-truthing approaches. In addition, we demonstrate how unsupervised classification methods can be used to map land cover in northern Ghana in 1994, bypassing the need of historical ground-truthing points. Finally, we employ a spatial regression analysis to examine the links between historical land cover change and current rural livelihoods, as proxied by maize yield and harvest value, while controlling for spatial correlation.
Through mapping of land cover distribution across northern Ghana in both 1994 and 2014, we observe a large expansion of land devoted to crop cultivation over the last twenty years, which mainly replaced natural vegetation areas. Bare land has also increased, even though at a lower extent than cultivated areas. This is likely caused by practices of agricultural extensification rather than intensification. Regression results show how households located in areas that turned from natural (forest, shrubs, savannah, or water bodies) to productive covers (cropland and urban areas) over the last twenty years are associated higher crop production and productivity today, relative to those living in current productive areas that were previously degraded (or bare land). These findings suggest that land initial conditions matter for agricultural production and livelihoods, and that the cover type twenty years ago seems to be a good proxy for soil fertility today. Our study confirms that degraded bare soils are very difficult to restore as well as render fertile and productive again. Therefore, our findings call for effective conservation practices aimed at restoration of the soil nutrient content that prevents land degradation.
It is worth noting that the observed positive impact of the natural-productive change may vanish in the long term, when the increased scarcity of natural resources will begin to play a detrimental role for local livelihoods. Furthermore, the overexploitation of cultivated areas that prevent the regeneration of soil nutrients will lead to additional degradation over the long run. In recent years these concerns are indeed the driving force behind the increased focus on the need to sustainably intensify the smallholder agricultural sector, in order to balance the trade-off between productive land and natural stock, allowing a self-regenerating system. The medium-to-long term effects of different land cover changes merit further consideration beyond the scope of this study, spurring further research.
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15PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
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16PROCEEDINGS ICAS VII Seventh International Conference on Agricultural Statistics I Rome 24-26 October 2016
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APPENDIX
Figure A1: Land Cover in Northern Ghana, 1994
Source: Authors’ calculation based on data from Landsat 5, Ghana Africa RISING Baseline Survey
(GARBES), and Google Earth.
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Figure A2: Land Cover in Northern Ghana, 2014
Source: Authors’ calculation based on data from Landsat 8, Ghana Africa RISING Baseline Survey
(GARBES), and Google Earth.
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Figure A3: Share of land cover (by year and buffer zone)
Note: For each buffer zone, the share of a given land cover is computed as the ratio of the number of pixels represented by the cover and the total number of pixels in the buffer zone.
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of land cover
2014
1994
9 pixels
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of land cover
2014
1994
25 pixels
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of land cover
2014
1994
49 pixels
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of land cover
2014
1994
81 pixels
Cropland Forest
Bareland
Savannah/Shrubs
Water Urban
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Figure A4: Summary of Moran’s I tests of spatial autocorrelation in the outcome variables
Figure A5a: Summary of Moran’s I tests of spatial autocorrelation in land cover trajectories
-10
12
3
Spatially
lagged H
arv
est
-2 0 2 4 6Harvest
WHarvest Fitted values
(Moran's I=0.0753 and P-value=0.0010)
-10
12
3
Spatially
lagged Y
ield
0 5 10Yield
WYield Fitted values
(Moran's I=0.0523 and P-value=0.0010)
-.5
0.5
11.5
2
Spatially
lagged N
atu
ral_
Oth
er
-1 0 1 2 3Natural_Other
WNatural_Other Fitted values
(Moran's I=0.1899 and P-value=0.0010)
-10
12
3
Spatially
lagged N
atu
ral_
Pro
ductive
-1 0 1 2 3Natural_Productive
WNatural_Productive Fitted values
(Moran's I=0.1917 and P-value=0.0010)
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Figure A5b: Summary of Moran’s I tests of spatial autocorrelation in land cover trajectories (Cont’d)
Figure A5c: Summary of Moran’s I tests of spatial autocorrelation in land cover trajectories (Cont’d)
-1S
patially
lagged P
roductive_P
roductive
01
23
Spatially
lagged D
egra
ded_P
roductive
-1 0 1 2 3Degraded_Productive
WDegraded_Productive Fitted values
(Moran's I=0.0987 and P-value=0.0010)
-1-.
50
.51
-1 1-.5 0 .5Productive_Productive
WProductive_Productive Fitted values
(Moran's I=0.2477 and P-value=0.0010)
01
23
4
Spatially
lagged D
egra
ded_O
ther
0 2 4 6 8Degraded_Other
WDegraded_Other Fitted values
(Moran's I=0.0874 and P-value=0.0010)
-10
12
3
Spatially
lagged P
roductive_O
ther
0 1 2 3 4Productive_Other
WProductive_Other Fitted values
(Moran's I=0.1301 and P-value=0.0010)
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