Country Paper Presentation, GGGI_2016

Agricultural Adaptive Strategies in the context of Climate Change and Gender Equity in Rwanda

BY

Alfred R. Bizoza

Agricultural Economist, University of Rwanda, Kigali, Rwanda

Country Paper Presented at the Fourth Green Growth Knowledge Platform (GGKP) Annual Conference: Transforming Development through Inclusive Green Growth

6-7 September 2016, Jeju Island, Republic of Korea

Abstract

The interest of this paper is on the adoption of agricultural adaptive measures in the context of climate change and variability and gender equity in Rwanda. Bench terraces, erosion fences, erosion ditches, and planting of trees are the most adaptive measures considered for the analysis. Results from a multinomial logit estimation put into perspective gender, land tenure security, and capacity variables such as wealth index for future adaptation to climate change and variability in Rwanda. The comparison between male and female headed households for various interventions including those of climate adaptation needs to be put into a specific context and perspective.

Key words: Climate change, adaptive measures, gender, Rwanda.

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1. Introduction

Extreme weather shocks, such as flooding from heavy rains and longer and more severe periods of droughts, are now facing Sub-Saharan Africa including Rwanda (Muhire and Abutaleb, 2014). Agriculture is a sector that will see the highest impacts from these shocks; especially small-scale farmers, a majority of which are female (Goytom et al. 2014; Worlfrom et al. 2010; Schelenker and Lobell, 2010). Recent extreme events that Rwanda has experienced include those of 1997, 2006, 2007, 2009, 2012, and 2015. The agricultural sector has been consequently affected causing low productivity especially in the northern and eastern parts of the country due to unpredictable weather variability, among others. The May 2012 wet season for example, has caused agricultural loses equivalent to Rfw 31,926,941 of which 50 % were for seed loses, 32 % for labour, and 18 % for fertilizers (REMA, 2013). Therefore, farmer’s adaptation strategies are meant to foster increased agricultural production, ensure food security, continue to create more

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Country Paper Presentation, GGGI_2016 jobs (currently 70% of jobs are created in this very sector), and contribute to unlocking of other opportunities in the service and industrial sector.

Environmental issues such as flooding, droughts, erosion, landslides, and climate change remain major challenges despite the efforts carried out to address these issues in Rwanda during the last 20 years post the 1994 genocide. The use of current soil and water conservation measures as well as adjusting the farming practices for the sake of alienating the effects of excessive rain and temperature depends on many social and economic factors as well as bio-physical ones.

The analysis of the adoption of agricultural adaptation measures in the context of climate variability and gender equity in Rwanda is scarce, even non-existent. Precedent studies have integrated idea of gender in the adoption of climate change adaptation measures from a perspective of households headed by male and those headed by female to estimate differential gender impacts. We think that this perspective is very simplistic and would undermine the reality hidden in local institutions and intra-household relationships. A household headed by a male does not necessarily imply that a man is dominant in the decision-making. The reverse is also possible. Consistent with a recent study just five months ago by ODI (2016), measuring gender equity with simple indicators of representation – such as the number or proportion of women in various programmes/ interventions, or even the number of women in decision-making positions, is a start but can still be deepened.

In the Rwandan context, female-headed households implicitly mean that the women are widowed, are single mothers, or are divorced. Those households headed by males imply also having their partner wives – that is the headship of the household is somewhat done by both male and female. This is the household that is often compared with the female-headed only. Secondly, it is often assumed that households headed by females are likely to be poorer and hence find difficulty to adopt new agricultural adaptation measures. But this is not always the case. Men also find difficulty to pay for new technologies because they are expensive, as do women. The purpose of this contribution is to link agricultural adaptive measures to climate variability, and gender equity, and to identify the existing gaps and limitations in the empirical analysis that is gender-based. The leading assumption is that women’s adaptive capacity to address effects of climate change and variability is determined by their power over control of productive resources and in their level of income or assets endowment. The adaptation to climate change and variability is underpinned by barriers, limits, and cots, but these are not yet fully understood globally and in Rwanda in particular.

Towards this end, we start by introducing the main environmental problems and adaptation measures used by farmers in a historical perspective. In section three, we link climate change and gender consideration by assessing whether women and men are equally affected by the environmental problems identified. In section four we assess empirically the determinants of adoption of agricultural adaptive strategies to climate change and variability, with focus on the

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Country Paper Presentation, GGGI_2016 assessment of the gender differential effects. The paper concludes with some policy recommendations for future adaptation of climate change in the context of gender equity.

2. Gendered Climate Change Effects and Adaptation Strategies

2.1.Environmental Problems and Adaptive strategies

The main environmental problem faced by farmers in Rwanda is climate change. Based on the national statistics generated by the National Institute of Rwanda (2015), about 58% of the respondents confirm to have experienced climate change; which was defined in terms of weather or climate variability. When the data is disaggregated in terms of male and female headed households 59.8% of males and 58.5% of females sustain to be affected by climate change. Other consequent problems reported include floods (4%); erosion (14%), landslides (10%), destructive rains (8%), and loss of soil fertility (5%). To respond to these changes, Rwanda has introduced a number of measures, although most of these were initiated for agricultural purposes rather than for climate change adaptation per se. Later, some of these adaptation measures started to be seen as important in addressing negative effects of extreme rainfall and higher temperature. Some of these comprise soil and water conservation (SWC) measures like bench terraces, ditches combined with erosion fences, and tree plantation. Erosion fences (55.5%) and ditches (37%) are the dominant measures used by farmers. Bench terraces are less used partly due to their relatively high costs (Bizoza and de Graaff, 2012).

Table (1). Environmental problems and adaptive measures used in Rwanda

If : Male (%) Female (%) Both Male and Females Yes (%) No (%) Yes (%) No (%) Yes (%) No (%) The land is protected from Erosion 72.3 27.6 73.7 26.3 78.6 21.4 The land was irrigated in the las 12 months 4.9 95.1 3.6 96.4 4.2 95.7 The land was used for land use consolidation activity 10.9 89.1 13.5 86.4 17.1 82.9 Faced environmental problems in their plots 22.4 77.6 24.0 76.0 24.3 75.7 Types of SWC measures used Terracing 3.3 4.2 5.4 Erosion fence 46.0 51.2 55.5 Erosion ditch 48.5 42.0 36.6 Planting trees 1.0 1.6 1.7 Other 1.1 1.0 0.8 Types of environmental problems accounted Floods 3.0 3.6 3.6 Erosion 13.3 15.0 14.0 Landslides 6.1 8.1 10.2 Change of climate 59.8 58.5 57.7 Destructive rains 8.6 7.9 7.7 Loss of soil fertility 7.5 5.7 5.2 Other 1.7 1.3 1.6 Source: EICV4 (NISR, 2015)

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The following aerial photographs by REMA were taken immediately after the May 2012 wet season that was characterized by extreme rains causing more land slides, road landslides damages, crop damages, and destruction of houses without sufficient buffer zones. The economic cost of these damages was estimated at 1.4% of the total 2011/2012 GDP only for 8 districts out of a total of 30 Districts in Rwanda (REMA, 2013). An earlier study by SEI (2009) estimated an economic cost of 0.1% to 0.6 % GDP only for 2 Districts during the 2007 flooding.

Figure (1): Aerial photographs of physical flooding effects (REMA, 2012)

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2.2. Gender Equity and Climate Change

Gender equity is increasingly becoming the engine of sustainable development. The 5th goal of the Sustainable Development Goals (SDGs) – Achieve gender equality and empower all women and girls – reflects the overreaching global interest of placing women at the heart of development frameworks towards sustainable development. Particular to climate change, there is a need, first of all, to have a common understanding of what gender means in the debate of climate change and adaptation.

At present, Rwanda is among the top five African countries that are doing best on gender equality, together with South Africa, Namibia, Mauritius and Malawi according the 2015 Africa Gender equality Index. The index measures gender equality across three dimensions: (1) equal economic opportunities in business and employment, (2) human development, and (3) laws and institutions (AfDB, 2015). Three respective questions are assessed. Do women have equal economic opportunities in business and employment? Do girls and boys have equal opportunities at school? Are women and men equally well represented in institutions? Overall, Rwanda is the second and performs well in assuring equal economic opportunities and women’s representation in laws and institutions as depicted by Figure (2).

Figure 2. Top 10 best performer countries on gender equality, Africa

Source: Adapted from AfDB (2015).

One of the pitfalls observed from the gender based literature is that gender is mostly seen as equal to women. Whenever people talk about gender they understand women. Consequently, most of the national datasets generated by the national institutes of statistics are disaggregated in terms of females and males to implicitly imply gender. These figures are simply a categorical representation of two categories of human beings. Gender is as complex as climate change. Therefore, disaggregating gender by male and female is not enough. Their interacting forces

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Country Paper Presentation, GGGI_2016 matter more towards equal access to economic opportunities, human development, and representation in laws and institutions. The notion of gender is also linked to the concept of inequality and vulnerability. Statements like “gender inequality and social and institutional discrimination remains high” would imply directly that women are negatively affected more than men. But this is not the panacea. You may also find men being affected negatively for both social and institutional discrimination. Therefore, for the case of climate change effects, focusing on women and putting aside men may obscure the negative effects of climate change on men. High rainfall or extreme temperature will affect severally both men and women. The inherent argument is that women are more severely affected by extreme events than men. This is partly explained by the fact the majority of women are involved in agriculture which in turn is affected by climate change. This statement on women’s labour in agriculture was also verified by FAO (2011). It is estimated 40% in the developing world and ranges between 20% in the Americas to almost 50% in Africa. The FAO’s State of food and agriculture (2015) shows that women supply 43% of all agriculture labour in low and middle income countries. This share reaches at least half in many countries of sub-Saharan Africa and elsewhere. Therefore, whether men and women are affected or adapt differently to climate change is to be put into a very specific context and perspective.

Information in Table (2) portrays the extent to which land plots owned by women and men are affected by the identified environmental problems: floods, erosion, landslides, and change of climate, destructive rains, and loss of soil fertility. With the current land tenure regularization in Rwanda, it has become possible to have some land parcels solely owned by women or men and both. The contrast between men and women is done only for those plots owned exclusively by women and those exclusively by men. Furthermore, this comparison is done along the three categories of poverty levels: extreme, poor, and non-poor. These categories are obtained based on the total poverty line of RWF 159,375 in January 2014 prices (NISR, 2015). This includes the consumption of both food and non-food items, because poverty is well hypothesized from the literature as being one of the main causes of gender inequality. Furthermore, poverty and unequal access to resources can exacerbate vulnerability to climate change (UNFCCC, 2011).

Looking at these figures, under the first category of poverty level, 51% of land plots owned by men are affected by the climate change, compared to 55 % of women-owned plots. Under the second category, 61% of men-owned plots are affected against 57 % of women-owned while non-poor men (61%) are more affected than women non-poor (60%). Although there are some variations within each type of environmental problem, only women-owned plots under the extreme poverty category are more affected than those of men whereas in category two and three of poverty land plots owned by men are the most affected by climate change. However, the non- classical test of mean comparison between women and men as well as between the three categories of poverty demonstrates no statistically significant difference between these categories in terms of environmental problems even at 10% level of significance.

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Table (2). Main environmental problems per gender and poverty levels (%)

Poverty Category Extremely Poor Poor Non-Poor Env. Problems/ Gender of the owners Male Female Total Male Female Total Male Female Total Floods 3.4 3.2 3.2 4.7 4.4 4.5 2.4 3.4 3.1 Erosion 20.7 14.7 16.5 12.9 16.6 15.5 12.0 14.4 13.7 Landslides 7.3 10.4 9.5 5.0 7.6 6.8 6.2 7.8 7.3 Change of climate 50.8 54.6 53.5 61.0 56.7 58.0 61.1 60.0 60.3 Destructive rains 9.5 8.6 8.9 9.7 6.7 7.6 8.1 8.1 8.1 Loss of soil fertility 6.7 7.7 7.4 6.0 6.1 6.1 8.2 5.1 6.1 Other 1.7 0.7 1.0 0.6 1.9 1.5 2.0 1.2 1.4 N 179 441 620.0 318 751 1069 989.0 2137.0 3126.0 Source of data: EICV4 (NISR, 2015).

With regard to the adaptation measures, using nominal figures in Table (3) below, though bench terraces are the least used by the sample households compared to other adaptive measures; plots owned by women received more bench terraces (4% to 5%) compared to men (3% to 4%). Moreover, we see more women using erosion fences (50% to 53%) compared to men (45% to 51%). But plots owned by men use more ditches (42% to 49%) compared to women (39% to 43%). These differences observed are not statistically significant, however. Therefore, these descriptive statistics provide only information in terms of men and women’s representation. They do not tell of any statistically significant difference between men and women in terms of being affected by climate change or the use of agricultural adaptive measures.

Table (3) Adaptive measures per gender and poverty categories

Poverty Category Extremely poor Poor Non-Poor Adap. Measures/ Gender Male Female Total Male Female Total Male Female Total Terracing 3.7 4.5 4.2 4.0 4.2 4.1 3.1 4.2 3.80423814 Erosion fence 51.4 53.4 52.8 45.5 52.7 50.6 45.2 50.2 48.6 Erosion ditch 42.6 39.5 40.5 48.1 40.7 42.9 49.6 42.9 45.1 Planting trees 1.2 1.5 1.4 1.0 1.4 1.3 1.0 1.7 1.5 Other 1.1 1.0 1.0 1.2 0.9 1.0 1.1 1.0 1.0 N 785 1857 2642 1378 3141 4519 4464 8867 13331

3. Data and Empirical Model

Data: A further analysis to assess gender differentials in terms of agricultural adaption is done based on both a large and small-scale surveys. We draw from the EICV4 data set published by the National Institute of Rwanda (NISR, 2015). This dataset is published every three years based on the Integrated Household Living Conditions Survey. This survey focuses on poverty as measured in consumption terms but also captures other dimensions including the environmental 7 | P a g e

Country Paper Presentation, GGGI_2016 one. Additional data used to provide insights for this study at a micro-level was obtained from a survey of 1041 plots obtained from 347 households as part of the overall post-doctoral research project on the “Economics of Climate Change and Resilience in Rwanda” sponsored by the Swedish International Development Cooperation Agency (SIDA) in collaboration with the University of Rwanda.

Survey respondents for the small-scale survey provided information on their socio-economic characteristics, information related to their institutions, plot level information on investment in soil and water conservation measures such as bench and progressive terraces, alternative climate change coping strategies, and whether they participate in social protection programmes such as Vision 2020 Umurenge Programme and Girinka – one cow per poor family programme. The survey was conducted in July and September 2015 in three Districts: Bugesera (East), Gicumbi (North), and Nyamagabe (South). The following Table (4) provides some key environmental characteristics of these Districts.

Table 4: Some environmental characteristics of sample District

District Busegera Gicunbi Nyamagabe Altitude 1,100-1,780m 1,500-1,800 m 1800-2700 m Average Temperature 26-29°C 15-16°C 18°C Main soil type Generally sandy Lateritic soils and granites Generally acidic (pH ranging from 3.6 - 5) Climate Dry Tropical Humid tropical Rainfall 800-1600 mm 1,200-1,500mm 1300-1450 mm Source: District Development Plans (2013)

Empirical Model: The multinomial logit (MNL) model is specified to assess gender differentials in farmer’s adaptation to climate change using bench terraces, erosion ditches, and agro forestry or tree planting. This model has been used in the analysis of farmer’s choice adaptation strategies in Eastern Africa such as in Ethiopia. We follow, Maddala (1983), Wooldridge (2002) and Temesgen et al. (2009) to estimate the following MNL response probabilities equation:

exp ⁡(푥훽푗 ) 푃 푦 = 푗/푥 = [Equation 1] 푗 1+ ℎ−1 exp xβh ,j=1,…,J

Where 푦 stands for the above adaptation options and 푥 represents all covariates identified including household, institutional, wealth, and plot level characteristics. From the estimation of this model we assess how a change in elements of the vector 푥 affects an independent choice of the adaptive strategies. The following Table (5) provides the description of the model variables.

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Table (5). Description of the model variables

Explanatory Variable Mean S.D Description and Measurement Socio-Economic Characteristics Years of formal education 4.8 3.5 Continuous Size of the household 5.1 1.9 Continuous Available labour 46.4 13.6 Continuous, total number of family members with or greater than 18 years old

Gender of the head of household 0.8 0.4 Dummy, equals 1 if female and 0 if male Farm income 213902 240024 Continuous and measures annual farm income in Rwandan Francs (Rwf) Off-farm income 114664 271823.4 Continuous and measures annual non-farm income in Rwandan Francs (Rwf) Consuption agregate 260913.5 264794.4 Continious measures the wealth index ( for the Macro-level data) Institutional Related factors CBO membership 0.6 0.5 Dummy, equals 1 if the head of the household belongs to community based Access to early warning information on CV 0.4 0.5 Dummy, equals 1 if access to early warning information on climate change and variability, and 0 if otherwise. Received sufficient extension services 1.6 0.9 Frequency, measured in nominal values (1= once, 2= twice, 3= trice, 4 if more than 3 times per season). Access to agricultural credit 0.08 0.3 Dummy, equals 1 if the head had access to agricultural credit and 0 if otherwise. Access to market price information 0.6 0.5 Dummy, equals 1 if the head had access to price information and 0 if otherwise.

Number of days allocated to collective action 2.2 2.2 Continuous Land size 0.5 0.9 Continuous and measures the size of the land in hectares Homestead elevation (meter) 2121 192 Continuous, homestead elevation in meter above the sea level Location of the homestead vs. to the position of the0.6 hill 0.5 Dummy, equals 1 if the house is located either at the mid to the top hill, and 0 when at the valley or lower part of the hill. Land Tenure Security 0.87 0.33 Dummy, equals 1 if the house has the right to sell the plot or give this as acollateral for a bank loan. Participation in Social Protection Programmes Monthly benefits from VUP Program 0.04 0.2 Dummy, equals 1 if the household is supported under VUP and 0 if otherwise.

Participation to the Girinka Program 0.1 0.3 Dummy, equals 1 if the household is supported under Girinka Program and 0 if otherwise. Charity services by churches 0.04 0.2 Dummy, equals 1 if the household is supported Charity Services and 0 if otherwise. Community work- Umuganda 0.09 0.3 Dummy, equals 1 if the household participates under Community work – Umuganda and 0 if otherwise. Adaptive strategies Use of progressive terraces 0.4 0.5 Dummy, equals 1 if the house has used progressive terraces, and 0 if otherwise. Use of bench terraces 0.3 0.5 Dummy, equals 1 if the house has used bench terraces, and 0 if otherwise. Use of hedgerows coupled with trenches Dummy, equals 1 if the house has used hedgerows coupled with trenches, and 0 if otherwise. Tree planting Dummy, equals 1 if the house has used tree planting, and 0 if otherwise.

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4. Results and Discussion

4.1. Tracing climate change effects from farmer ‘recall memory

The survey asked farmers to recall memories about climate change and its negative effects in sample Districts Bugesera, Gicumbi, and Nyamagabe. Findings show that climate change is not a new phenomenon in Rwanda and its effects in terms of shocks were observed for at least 80 years. From farmers’ recall memories the first floods were observed in 1935 and these resulted, among others, in shifting cattle from their normal grazing land to areas with moderate rains. Table 6 below gives a snapshot of major climate change shocks and their effects in different periods. These results are consistent with those from REMA’s Environmental Status report that show that from 1988 to 2013 about 241 people died due to extreme weather effects, about 88,004 people were somewhat affected, 8,189 homes were destroyed or affected, and 2,746 hectares of land and 3,000 plantations were affected ( REMA, 2015).

Table (6). Tracing climate effects from farmers’ recall memory

Period District Extreme Event Observed Effects of Variability 1935; 1944-1945 Bugesera, Gicumbi, Nyamagabe Increased high rain intensity Agricultural impacts which resulted in a hunger known Ruzagayura. Lack of food and rely on bean leaves (umushogoro) and other wild plants. Deaths of many people of which some carrying tobacco known as Abayuku 1960s Gicumbi Drizzling period in Gicumbi Famine,---- migration to the neighbouring areas. Since this time Bugesera has been 1978 Bugesera Persistent drought in Bugesera area marked by irregular change between the drought and favorable weather. Destruction of houses, damaged crops in various farms, death of livestock, death of many people, migration. Most farmers confirm that they have observed this 1985-1986 Bugesera, Gicumbi, Nyamagabe Very heavy and strong rain phenomenon in their respective sectors. This period of ling drought resulted in a famine that was given different names, depending on the region, such as “Rucamakara” (cut charcoal), “Kinga abazukuru baraje” (close doors, grand children are coming), “Nsura winsaba” (visit me, but do not ask me food), “Mbaraje ndagana hehe?” (If I accommodate you, where should I 1986 and onwards Nyamagabe Long period of drought go?). 1993 ( June to November 1993)Nyamagabe Long dry season that lasted about 6 months Famers failed to plant followed by serious food shortage Low food productivity, increase of market prices of food. During this period food donation and food for work programs started with support from World Vision, CARITAS, and WFP , people’s migration. This period has really remained in the 1996 to 2002 Bugesera, Gicumbi, Nyamagabe Irregularity rain and sun far from farmer’s expectations memories of farmers especially those in Bugesera and Nyamagabe District. In 2003 Nyamagabe Heavy rains and strong wings Houses where affected In 2005-2006 Bugesera, Gicumbi, Nyamagabe Long period of dry season Serious food shortage with a famine named “ tronc commun or O level”; The period after 2006 Bugesera, Gicumbi, Nyamagabe Unexpected heavy rains followed by snows Agricultural damages and land slides In 2009 Nyamagabe Long rainy period from February to August Not effect was observed by farmers especially on crops. In 2010 Gicumbi Very heavy rain AgriculturalDeath of people, damages, crop damages, landslides, destruction hunger due of tohouses. also to shortage in sweet potatoes in In 2013 Gicumbi, Nyamagabe Heavy rain areas where Land Use consolidation was being implemented In 2014 Bugesera, Gicumbi, Nyamagabe Abrupt end of the rain season all over the country Famine in Bugesera District, In 2015 –Season A Bugesera, Gicumbi, Nyamagabe No much effects observed on the crops Delayed the planting in season B Source: Primary Data (2015) 10 | P a g e

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4.2.Costs of the adaptation strategies

Each of the adaption techniques used by farmers has its merits and constraints. These can be grouped in three main categories: SWC measures, change or adaptation in the farming practices, and use of modern inputs such as fertilizers and improved seeds. Using information from the micro-level source of information, farmers give their own perspectives on the merits and disadvantages of the adaption measures. For example, bench terraces increase farm productivity, retain manure and water, maintains soil nutrients, and appear efficient in the use of manure as it requires small quantity. They are beneficial in terms of grasses planted alongside its contour since these are in turn used as fodder for animals. But bench terraces may cause heavy landslides in case of heavy rains when these are not well constructed or maintained. The cost of maintaining bench terraces may be higher compared to their initial construction (Bizoza, 2014).

Table (7). Cost estimates of the adaptation strategies

Agricultural Adapative Strageies Estimated Unit Costs Explanatory Notes Life-span/ duration (in USD) per Ha SWC practices Irrigation and Drainage ( Marshlands) 7,500-8,500 This unit cost include all type of 25-30 years depending on infrastructure per ha maintenance quality Irrigation and Drainage (Hillside pressurized 12,000-15,000 irrigation system.) Anti-erosion practices ( Ditches, Bench terraces, 2,500-3,000 This cost includes a full land- Permanent for bench terraces if manuring, liming, trees planting, grasses, etc husbandry properly maintained Progressive terraces 200-300 Progressive terraces take up to 3-4 years for progressive terraces, about 7 years to be effective as but require more regular bench terraces maintenance Change in farming practices

Use of high yielding and resistant varieties Use of disease resistant variety 72-143 Requires proper variety 10-15 years before degeneration. evaluation and maintenance ( e.g. Cassava Variety) Growing drought resistant crops and early maturing 72-143 Example of climbing beans n/a varieties ( e.g. Climbing beans) Increase of cultivated land (Ha) 143-286 In case of mechanization n/a

Increase of cultivated land (Ha) 172-215 In case of human labor Group Settlement 43,004- 5,738 Estimate Cost to resettle one n/a household with reference to Use of fertilizers and improved seed varieties Use of organic fertilizers 143-215 2 seasons Use of inorganic fertilizers 143-172 1 season Use of traditional seeds - Farmer normally save seed from 1 season for Maize and 2 seasons previous season, no extra cost. for cassava Use of improved seeds ( Maize) 22 1 season Use of improved seeds ( Maize) 72 2 seasons for cassava Water harvesting Roof Water Harvesting Tank 573-860 8-10 years Hill side Micro-Dams 573-717 10-15 years Notes: 1 USD= 723 Frw, Source: RAB (2014).

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Uses of agro forestry trees, including fruit trees, are important as they can constitute e a source of income through sales of fuel wood, timber and stake. Some of these trees are also used for fuel wood; they serve as windbreakers and protect the soil from erosion. They are able to capture rain, to retain the soil nutrients and avoid leaching of the soil. Similarly, planting of other trees, such as eucalyptus, can also capture or reduce the weight of the drop rain, they are necessary in generating the oxygen we need; they provide timber and are mostly used for charcoal and firewood. However, both agro-forestry trees and other woodlot trees bear some negative effects especially when there is no better management to ensure that the expected competition in terms of absorbing soil nutrients between the trees and the crops is well mastered.

In spite of the merits of the above adaptation strategies, they exhibit farmer’s inability to invest due to their low per capita income of $ 644 (MINECOFIN, 2014). For a regular farmer to invest in any adaptation measure, he or she will need an estimated amount of $200 to $8500 for soil and water conservation (SWC) practices, $72 to 285 US$ for use of high yielding and resistant varieties, and 22 to 215 US$ for application of fertilizers and improved seeds. The implication of these estimates is that the government and other development partners will need to bear a major part of the cost of adaptation until all farmers become non-poor.

4.3. Model results

The analysis performed focused on a farmer’s likelihood to adopt adaptive strategies and to identify the key drivers of adoption, with much focus on gender effects. We discuss some of the key hypothesized discriminating factors between men and women mostly linked to adaptive capacity which, in turn, is intimately connected to social and economic development (UNFCCC, 2011). We rely on the results from the analysis of both macro and micro level data sets. For the macro analysis we drive the discussion of land tenure, gender of the head of the household, wealth index measured by the consumption. While the discussion of participation of CBOs, household and plot level characteristics is based on the micro-level analysis.

(1) Land Tenure Security (LTS)

Tenure security is well documented in the literature as a key determinant of climate change adaptation measures and other investment in agriculture. However, the expected effect in the adoption literature is mixed and most of the conclusions sustain that whether tenure security enables adoption of new investments in agriculture is site specific (Deininger and Feder, 2009, Bizoza, 2012). Land tenure reforms introduced in Rwanda since the 1990s have strived to integrate issues and opportunities linked to women’s empowerment, social order and equity, and equal land rights between men and women. In the context of Land Tenure Regularization (LTR), women’s empowerment should be seen beyond the equation of 푀푒푛 = 푊표푚푒푛 to include women’s ability to use, control and claim their rights over land. Thus we expected a positive relationship with the adoption of the adaptation measures. LTS is a binary factor and was estimated positive and statistically significant (at 5% level) for the adoption of bench terraces.

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But the expected effect was not observed for the erosion ditches and tree planting. Possible explanation is that heavy or long-term investments like bench terraces require tenure security. However, findings from other scholars are mixed and hence not fully pronounced on the differential impact that LTS has on the decision to adopt the adaptation measures (See Knowler and Bradshaw, 2007; Feder et al., 1985;).

(2) Gender of the Head of Household

The existing literature substantiates that household headed by females are likely to adopt less the new adaptation strategies (ODI, 2016). But there are also other authors whose position is that female headed households are likely to be adopter at a higher rate based on the assumption that they are the most involved in agriculture and hence more likely to be exposed to the adaptation strategies than men (Nhemachema and Hassan, 2007; Temesgen et al. 2009). We hypotheses males (Gender=1) with greater likelihood to adopt the adaptation measures than females headed households (Gender =0). The results suggest that male-headed households are likely to adapt less to climate change using bench terraces than female-headed households while they seem to adapt more using erosion ditches than female-headed households. These findings corroborate those obtained in the descriptive analysis (See Table 3 above). Erosion ditches require much physical energy that has men than women; it is common in rural Rwanda that they are done mostly by men. These are equivalent to the trenches once the construction is well done and combined with erosion fences (using hedge rows) they can overtime form into progressive terraces. Progressive terraces in turn form into bench terraces for a period of about 7 years (Bizoza, 2012). Most of bench terraces in Rwanda are done through various government or donor sponsored projects such as Vision 2020 Umurenge Program under the social protection program which mostly target women and young people for the sake of job creation. These findings on gender differentials are mixed and call for a more robust approach that takes care of the interactive forces between women and men beyond these representation, this is beyond this paper.

(3) Wealth Index

Another stimulating assumption in disfavor of women’s adoption to new agricultural adaptive measures is the poverty level. There is quite a huge gender based literature suggesting that women are likely to adopt less the adaptation measures because they have limited income. We specified consumption aggregate as a wealth index as proxy factor for one’s wealth to assess its effects in the decision to decide on the adaptation measures. Findings from the analysis show this positive and statistically significant (1% and 5% levels of significance) with regard to the probability of using bench terraces, erosion ditches, and tree planting; all else equal.

(4) Participation to CBOs

In 2006, the government of Rwanda issued a legal and statutory framework to support the establishment of cooperatives and to contribute to their functioning and proliferation 13 | P a g e

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(Mujawamariya et al., 2013). Farmer organizations are established to improve people’s livelihood through mutual and collective actions (Ortega et al., 2016, Bizoza, 2012). About 61% of men are members of community based organization compared to 51% of women. Through cooperative and other forms of organizations, it becomes easier to have access to technical assistance, access to inputs, and increased bargaining power of members. Findings from the analysis show that being a member of CBOs increases chances of adopting bench terraces (significant at 1% level) but not progressive terraces. This echoes the expectations since most of terraces require more of collective work and most of this is done through cooperatives.

(5). Education and Family Size.

Formal education is expected to induce adoption of agriculture adaptive strategies (e.g. Graaff et al., 2008; Diagne and Demont, 2007; Dimara and Skuras, 2003; Mbaga-Semgalawe and Folmer, 2000). Using the small-scale survey, this relationship does not hold for the case of Rwanda. This echoes Knowler and Bradshaw (2007) who also argue that the impact of education on adoption of agricultural technologies does not hold in all cases. In small-scale and traditional farming practices such as in Rwanda, effects of education on agriculture are hardly observed (Welch, 1978). It remains debatable whether a sample farmers with an average age of 46 years old with 5 years of primary education rely on the knowledge obtained back at primary school after 40 years (assuming he or she started primary education at 6 years old). Thus, farmers are likely to adopt because of the experiences they share with neighbours, training they receive, and their contacts with extension officials.

Whereas the size of the family matters especially for the progressive terraces, these are mostly cultivated by household members. The estimate was found positive and statistically significant at 5% critical level.

(6). Plot characteristics (Size and Location). Reference made to the micro-level evidence, both size and location of the plot represent some of the physical characteristics of the plot. The more a plot is at a higher elevation with a slope less than 50%, the more it is appropriate for bench terraces. The estimate is positive and significant for bench terraces but not for progressive terraces (as expected). The same applies for land size. For more land that a household has, it adds more chances for adoption of bench terraces (significant at 10% critical level). These results are consistent with our earlier finding in 2012 in the same areas, Gicumbi and Nyamagabe (Bizoza and Ortman, 2007, Bizoza, 2011).

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Table (8). Parameter estimates of MNL of adaptive strategies

Explanatory Variables Micro-Level Analysis Macro-Level Analysis

Bench terraces Progressive Terraces Bench Dicthes Tree planting Boostrap Z value t -test Boostrap Z value t -test Boostrap Z value t -test Boostrap Z value t -test Boostrap z value t-test Coeff P > [t ] Coeff P > [t ] Coeff P > [t ] Coeff P > [t ] Coeff p>[t] Socio-Economic Characteristics Years of formal education 0.03 0.84 0.402 0.01 0.37 0.714 Size of the household -0.05 -0.8 0.425 0.14 2.14** 0.033 Gender of the head of household -0.26 -0.76 0.45 0.32 0.88 0.377 -0.32 (-) 2.56** 0.010 0.26 4.29*** 0.000 -0.12 -0.59 0.554 Farm income -2.92E-07 -0.65 0.517 -8.21E-07 -1.3 0.194 Off-farm income 1.09E-06 1.31 0.191 -8.21E-08 -0.09 0.925 Consuption agreagate 4.54E-07 1.53 0.127 2.24E-07 2.83*** 0.005 4.83E-07 2.24** 0.025 Institutional Related factors COB membership 1.05 2.69*** 0.007 -0.03 -0.1 0.92 Access to early warning information on CV 0.28 1.05 0.296 0.18 0.63 0.526 Received sufficient extension services -0.15 -1.19 0.235 0.07 0.5 0.616 Access to agricultural credit -1.25 (-) 2.04** 0.042 0.64 1.46 0.145 Number of days allocated to collective action -0.12 -1.33 0.184 0.11 1.45 0.148 VUP Program 0.22 0.35 0.723 0.23 0.44 0.656 Girinka Program 0.1 0.24 0.814 0.32 1.05 0.295 Community work- Umuganda -0.37 -0.44 0.661 0.97 1.18 0.238 Plot level Characteristics Land size (ha) 0.24 1.93** 0.054 0.2 1.32 0.187 Land tenure 2.51 5.95*** 0.000 1.76 3.57*** 0.000 0.36 2.12** 0.034 -0.04 -0.59 0.523 0.029 0.11 0.915 Homestead elevation (meter) 0 3.88*** 0.000 0 (-)3.4*** 0.001 Constant -3.02 -15.44 0.000 -0.30 -4.78 0.000 -3.69 -14.73 0.000 N= 989 N = 989 N = 9050 Wald chi2(33) = 221.94 Wald chi2(33) = 245.76 Wald chi2(9) = 73.00 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Pseudo R2 = 0.1793 Pseudo R2 = 0.2258 Pseudo R2 = 0.003 Notes : Significant at 1% level (***), 5% (**), and 10% (*) Notes : Significant at 1% level (***), 5% (**), and 10% (*)

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5. Conclusions and Policy Implications

The analysis in this study focuses on the gender effects on the choice of adaptation to climate change variability. This analysis is based on the Integrated Household Living Condition Surveys (EICV_4) by the National Institute of Statistics of Rwanda (2015). We identified few key factors that determine the adoption of adaptive measures to climate change by farmers in order to assess the differential effects of gender in the adoption of climate change adaptation measures. These include bench terraces, erosion ditches, and planting of trees.

A multinomial logit (MNL) model was used to estimate the individual effect of the above factors in people’s choices of adoption of adaptation measures. Results confirm some of the hypotheses made in this paper in contribution to the debate of climate change adaptation. Male-headed households have lower odds to adopt bench terraces but with greater likelihood to adapt to climate change using erosion ditches than their female counterparts. Securities of tenure and wealth index are important factors for the adaptation of climate change in the context of gender equity. Further policy options aimed at climate change adaption need to consider people’s financial ability to invest in established adaptive measures. Controlling the effects of these two factors, gender remains important for the adoption of bench terraces and erosion ditches. However, bench terraces are still implemented at lower rates despite their effectiveness and the investment efforts by the government and other development partners since 1970s.

We are cognizant of the potential effects that the social characteristics such as education, age, and off-farm income would play in the appreciation of gender effects in the adoption of the adaptive measures. Unfortunately we did not find these two variables in the same dataset used for this analysis. However, simple descriptive estimates from other dataset show that males are more educated (74.7%) compared to females (71.7%). While the average age for family members is 24 years old for females compared to 23 years old (all family members inclusive). These factors need to be put into perspective for further analysis of gender effects in the adoption of adaptation measures in Rwanda.

The paper ends with some policy and research considerations with regard to the limitations in the empirical appreciation of the gender effects in the analysis of climate change. First of all, there is a need to ensure appropriate data is available for the analysis. Most the data available give women and men representation, but there is need to go beyond this and measure other components of gender, with focus on the constituents of gender equity and how these explain possible differentials between women and men in adaptive capacity. This implies a deeper analysis of social, economic, political, historical and institutional barriers to both women and men’s abilities to adapt to climate change.

Secondly, the comparison made between male or female-headed households for most of gender studies needs some caution as far as data collection is concerned. Household headed by male implies by de facto women counterparts supporting their husbands while those headed by

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Country Paper Presentation, GGGI_2016 females are mostly headed by widows, single mothers or divorced women. When these are compared to male headed households it is meant to compare two different entities, it is already expected that male have more advantages than female from the historical and restrictive cultural practices. A within rather than a between comparison is the best option. In addition, the experimental design would be an option for a comprehensive and adequate comparison of gender-based differential effects. Of course, this requires more resources but it is more likely to provide robust gender treatment effects.

Finally, and of equal importance, historical, institutional, and restrictive cultural practices (such as limited access to education and resources, discriminatory laws) have some explanation in the current observed gender inequality. We compare women and men who have been historically treated differently. Therefore, the extent to which one is unable to factor in these lagged-effects in the analysis constitutes another limitation for both policy and research.

References

AfDB (2015). Africa Gender Equality Index. African Development Bank Group. Abidjan Ivory Cost.

Bizoza A.R (2014). Institutions and the Adoption of Technologies: Bench Terraces in Rwanda. In: Challenges and Opportunities for Agricultural Intensification of the Humid Highland Systems of Sub-Saharan Africa. (Eds): Bernard Vanlauwe, Piet van Asten, Guy Blomme. Publisher: Springer, ISBN: 978-3-319-07661-4 (Print) 978-3-319- 07662-1 (Online). Bizoza A.R., Ortmann, G., and Lyne MC. (2007). Determinants of Potato yield in Gikongoro Province, Rwanda. The Journal of Africa Insight, Vol. 37 (1). 2007: P. 169. Bizoza, A.R. & Graaff Jde (2012). Financial cost-benefit analysis of bench terraces in Rwanda. Land Degradation & Development, 23 (2): 103-115. Bizoza, A.R. (2011). Farmers, Institutions and Land Conservation, Institutional Economic Analysis of Bench Terracing in the Highlands of Rwanda. Ph. Thesis, Wageningen University, the Netherlands Deininger,K. and Feder, G. (2009). Land registration, governance , and development : evidence and implications for policy. The World bank Research Observer, 24:233-266. Diagne, A. and Demont, M. (2007). Taking a new look at empirical models of adoption: average treatment effect estimation of adoption rates and their determinants. Agricultural Economics, 37:201-210. FAO (2011). The Role of Women in Agriculture. The Food and Agriculture Organization of the United Nations. ESA Working Paper No. 11-02, Rome, Italy.

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FAO (2015). The State of Food and Agriculture. Social protection and agriculture: breaking the cycle of rural poverty. The Food and Agriculture Organization of the United Nations. Rome, Italy. Feder, G. Just, R.E. and Zilberman, D., 1985. Adoption of Agricultural Innovation in Developing Countries: A survey. Econ. Dev. Cult. Change, 33(2): 255-298. Goytom Abraha Kahsay, Lars Gårn Hansen (2014). The Effect of Climate Change and Adaptation Policy on Agricultural Production in Eastern Africa. IFRO Working Paper 2014 / 08. Graaff, J.de. Amsalu, A., Bodnar, F. Kessler, A. and Tenge, A. (2008). Factors influencing adoption and continued use of long-term soil and water conservation measures in five developing countries. Applied Geography, 28: 271-280. Knowler, D. and Bradshaw, B. (2007). Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy, 32: 25-48.

Maddala, G.S. (1983). Limited–Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge. Mbanga-Semgalawe, Z. and Folmer, H. (2000). Household adoption behaviour of improved soil conservation: the case of the North Pare and West Usambara Mountains of Tanzania. Land Use Policy, 321-336. Muhire, F and Abutalab, A.K (2014). Relationships between Rwandan seasonal rainfall anomalies and ENSO events. Theor Appl Climatol. DOI 10.1007/s00704-014-1299-4. Mujawamariya, G., D’Haese, M., & Speelman, S. (2013). Exploring double side-selling in cooperatives, case study of four coffee cooperatives in Rwanda. Food Policy, 39, 72-83. NISR (2015). Rwanda Poverty Profile Report. National Institute of Statistics of Rwanda (NISR). Kigali, Rwanda. ODI (2016). Gender, Agriculture and Water Insecurity. ODI Insights. Oversees Development Institute, UK. Ortega, D.L. Bro, A.S., Clay C.D., Lopez M.C, Church, R.A., Bizoza, A.R. (2016). The role of cooperatives on adoption of best management practices and productivity in Rwanda’s coffee sector. Working Paper, Michigan State University, USA. REMA (2013).The assessment of economic impacts of the 2012 wet season flooding in Rwanda. Rwanda Natural Resource Management Authority, Kigali. Rwanda REMA (2015). Rwanda: State of Environment and Outlook Report. Rwanda Environnent Management Authority. Kigali, Rwanda. SEI (2009). Economics of Climate Change in Rwanda. Stockholm Environment Institute. Sweeden.Temesgen, T.D, Rashid M.H., Claudia.R., Tekie, A. , and Mohamud, Y. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, xxx (2009) xxx–xxx UNFCCC (2011). Fact sheet: Climate change science - the status of climate change science today. United Nations Framework Convention on Climate Change Welch, F., 1978. The role of investments in human capital in agriculture. In: Schultz, W.T.(eds). Distortions of Agricultural Incentives, Indiana University Press, Bloomington, London.

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Wolfram S. and David B Lobell (2010). Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5 (2010) 014010 (8pp). doi:10.1088/1748- 9326/5/1/014010. Wooldridge, J.M., 2002. Econometric analysis of cross section and panel data. Massachusetts: MIT press.

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