Agricultural Technology Adoption under Climate

Journal of African Economies, 2016, Vol. 25, number 5, 637–669 doi: 10.1093/jae/ejw005 Advance Access Publication Date: 28 April 2016 Article

Solomon Asfawa,*, Federica Di Battistab, and Leslie Lippera a Economic and Social Development, Agricultural Development Economics Division (ESA), Food and Agricultural Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy, and bUniversity of Rome Tor Vergata, Rome, Italy

*Corresponding author: Solomon Asfaw. E-mail: [email protected]

Abstract In this article, we assess the determinants of adoption of agricultural technologies under climate risk and evaluate their impact on food security using data from Niger, together with a set of novel weather variation indicators. We employ multivariate probit and instrumental variable techniques to model adoption decisions and their impact. We find that the adoption of both modern inputs (inorganic fertiliser and improved seed) and organic fertiliser is positively associated with crop productivity and crop income. The use of crop residues does not seem to correlate positively with crop productivity and could even have a negative effect. We find a strong negative association on crop productivity among households reporting that they had experienced a delayed onset of the rainy season. We also find that factors driving modern input use are different from those of crop residues and organic fertiliser. While the latter can be characterised as low-investment capital requirements, more labour requirements and longer times for results, the former includes higher investment capital requirements, fewer labour requirements and shorter times for returns. Furthermore, we find that weather variability is one of the strongest determinants of the type of practice adopted. In regions with greater rainfall and temperature variability, crop residue incorporation into soils is more widely adopted. The probability of using modern inputs and organic fertiliser is negatively and strongly correlated with variability in rainfall and temperature. Households with higher levels of wealth, education and labour are most likely to adopt modern inputs. Distance to the nearest market and extension service also plays a strong role in adoption; the greater the distance, the less likely they are to use modern inputs. The converse holds true for organic fertiliser use: the greater distance the farm household is from the market and the extension centre, the more likely it is to use organic fertiliser. Key words: weather variability, adaptation, food security, multivariate probit, instrumental variable, Niger, Africa JEL classification: Q01, Q12, Q16, Q18

© The Author 2016. Published by Oxford University Press on behalf of the Centre for the Study of African Economies. All rights reserved. For permissions, please email: [email protected]

Downloaded from http://jae.oxfordjournals.org/ at Kwame Nkrumah University of Science & Technology, Kumasi on November 11, 2016

Agricultural Technology Adoption under Climate Change in the Sahel: Micro-evidence from Niger

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Solomon Asfaw et al.

1. Introduction


There is a growing consensus in the scientific literature that the earth is warming due to anthropogenic increases in greenhouse gas emissions into the atmosphere (IPCC, 2014). There is a considerable reason to be concerned about the effects on food security. We have seen several periods of rapid food and cereal price increases following extreme climatic events in key producing regions, indicating a sensitivity of current markets to the changing climatic conditions. Studies have also documented a large decrease in crop yields resulting from extreme daytime temperatures around 30°C (IPCC, 2014). Together with temperature increases, climate change is expected to result in increasingly unpredictable and variable rainfall (both in amount and timing), changing seasonal patterns and more frequent occurrence of extreme weather events. Considering these effects, it is generally recognised that climate change has very significant implications for smallholder agriculture. Because of climate change, some cultivated areas may become unsuitable for cropping and some tropical grassland may become more and more arid. In SSA alone, projections predict a loss of 10–20 million hectares of land available for double cropping and 5–10 million hectares for triple cropping because of climate change (Fischer et al., 2005; Schmidhuber and Tubiello, 2007). At the regional level, the biggest losses in cropland due to climate change are likely to be in Africa. People exposed to the most severe climate-related hazards are often those least able to cope with the associated impacts due to their limited adaptive capacity. This in turn poses multiple threats to economic growth, wider poverty reduction and the achievement of the sustainable development goals. Climate change threatens agriculture and food production in complex ways. It influences food production directly through changes in agro-ecological conditions and indirectly by affecting the growth and the distribution of incomes, and thus the demand for agricultural production (Schmidhuber and Tubiello, 2007). According to the IPCC (2014) report, all aspects of food security are potentially hit by climate change, including food access, utilisation and price stability. To be able to cope against the negative impact of climate change, farmers need ex-ante and ex-post strategies for managing climate risks that enable them to enhance their livelihoods, e.g., increase their resilience. Therefore, the state of knowledge and experience until now implies that we need to think of context-specific adaptation strategies to manage climate risk at the farm level. Adaptation strategies may include diversification into off-farm activities as well as a range of on-farm actions. At the farm level, a wide range of strategies may contribute to adaptation, including modifying planting times and changing to varieties resistant to heat and drought (Phiri and Saka, 2008); development and adoption of new cultivars (Eckhardt et al., 2009); changing the farm portfolio of crops and livestock (Howden et al., 2007); improved soil and water management practices, including conservation agriculture (Kurukulasuriya and Rosenthal, 2003; McCarthy et al., 2011); increasing regional farm diversity (Reidsma and Ewert, 2008) and shifting to non-farm livelihood sources (Morton, 2007). Which of these actually contribute to adaptation depends on the locally specific effects weather change has and will have, as well as agro-ecological conditions and socioeconomic factors such as market development. There is a large body of literature on the theoretical and empirical impacts indicating the presence of several barriers to farmers’ ex-ante production technology choices, ranging from the lack of insurance and limited credit access to price risk, mainly focusing on the impact of production risk on overall output (Fafchamps, 1992, 1999; Chavas and Holt 1996;

Technology Adoption under Climate Change

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Just and Candler, 1985; Sadoulet and de Janvry, 1995; Kassie and Holden, 2008; Di Falco et al., 2011; Di Falco and Veronesi, 2014). Pope and Kramer (1979) considered inputs that could be both risk-increasing and risk-decreasing, and in general, the use of risk-decreasing inputs increases where producers are more risk-averse or are in more risk-prone environments, which is important in the context of weather variability. Many sustainable land management (SLM) practices such as soil and water conservation, conservation agriculture and agroforestry, are risk-decreasing, so that the increased frequency of extreme weather events should favour adoption of SLM. There are few empirical studies that explicitly evaluate the impact of weather risk on the adoption of SLM practices or other input choices (Kassie and Holden, 2008; Arslan et al. 2013; Heltberg and Tarp, 2002; Deressa and Hassan, 2010; Di Falco and Veronesi, 2014; Deschenes and Greenstone, 2007; Seo and Mendelsohn, 2008; Kurukulasuriya and Mendelsohn, 2008; Seo and Mendelsohn, 2008). Arslan et al. (2013) and Asfaw et al. (2014) provided evidence of a positive correlation between rainfall variability and the selection of SLM type practices. Kassie and Holden (2008) found that production risk deters adoption of fertiliser but has no effect on the conservation agriculture adoption decision. Heltberg and Tarp (2002) found that farmers located in regions with greater exposure to extreme weather events were less likely to engage in market transactions, implying a greater emphasis on meeting subsistence needs with own production. Aside from risk, several supply and demand side constraints have also been identified to account for the use of SLM practices, including high upfront costs but delayed benefits (Sylwester, 2004), credit and insurance market imperfections (Carter and Barrett, 2006), tenure insecurity (Pender and Gebremedhin, 2007), household endowments of physical and human capital (Pender and Fafchamps, 2006), agricultural extension and market access (Holden and Shiferaw, 2004; Teklewold et al., 2013b; Arslan et al., 2013) and limited off-farm opportunities (Pender and Gebremedhin, 2007). McCarthy et al. (2011) synthesised the recent empirical literature on factors affecting the use of SLM practices with a strong focus on SSA. Given the absence of evidence on the potential effects of climate change and the use of climate-smart farming practices in the Sahel area at large, our detailed study of Niger is important because it is one of the first studies to look at this topic carefully for a Sahelian country, which has an implication for African countries at large. In particular, this article generates empirical evidence on factors that hinder or accelerate the use of potentially riskreducing climate-smart agricultural practices (organic fertilisers, crop residues, legume intercropping, soil and water conservation practices (SWC)) that are high priorities in most African national agricultural plans. Although context-specific, these practices are often considered effective in terms of increasing resilience of agricultural systems and reducing exposure to weather shocks (McCarthy et al., 2011). We also consider two practices (improved varieties and use of inorganic fertilisers) that are aimed primarily at improving average yields, though with uncertain benefits in terms of adapting to weather variability and in reducing risk to current weather stresses. The second objective is to understand which practices have the potential to boost agricultural productivity and increase incomes under varying weather conditions. The choice of agricultural practices considered is mainly driven by the availability of data—the use of organic and inorganic fertiliser, crop residue, improved seed as well as anti-erosion measures are very well documented in the data set we are using for this article. The survey questionnaire also provides a rich section on tree plantation, but unfortunately the response rate in this section was very low, which made this set of information not usable.

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2. Country background Niger is a large landlocked country of 1.27 million square kilometres in Western Africa. It is bordered by Algeria, Benin, Burkina Faso, Chad, Libya, Mali and Nigeria. The northern part is characterised by the Sahara desert which occupies around 75% of the country (NECSD, 2006). With 60% of the population living below the poverty line in 2007 (World Bank, 2013), Niger was ranked the poorest country among the 186 nations considered by the UN for the computation of the Human Development Index (UNDP, 2013). In 2011 the country reported the life expectancy at birth to be 57 years of age, and under-five mortality rate to be 120 per 1000 live births. Although the Nigerien government has recently launched the production and export of oil, agriculture remains essential for rural households with 87% of the population relying on crop production and livestock growth for their livelihoods (Smith, 2011). About 14% of Niger’s GDP is generated by livestock production, including camels, goats, sheep and cattle. Farmers in Niger produce mainly for own consumption. While millet, sorghum and cow peas are the main subsistence crops, onions constitute the main export crop (Institute National de la Statistique, 2013). High poverty is associated with low productivity and low returns to agriculture, contributing also to high rates of food insecurity. With 99% of the cultivated land being rain-fed, agricultural production in Niger is heavily dependent on a rainy season of 3–4 months (i.e., June–September) to feed the population for 12 months (Wilby, 2008). The amount of rainfall varies from less than 100 mm per year in the north to more than 600 mm per year in the south. The start of the rainy season is variable itself and varies according to the latitude; August is the rainy month everywhere except in the far north, where the rainfall is unpredictable. Moreover rainfall varies temporally as well, in terms of the start of the season, and total amount (Wilby, 2008). In recent years Niger has suffered droughts on average once every 2 years, and in 2009 the


From methodological point of view, our contribution to the existing literature is threefold: firstly, our analysis uses a comprehensive large national representative plot-level survey with rich socio-economic information, merged with geo-referenced climatic information. This allows us to evaluate the role of biophysical (e.g., nutrient content of the soil) and weather variables in determining farmers’ input use decisions, and consequently, the impact on crop productivity and profitability. We argue that weather variability and other shifts in recent weather patterns are major determinants of farm practice choices, extending the literature which examines the effects of weather shocks (using the level of rainfall or deviation from its mean) on productivity. While acknowledging the important role of weather shocks, we pay particular attention to long-term weather variability as a proxy for expectations about future uncertainty. Secondly, we explicitly account for the possibility of farmers choosing a mix of practices (Teklewold et al., 2013a,b). In order to model simultaneous and correlated farming practice selection decisions, we used a method that takes into account the potential interdependence among different practices. Thirdly, we estimate the causal impact of practice use on productivity using instrumental variables (IV) techniques using the method by Lewbel (2012), as well as conditional recursive mixed process (CMP) estimators as proposed by Roodman (2011). This article is structured as follows. In Section 2 we provide an overview of the country background, whereas in Section 3 we describe the data used and provide some descriptive results. In Section 4 we describe the empirical methodology used. Section 5 presents the empirical results, and in Section 6 we draw conclusions and provide policy implications.


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3. Data description and descriptive statistics 3.1 Data We use two main sources of data in our analysis: recently released socio-economic data from the Niger National Survey of Household Living Conditions and Agriculture (ECVMA) and historical data on rainfall and temperature from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium Range Weather Forecasts (ECMWF), respectively. The primary source of our socio-economic data is the Niger ECVMA survey that was conducted from July to December 2011 and implemented by the Niger Institut National de la Statistique (INS) in collaboration with the World Bank. The ECVMA 2011 is designed to have national coverage, including both urban and rural areas. The target population is drawn from households in all eight regions of the country with the exception of certain strata found in Arlit (Agadez region) because of difficulties in going there, the very low population density and collective housing. The portion of the population excluded from the sample represents less than 0.4% of the total population of Niger. The sample was chosen through a random two-stage process. In the first stage a certain number of enumeration areas (known as Zones de Dénombrement or ZDs) was selected with the probability proportional to size using the 2001 General Census of Population and Housing as the base for the sample and the number of households as a measure of size. In the second stage, 12 or 18 households were selected with equal probability in each urban or rural ZD, respectively. The base for the sample will be an exhaustive listing of households that will be done before the start of the survey. The total sample size is 4,074 households drawn from 270 enumeration areas (EAs). However, for this article, we restricted the sample to rural households involved in farming activities during the rainy season. At the end of the cleaning process, our sample is composed of 5,340 plots with 1938 households. The ECVMA involves two visits, which means that each household is visited twice. The first visit takes place during the planting season and the second visit takes place during the harvest season. The household, agriculture/livestock and community/price questionnaires are administered during the first visit. During the second visit, only the household and agriculture/livestock questionnaires are administered. The household questionnaire was designed to provide information on various aspects of household welfare in Niger, such as household composition and characteristics, health, wage employment and income sources, as well as data on consumption, food security, non-farm enterprises, and durable and agricultural asset ownership, among other topics. For households that were involved in


population was affected by both droughts and floods (Smith, 2011; IRIN, 2013). The 2009 drought is reported to be among the most severe experienced by the Nigerien population. Although the main cultivated crops are well adapted to the tough climate conditions of the country, insufficient rains caused national crop production to drop by 31% compared to 2008 (IRIN, 2010). Floods also affect the Nigerien population but with more limited effects compared to droughts; nonetheless, floods have the potential to destroy harvests and help bring about illnesses like Malaria and diarrhoeal diseases (DANIDA, 2008). Extreme phenomena like droughts and floods are likely to become more frequent because of climate change, which makes the need of adaptation strategies even more critical (IPCC, 2011). Land degradation is also a big issue in Niger where only 12% of the land is suitable for cultivation (IFAD, 2009).

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1 Average of a 10-km radius buffer of decadal sum of daily values per each EA centroid. For more details on ARC2 algorithms, see http://www.cpc.ncep.noaa.gov/products/fews/AFR_CLIM/AMS_ ARC2a.pdf 2 Point extraction per each EA centre point of values of average of a 50-km radius buffer of decadal values. 3 The HWSD is based on a spatial layer with soil mapping units (SMU) linked to a Microsoft Access .mdb file storing the various parameters for the SMUs. Each SMU is a combination of different subunits, without spatial attributes but showing a different area share. For more information, see http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/


agricultural activities, data were collected on land tenure, labour and non-labour input use, and crop cultivation and production at the plot level. Data were also collected at the community level to capture determinants of system-level capacity in terms of enabling factors for adaptation, which include issues related to collective action, access to information and to infrastructure among other factors. For details on the survey instruments and data collection process, please refer to the basic information document produced (ECVMA, 2013). The ECVMA survey data also recorded geo-referenced household and EA level latitude and longitude coordinates using handheld Global Positioning System (GPS) devices, which allow for the linking of household-level data to geo-referenced weather and soil data. We extracted time series indicators such as historical rainfall and temperature at the highest resolution and the longest time period publicly available at the time of writing. Rainfall data are extracted from the Africa Rainfall Climatology version 2 (ARC2) of the National Oceanic and Atmospheric Administration’s Climate Prediction Center for each decade (i.e., 10-day intervals) covering the period of 1983–2012. ARC2 data are based on the latest estimation techniques on a daily basis and have a spatial resolution of 0.1 degrees (~10 km).1 Temperature data are surface temperature measurements at each decade for the period of 1989–2010 obtained from the ECMWF at a spatial resolution of 0.25 degrees.2 These data are then merged with the ECVMA data at the EA level (270 EAs in ECVMA) to create a set of exposure to weather variables to represent the short- and long-term variations both within and across years in rainfall and temperature. The set of variables chosen is hypothesised to affect the adoption of agricultural practices and agricultural productivity based on the agronomy, climate and economics literature. Some of the exposure variables created include rainfall during the growing season, long-term mean rainfall and the coefficient of variation in rainfall, as well as mean and maximum temperature and the coefficient of variation of maximum temperature (1983–2011). Taking the annual measure of main cropping season rainfall at each EA level, we calculate the coefficient of variation for rainfall (CV), measured as the standard deviation divided by the mean for the respective periods: 1983–2011. As the CV is scale invariant, the measure provides a comparable measure of variation for households that may have very different rainfall levels. These weather variables are constructed for the major crop calendar, which is from June to September. We also extracted EA level information on soil nutrient availability from the Harmonised World Soil Database (HWSD) to control for the effects of biophysical characteristics. The HWSD has a resolution of 30 arc-seconds and combines existing regional and national updates of soil information worldwide.3 By merging the ECVMA data with historical data on rainfall and temperature at the community level, we create a unique data set allowing for microeconomic analysis of


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weather impacts in Niger. To the best of our knowledge, there are no other studies that bring together such data from various sources to understand the linkages between weather variability and adoption of farming practices that have adaptation potential in Niger.

Given our data set, we focus on four different potentially climate-smart agricultural practices (use of crop residue, legume intercropping, SWC and use of organic fertiliser) and consider two practices that are aimed primarily at improving average yields (improved varieties and use of inorganic fertilisers). Table 1 shows the proportion of households that implement the aforementioned agricultural practices alone or in combination with their plots disaggregated by land use type. Unlike legume intercropping, the use of crop residue is more widespread in the rural areas of the country and particularly in the agro-pastoral zones of Maradi, Zinder and Diffa. Crop residue is practiced on about 40% of the plots during the cropping season analysed, whereas legume intercropping is practiced in only 6% of the plots. It is however important to point out that for farmers in Niger, crop residues are highly valuable as they are used as feed for livestock, as fuel for cooking and as thatching/craft material. SWC structures provide multiple on-farm private benefits in the form of increased and more stable yields by reducing water erosion, improving water quality and promoting the formation of natural terraces over time, in addition to providing off-farm private and public benefits (Blanco and Lal, 2008; McCarthy et al., 2011). SWC structures considered here include sand bags, half-moons, zai, tree belts, and wall and stone perimeters. Erosion problems are reported on more than 15% of the plots in the sample. The problem is particularly acute in the pastoral areas reporting 27% of the plots as affected by erosion. Despite the high rate of erosion, we can observe from Table 1 that the use of an anti-erosion measure is very low in all the land use types. Only 3% (4% in pastoral areas) of the plots have been treated using techniques aiming to offset the effects of soil degradation. The use of organic fertiliser is another major component of a sustainable agricultural system and a commonly suggested method of improving soil fertility while capturing economies of scope in crop–livestock systems. Our data show that organic fertiliser (which is composed of animal manure, compost and green manure) is used on about 33% of the sample plots. The uptake seems to also be heterogeneous across the different land use types. The use of high yielding varieties could contribute to improving food security and income for the rural population by providing higher yields (Kijima et al., 2008; Mendola, 2007; Asfaw et al., 2012a,b; Amare et al., 2012, etc.). Despite the potential productivity benefit, the proportion of plots planted with improved varieties in Niger is only about 2%. We also consider the utilisation of inorganic fertilisers, and our data show that about 11% of sample plots are treated with inorganic fertiliser, which is relatively high compared to the use of improved seeds. Looking across the different land use types, there seems to be significant differences in the use of inorganic fertilisers. Although the impact on productivity of using inorganic fertiliser and improved seed is widely documented, the benefits in terms of adapting to climate variability and/or reducing risk to current weather stresses are uncertain. Given the very low uptake of some of the practices (e.g., legume intercropping, improved varieties and SWC measures), in the subsequent section we mainly focus and organise our


3.2 Description of agricultural technologies and socio-economic variables


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Table 1: Descriptive Summary of Agricultural Technology Adoption (in Proportion) Land use types

Modern inputs (F) Inorganic fertilisers Improved seeds Conservation practices Crop residues (R) Legume intercropping Anti-erosion measures Sand bag Half moon Zai Tree belts Wall Stone perimeter Residue, organic and modern inputs (ROF) Residue and organic (RO) Residue and modern inputs (RF) Organic and modern inputs (OF) None

Agro-pastoral (N = 2,393)

Pastoral (N = 460)

Total (N = 5,340)

0.37 [0.48] 0.13 [0.34] 0.12 [0.32] 0.02 [0.13] 0.46 [0.50] 0.39 [0.49] 0.06 [0.24] 0.03 [0.18] 0.01 [0.07] 0.00 [0.06] 0.00 [0.03] 0.02 [0.13] 0.00 [0.02] 0.01 [0.09] 0.04 [0.19] 0.13 [0.33] 0.03 [0.16] 0.03 [0.18] 0.14 [0.48]

0.25 [0.44] 0.11 [0.31] 0.09 [0.29] 0.02 [0.13] 0.50 [0.50] 0.45 [0.50] 0.05 [0.21] 0.03 [0.16] 0.00 [0.03] 0.00 [0.05] 0.00 [0.06] 0.02 [0.12] 0.00 [0.03] 0.01 [0.08] 0.03 [0.16] 0.10 [0.30] 0.03 [0.17] 0.02 [0.14] 0.22 [0.49]

0.12 [0.33] 0.06 [0.24] 0.03 [0.16] 0.03 [0.18] 0.31 [0.46] 0.27 [0.44] 0.03 [0.17] 0.04 [0.19] 0.02 [0.13] 0.00 [0.07] 0.00 [0.00] 0.02 [0.15] 0.00 [0.00] 0.00 [0.05] 0.02 [0.12] 0.04 [0.19] 0.01 [0.11] 0.01 [0.12] 0.47 [0.48]

0.33 [0.47] 0.12 [0.33] 0.11 [0.31] 0.02 [0.14] 0.47 [0.50] 0.40 [0.49] 0.06 [0.23] 0.03 [0.17] 0.00 [0.07] 0.00 [0.05] 0.00 [0.04] 0.02 [0.13] 0.00 [0.02] 0.01 [0.08] 0.03 [0.18] 0.12 [0.32] 0.03 [0.16] 0.03 [0.17] 0.18 [0.49]

The numbers in brackets are standard errors.

descriptive and empirical analysis on three major inputs/techniques widely used by Nigerien farmers: organic fertilisers (O), modern inputs (use of inorganic fertiliser or improved seed) (F) and crop residues (R). We have grouped both the use of inorganic fertiliser and improved seed into one category since the level of use for improved seeds is very low.


Organic fertilisers (O)

Agricultural (N = 2,487)


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4. Empirical strategies 4.1 Modelling the adoption decision Based on the extensive literature on the choice of farming practice (including input use), we model the farming practice selection decision as the outcome of a constrained optimisation problem by rational agents (Feder et al., 1985; Foster and Rosenzweig, 2003; Suri, 2011; de Janvery et al., 2010). The most common constraints are access to information, credit and the availability of both technology and other inputs. Households are assumed to maximise their utility, subject to these constraints, and adopt a given technology if and only if the technology is available and affordable, and if at the same time the selection decision is expected to be beneficial (in terms of profits or otherwise) (de Janvery et al., 2010). Farmers are also more likely to adopt a mix of measures to deal with a multitude of production constraints than to adopt a single practice. In this context, recent empirical studies of technology adoption decisions assume that farmers consider a set of possible technologies and choose the particular technology bundle that maximises expected utility, accounting for interdependent and simultaneous adoption decisions (Dorfman, 1996; Teklewold et al., 2013a,b). In order to be able to account for this interdependency, we use a multivariate probit (MVP) technique applied to multiple plot observations to jointly analyse the factors that increase or decrease the probability of adopting each agricultural practice analysed in this article. This approach simultaneously models the influence of the set of explanatory variables on each of the practices while allowing the unobserved and unmeasured factors (error terms) to be freely correlated. One source of correlation may be due to complementarity (positive correlation) or substitutability (negative correlation) between different practices. The MVP model is characterised by a set of binary dependent variables (Aikj (t − 1) ) that equal to 1 if farmer i adopts practice j on plot k, at time t − 1, which denotes the time when adoption decisions are taken and zero otherwise, such that: ⎧ 1 if A ⁎ = δVik (t − 1) + θWc (t − 1) + ejk (t − 1) > 0 ijk Aikj (t − 1) = ⎨ , ⎩ 0 otherwise

for each j = 1, … , j , (1)

where Vikt is a vector of household, plot and community characteristics, Wct is a bundle of climatic variables characterising the cropping season at time t in community c and ejk is the error ⁎ term. In equation (1) the assumption is that the rational ith farmer has a latent variable, Gijk , which captures the observed and unobserved preferences or demand associated with the jth


Table 1 also reports the adoption of combined practices on the same plot to understand whether farmers in Niger use a mix of measures to deal with a multitude of production constraints rather than use a single practice. Of the 5,350 plots considered in the analysis, about 61% of the plots benefited from one or more farm management practices although all three practices together were applied on only 3% of the plots. Although the use of crop residues and organic fertilisers are used in most of the cases as a standalone practice, it is not uncommon to find them combined together, especially in agricultural and agro-pastoral areas. However, the use of modern inputs in combination with other inputs is quite low. The bottom line is that the proportions of use of a given practice in combination with other practices are relatively small, indicating that there are few dominant packages across the entire country. Instead, the evidence suggests that individual households are choosing packages specific to the agro-ecological and socio-economic characteristics.

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4 We have also estimated the regression by collapsing the analysis to the household level, rather than the plot level. 5 The household wealth index is constructed using principal component analysis, which uses assets and other ownerships. In this specific case the following variables have been included: number of (per-capita) rooms in the dwelling, a set of dummy variables accounting for the ownership of dwelling, mortar, bed, table, chair, fan, radio, tape/CD player, TV/VCR, sewing machine, paraffin/ kerosene/electric/gas stove, refrigerator, bicycle, car/motorcycle/minibus/lorry, beer brewing drum, sofa, coffee table, cupboard, lantern, clock, iron, computer, fixed phone line, cell phone, satellite dish, air-conditioner, washing machine, generator, solar panel, desk and a vector of dummy variables capturing access to improved outer walls, roof, floor, toilet and water source. The household agricultural implement access index is also computed using principal components analysis and covers a range of dummy variables on the ownership of hand hoe, slasher, axe, sprayer, panga knife, sickle, treadle pump, watering can, ox cart, ox plough, tractor, tractor plough, ridger, cultivator, generator, motorised pump, grain mail, chicken house, livestock kraal, poultry kraal, storage house, granary, barn and pig sty.


practice. This latent variable is assumed to be a linear combination of farmer, household, plot, climatic and community characteristics (Vkt−1 and Wct−1), that affect the use of the jth practice. Based on empirical work and economic theory, we have summarised variables hypothesised to explain the adoption decision and resulting yield increase under four major categories: (i) exposure to weather stress; (ii) biophysical sensitivity to such stress; (iii) household-level determinants of farmers’ ability to prepare and adjust to the resulting stress and, finally, (iv) system-level determinants of enabling factors for adaptation. The rationale of these sets of variables and their characteristics are described in more detail in Table 2. The first variables used in the analyses are weather variables that characterise exposure to weather-related stress. For input decisions, we use long-term historical data on rainfall patterns and temperatures to capture expected weather at the beginning of the season. Specifically, we use the coefficient of variation in rainfall (1983–2011), average rainfall shortfall (1983–2011), the coefficient of variation in temperature and number of decades in which the maximum temperature was greater than 35 degrees. The shortfall variable has been computed as the average distance between the yearly precipitations during rainy season and their long-term mean. Those years reporting a level of rainfall higher than the long-run average have not been considered for the computation of the variable. Greater riskiness, reflected in the coefficients of variation and average shortfall, is expected to increase use of risk-reducing inputs but decrease use of modern inputs. Higher maximum temperatures are also expected to increase risk-reducing inputs such as crop residue and organic fertiliser, whereas lower maximum temperatures should favour improved seeds and chemical fertiliser use. For productivity, we include actual weather realisations. Specifically, we use growing season rainfall and self-reported delay in onset of rainfall observed in the growing season. We include several plot-specific characteristics, such as soil nutrient availability constraints, plot size, types of soil on the plot and topography of the plot4. Land size can be expected to affect input use positively, as farmers with larger land sizes may find it easier to experiment with a new technology on a part of their land. A diverse set of potential household-level determinants are considered. Household wealth indicators include a wealth index5 based on durable goods ownership and housing condition, an agricultural machinery index based on agricultural implements and

Variable

Exposure to climatic stress Coefficient of variation of rainfall (1983–2011) Average rainfall shortfall (1983–2011) Long-term mean rainfall (1983–2011) Number of decades growing season max temp was over 35°C (1989–2011) Coefficient of variation of maximum temperature (1989–2011) Biophysical sensitivity Types of soil in the plot Silty soil Clay soil Rocky soil Topography of plots Plot is located on hill Plot is located on gentle slope Plot is located on steep slope Plot is located in a valley Nutrient availability constraint (1–4 scale, 5 = mainly non-soil) Total land area per household (acre) Number of plot owned by the household Plot cultivated during dry season (1 = yes) Household-level variables

Organic fertiliser

Crop residues

User

Non-user

User

Non-user

User

Non-user

0.277 2.452 382.751 49.788

0.285*** 2.224*** 365.204* 63.065***

0.295 2.022 365.332 54.851

0.275*** 2.454*** 373.325*** 61.836***

0.280 1.896 386.173 52.135

0.283 2.350*** 367.925*** 60.264***

0.28 2.65 373.42 57.96

0.022

0.022***

0.024

0.021***

0.019

0.022***

0.02

0.046 0.066 0.096

0.047 0.134*** 0.100

0.055 0.074 0.096

0.042** 0.139*** 0.100

0.072 0.106 0.102

0.043*** 0.116 0.098

0.05 0.12 0.09

0.059 0.113 0.039 0.140 2.384

0.055 0.131* 0.058*** 0.109*** 2.246***

0.039 0.147 0.067 0.149 2.440

0.067*** 0.112*** 0.044*** 0.099*** 2.193***

0.042 0.086 0.032 0.142 2.235

0.059* 0.132*** 0.056* 0.114** 2.294**

0.06 0.12 0.05 0.12 2.31

15.555 3.754 0.069

14.966 3.868* 0.133***

14.524 4.158 0.165

15.288 3.775*** 0.100***

15.18 3.72 0.12

14.577 3.797 0.115

15.439** 3.837 0.106

Modern inputs

Overall

647

Continued


Table 2: Descriptive Summary of Selected Variables by Adopter Categories


648

Table 2: Continued Variable

Crop residues

User

User

0.134 45.703 7.460 1.226 1.483 4.226 0.884 −0.113 1.011 1.948

Non-user 0.147 44.495*** 7.121*** 1.259 1.460 3.523*** 0.808*** −0.116 0.763*** 1.672**

Modern inputs Non-user

User

0.174 44.504 7.161 1.266 1.539 3.497 0.879 −0.133 0.835 1.847

0.124*** 45.054 7.255 1.239 1.423*** 3.868*** 0.801*** −0.103*** 0.836 1.695*

0.115 45.447 7.576 1.189 1.514 4.748 0.802 −0.095 1.152 1.756

Overall

Non-user 0.147** 44.756 7.166*** 1.259* 1.459 3.574*** 0.834** −0.118*** 0.788*** 1.752

0.14 44.84 7.23 1.24 1.45 3.72 0.81 −0.10 0.80 0.73

0.133

0.156**

0.106

0.175***

0.128

0.152*

0.15

41.109 23.708 2.454 34.22

38.156* 20.105*** 3.634*** 32.21***

38.349 25.437 2.827 36.30

39.419 18.560*** 3.571*** 30.67***

48.265 18.540 2.858 29.46

37.624*** 21.547** 3.355*** 33.30***

39.01 21.15 3.29 32.79

24.46 12.61

27.91 17.68**

22.70 12.59

43.60*** 24.02***

29.21 15.2

19.18*** 12.25

38.67 14.10

Note: 1US$ = 575 CFAF. *** p < 0.01, ** p < 0.05, * p < 0.1.


Female farm (1 = yes) Age of the user (years) Household size Sex ratio Dependency ratio Highest education in the household Household owns the plot Wealth Index Index on Agricultural implements Livestock size (TLU) System-level variables At least one cooperative exist in the community (1 = yes) Distance from the nearest market Distance from the Agricultural Extension Office (km) Distance to dowelling (km) Road density: length in kilometres in a 10-km radius Outcome variables Value of harvest (‘000 CFAF/acre) Net crop income (‘000 CFAF/acre)

Organic fertiliser



649

6 It is important to point out that the estimates for crop production and productivity tend to be very low as zero harvest was reported on many plots. This is mainly a consequence of the high rate of preharvest lost in the country. A high percentage of farmers have been affected by preharvest loss, and for almost all of them the loss was equal to 100% of the harvest. The main reason behind such a high loss has been reported to be drought (65% of the cases).


machinery access, and livestock size (measured in tropical livestock units (TLU)). Household size, age, gender, sex ratio and education level of the household head are also included. Family size in adult equivalent units is a potential indicator of labour supply for production, and labour bottlenecks can also be a significant constraint to the use of some farm management practices. Furthermore, land tenure status is considered as tenure security increases the likelihood that farmers adopt strategies that capture the returns from their investments in the long run (Kassie et al., 2010; Denning et al., 2009; Teklewold et al., 2013a,b). Some other studies, however, point out that formal tenure recognition, like land titles, are not needed for farmers to make investments in their land. Recognition by relevant actors may be sufficient (Bromley, 2008). Table 2 presents selected variables disaggregated by adopters’ categories. When considering system-level determinants, access to institutions and transaction costs are among the main determinants governing input choice decisions. This study proxies transaction costs and access to institutions via observable factors that explain transaction costs or mitigate transactions costs, such as geographical areas, distance-related variables and road density. By increasing travel time and transport costs, distance-related variables are expected to have a negative influence on input use decisions. By facilitating information flow or mitigating transactions costs, access to institution variables are expected to have a positive effect on the input use decision. We also analyse whether adopters and non-adopters of these three inputs/techniques are distinguishable in terms of crop productivity6 and crop net income. We have considered all major crops cultivated during the rainy season for the analysis, which include millet, rice, sorghum, peanuts, sorrel, rice, maize, sesame, cassava, okra, onion, potatoes and other crops. Crop productivity is measured in terms of value (CFAC per acre) instead of quantities (kilograms per acre) because of the difficulty of aggregating different kinds of crops that may grow on the same plot and may have different productivity levels and economic values. To compute the value of the harvest, each crop has been evaluated using average market price for the community computed from the consumption section of the community questionnaire. With respect to cost of production, we included both inputs purchased (e.g., inorganic fertiliser, casual labour, seeds, etc.) as well as opportunity cost of inputs used from home (e.g., family labour, organic fertiliser, etc.). As shown in the bottom of Table 2, differences in net crop income between users and non-users of organic fertilisers are overall statistically significant. Results also show that plots with modern inputs perform better than plots without modern inputs in terms of value of harvest and income in all land use types. However, users of crop residues tend to have lower crop productivity compared to non-users although we observe no significant difference in terms of net crop income. It is important however to point out that the results presented above are indicative only of the potential impact of use of these practices on crop productivity. Thus, in the following sections, we will carry out a rigorous empirical analysis to verify whether these differences in productivity remain unchanged after controlling for all confounding factors.


650

4.2 Modelling the effect of adoption on crop productivity and income

Yikt = α′Vikt + β′Wct + γ jAikj (t − 1) + εikt

(2)

where Yikt is the vector of outputs considered in our model (productivity and net crop j income) from plot k at time t. It is clear from equation (2) that Aikt − 1 is endogenous; farmers who select a certain practice may have systematically different characteristics from the farmers who do not. Estimating crop productivity as in this equation, however, might generate biased estimates because it assumes that agricultural practice selection (or input use) (A) is exogenously determined, while it is likely endogenous, as discussed above. Therefore, to explicitly account for multiple endogeneity problems in our structural model, we employ the CMP estimator as proposed by Roodman (2011). This approach is suitable for a large family of multi-equation systems where the dependent variable of each equation may have a different format. “Mixed process” means that different equations can have different kinds of dependent variables. “Recursive” means, however, that CMP can only fit sets of equations with clearly defined stages, not the ones with simultaneous causation. CMP is appropriate for two types of models: (i) those in which a truly recursive data-generating process is posited and (ii) those in which there is simultaneity but instruments that allow for the construction of a recursive set of equations, as in two-stage least squares, that can be used to consistently estimate structural parameters in the final stage. In the first case, CMP is a full-information maximum likelihood estimator and all estimated parameters are structural. In the latter, it is a limited-information maximum likelihood estimator, and only the final stage’s coefficients are structural, the rest are reduced-form parameters. CMP’s modelling framework therefore embraces those of the official Stata commands but goes beyond those commands in several ways. As a matter of algorithm, CMP is a seemingly unrelated regression (SUR) estimator and treats equations as independent from one another except for modelling their underlying errors as jointly normally distributed. Maximum likelihood (ML) SUR estimators, including CMP, are appropriate for an important class of simultaneous equation models in which endogenous variables appear on the right side of structural equations as well as on the left. Models of this kind for which ML SUR is nevertheless consistent must satisfy two criteria: First, they are recursive, i.e., the equations can be arranged so that the matrix of coefficients of the dependent variables in each other’s equations is triangular. As emphasised above, this means that the models have clearly defined stages though there can be more than one equation per stage. Second, dependent variables in one stage also enter subsequent stages only as observed. For instrumented regressions, CMP differs from other IV commands because it does not automatically include in the first stage exogenous regressors (included instruments) from the second stage. The major limitations of implementing this approach is computational burden and on achieving convergence, especially for a large family of multi-equations. Therefore, we restricted ourselves to a maximum of four equations.


Taking productivity impact as a key indicator, we move to an analysis of the relationship between farm practice selection and crop outcomes (i.e., crop productivity and net crop income). In this respect, the relevant estimating equation for the yield/income model is given by equation (2). The impact of adoption of the jth practice on the outcome variables is measured by the estimate of the parameter γ j .

651

Technology Adoption under Climate Change 2 2 1 Yikt = α′Vikt + β′Wct + γ 1Aik (t − 1) + γ Aik (t − 1) + εikt j Aikt − 1 = δVikt + θWc (t − 1) + e jk (t − 1) ,

for j = 1, 2, 3

(3) (4)

5. Results and discussion 5.1 Description of weather variability in Niger We provide a detailed description of the weather variables available (both objective and subjective) and a preliminary view of how they may influence their adaptation strategies. Data from NOAA presented in Figure 1 show the time pattern of average rainfalls and temperature during the rainy season in Niger by land use type. Figure 1 shows that the amount of rainfall is increasing over time with minor differences among land use types. This trend is confirmed by forecasts from NECSD (2006) which predicted rainfalls to increase in the Sahel region due to weather change. We can observe in Figure 1 very high oscillations in the rainfall pattern. This is also evident from Figure 2 that


The consistency of this method depends on the validity of instruments to identify the adoption equations, which in turn, rely on two conditions. First, the instruments must be correlated with the endogenous variables (use of agricultural practices). Second, they must not be correlated with the unobserved factors that may affect the plot’s productivity (i.e., εkt ). The choice of instruments is challenging, but given the data available we consider using the rainy season coefficient of variation of rainfall (1983–2010), average shortfall of rainfall (2006–2010) and number of decades that the average temperatures are greater than 35ºC (1983–2010) as potential instruments for the household decision to use agricultural practices during the current year. While the level of rainfall or rainfall shocks tend to be used as IV or proxy variables for income or covariate income shocks, there are limitations to this (Rosenzweig and Wolpin, 2000). As a result, there are identification issues with using the level of rainfall or rainfall shocks. For example, more rainfall is usually defined as good, i.e., the coefficient is positive. However, even controlling for a quadratic rainfall term—expected to have a negative coefficient, indicating diminishing returns to rainfall— may not be sufficient identification. If farmers form expectations about the climatic conditions of their area, we might expect that they plant crops and use farm practices that are suited to their expectations. Any deviation from this optimal cropping decision in terms of more or less rainfall may not be welfare improving. The formation of these expectations is key for production. Thus for households in rural areas, weather variation across space and time should generate corresponding variation in household response or behaviour in terms of change in farm practices that will in turn create variation in agricultural output and thus household income. Its impact on expected utility maximisation is realised mainly through production technology choices. For this reason, we focus on weather variability which, we argue, generates uncertainty about expected climatic conditions. We are quick to point out that selected IV may not be perfect, but we will try to demonstrate that the test statistics support the idea that they help to bolster our case. We assess the quality of our instruments by using an F-test of the joint significance of the excluded instruments. According to Staiger and Stock (1997), the weak instrument hypothesis will be rejected if an F-test is greater than 10. Additionally, as part of a robustness check, we also perform over identification tests of the model.

652


Figure 2: Maximum Temperature During the Rainy Growing Season.

shows the distribution of the coefficient of variation of rainfall across time. Pastoral areas experience relatively low levels of rainfall and high variability compared to the agricultural and agro-pastoral areas. Figure 3 also shows the distribution of current and long-run average rainfall, and we can observe significant differences across the different land use types. Such high temporal and spatial rainfall variability makes Nigerien farmers vulnerable, hindering the ability of national agricultural production to satisfy the increasing population’s demand for food. Average and maximum temperature trends over time also provide a good picture of the problems from weather change in Niger. As shown in Figure 1, average temperatures in the rainy season are clearly increasing over time. Figure 4 also shows the spatial distribution of the current and long-run average temperature, which indicate temporal and spatial differences and variability. Although Nigerien farmers are specialised in the cultivation of crops that are particularly resistant to high temperatures (e.g., millet and sorghum), the increase in the temperature level will eventually change farming environments in the three land use types. In particular, one of the most plausible scenarios is the expansion of the desert areas (Kandji et al., 2006). As only less than 15% of the land in Niger is arable (IFAD, 2009), a further expansion of the Sahara would further constrain the production of the country’s agricultural sector. To complement the objective weather data presented above, we also present more subjective data from the agriculture section module of the ECVMA. The module provides


Figure 1: Total Amount of Rainfall During the Rainy Growing Season.

653


Coefficient of variation of rainfall (1983-2011)

100 0

0

50

5

kdensity

kdensity

10

150

15

Coefficient of variation of temperature (1983-2011)

.2

.4

.6

.8

COV of rainfall Overall Agro-pastoral

1

.005

.01

.015

.02

.025

.03

COV of temperature Agricultural Pastoral

Overall Agro-pastoral

Agricultural Pastoral

Figure 4: Coefficient of Variation of Rainfall and Maximum Temperature During the Rainy Growing Season.

information on how households involved in agriculture activities perceive weather changes, as well as the strategies used to adapt to and mitigate the effect of weather changes. Most of the households interviewed reported changes in rainfall and temperature patterns in the 5 years preceding the interview (Table 3). Despite what we observed in Figure 1, in all land use types, the most relevant phenomena are the reduction in the amount of rainfall (likely a consequence of the drought in 2009) and the change in the distribution of rain. The general tendency for the sample households is to report less rainfall (81%), worse rain distribution (77%) and more frequent droughts (84%); this is particularly true for pastoral areas. Although agricultural areas share the same overall patterns, 38% of the households report more frequent floods compared to 19% for both agropastoral and pastoral areas. About 72% and 82% of the sample households reported respectively more of a delayed start and an early finish of the rainy season in the 5 years before the interview. Changes in temperatures also affected 65% of Nigerien households, who reported longer heat periods. Since the 3- or 4-month long Nigerien rainy season feeds the population for 12 months, changes in its timing, duration or intensity have strong implications in terms of agricultural production and food security for the country. Table 4 details the most common strategies households used to adapt to the effects of weather change. The most commonly used


Figure 3: Minimum Temperature During the Rainy Growing Season.


654

Table 3: Perception of Weather Changes Reported for the Last 5 Years by Land Use Types (in Proportion) Land use types

More rainfall Worst distribution rainfall Shorter heat period Longer heat period More frequent drought More frequent floods Delay in the start of the rainy season Rainy season comes earlier

Agro-pastoral (N = 892)

Pastoral (N = 632)

Total (N = 2,430)

0.78 [0.01] 0.15 [0.01] 0.79 [0.01] 0.16 [0.01] 0.63 [0.02] 0.77 [0.01] 0.38 [0.02] 0.71 [0.02] 0.74 [0.01]

0.77 [0.01] 0.15 [0.01] 0.74 [0.01] 0.14 [0.01] 0.65 [0.02] 0.83 [0.01] 0.19 [0.01] 0.66 [0.02] 0.84 [0.01]

0.89 [0.01] 0.07 [0.01] 0.78 [0.02] 0.1 [0.01] 0.68 [0.02] 0.95 [0.01] 0.19 [0.02] 0.82 [0.02] 0.92 [0.01]

0.81 [0.01] 0.13 [0.01] 0.77 [0.01] 0.14 [0.01] 0.65 [0.01] 0.84 [0.01] 0.26 [0.01] 0.72 [0.01] 0.82 [0.01]

The numbers in brackets are standard errors. The household sample here is larger, given that we use the entire sample available for this section and not the sample composed by households involved in farming activities only and interviewed in both waves.

strategies across all land use types are to diversify income sources (48%) especially in the pastoral areas and where weather change manifests through a change in rainfall patterns. Migration is also a common strategy used mostly in the agricultural zones of the country. Change in seeds varieties, use of anti-erosion methods and switches from livestock raising to crop production are also strategies commonly used, particularly in agricultural and agropastoral areas. Households in pastoral zones engage more in dry season agriculture and to raise fewer sheep and switch to goats when facing changes in temperatures and in rainfall patterns.

5.2 Determinants of adoption — MVP results The maximum likelihood estimates of the MVP model of the use of farm management practices are presented in Table 5. It provides the driving forces behind farmers’ decisions to use farm management strategies, where the dependent variable takes the value of 1 if the farmer uses specific practices or inputs on a given plot and 0 otherwise7. The model fits the data reasonably well the Wald test of the hypothesis that all regression coefficients in each 7 We have also collapsed the plot-level data set into household level and conducted the same analysis excluding plot-specific indicators. Results are reported in the Appendix. Overall, the plot- and household-level results are comparable with few exceptions.


Less rainfall

Agricultural (N = 906)

Change in temperatures

Change seed varieties Use methods to protect against erosion Engage in dry season agriculture Plant trees Irrigate more intensively Raise less livestock and increase crop production Raise fewer small ruminants and switch to cattle Raise fewer cattle and switch to camels Adopt techniques to regenerate grass cover favoured by livestock Raise fewer sheep and switch to goats Migration Diversify sources of revenue

Change in rainfall patterns


Pastoral (N = 632)

Total Agricultural (N = 2,430) (N = 906)


Pastoral (N = 632)

Total (N = 2,430)

0.24 [0.02] 0.24 [0.02] 0.21 [0.02] 0.16 [0.02] 0.08 [0.01] 0.21 [0.02] 0.17 [0.02] 0.07 [0.01] 0.13 [0.02] 0.16 [0.02] 0.39 [0.02] 0.47 [0.02]

0.26 [0.02] 0.28 [0.02] 0.16 [0.02] 0.14 [0.02] 0.12 [0.02] 0.26 [0.02] 0.17 [0.02] 0.09 [0.01] 0.14 [0.02] 0.18 [0.02] 0.27 [0.02] 0.48 [0.02]

0.24 [0.03] 0.18 [0.02] 0.39 [0.03] 0.18 [0.02] 0.17 [0.02] 0.26 [0.03] 0.11 [0.02] 0.1 [0.02] 0.14 [0.02] 0.32 [0.03] 0.25 [0.03] 0.49 [0.03]

0.25 [0.01] 0.24 [0.01] 0.24 [0.01] 0.16 [0.01] 0.11 [0.01] 0.24 [0.01] 0.15 [0.01] 0.08 [0.01] 0.14 [0.01] 0.21 [0.01] 0.31 [0.01] 0.48 [0.01]

0.36 [0.02] 0.31 [0.02] 0.16 [0.02] 0.12 [0.01] 0.09 [0.01] 0.26 [0.02] 0.14 [0.02] 0.06 [0.01] 0.13 [0.02] 0.18 [0.02] 0.24 [0.02] 0.47 [0.02]

0.19 [0.02] 0.17 [0.02] 0.31 [0.03] 0.15 [0.02] 0.14 [0.02] 0.21 [0.02] 0.08 [0.01] 0.12 [0.02] 0.12 [0.02] 0.34 [0.03] 0.22 [0.02] 0.54 [0.03]

0.3 [0.01] 0.26 [0.01] 0.22 [0.01] 0.14 [0.01] 0.1 [0.01] 0.24 [0.01] 0.14 [0.01] 0.08 [0.01] 0.13 [0.01] 0.22 [0.01] 0.28 [0.01] 0.48 [0.01]

0.32 [0.02] 0.27 [0.02] 0.21 [0.02] 0.15 [0.02] 0.09 [0.01] 0.25 [0.02] 0.17 [0.02] 0.07 [0.01] 0.15 [0.02] 0.19 [0.02] 0.37 [0.02] 0.44 [0.02]

The numbers in brackets are standard errors.

655

Agricultural (N = 906)


Table 4: Strategies to Adapt to and Mitigate Weather Change Effects by Land Use Types (in Proportion)



656

Table 5: MVP Estimates—Determinants of Agricultural Technology Adoption: Plot Level Analysis Crop residues

Modern inputs

Coefficient

Coefficient

Coefficient

SE

SE

−0.003

0.002

−0.004**

0.002

−0.004*

0.002

0.478***

0.136

−0.164

0.137

−0.307**

0.155

1.809***

0.255

−2.913***

0.257

−3.337***

0.335

0.165***

0.026

−0.046*

0.025

0.009

0.032

0.227*** −0.108* 0.036 −0.236***

0.087 0.064 0.064 0.083

−0.081 −0.418*** −0.115* 0.061

0.087 0.066 0.064 0.080

0.058 −0.041 0.040 −0.071

0.098 0.074 0.075 0.100

0.001 0.098 0.172*** 0.140***

0.058 0.086 0.062 0.036

−0.068 −0.187** 0.083 0.105***

0.060 0.092 0.062 0.036

−0.145* −0.254** 0.150** 0.051

0.077 0.122 0.072 0.043

−0.008 0.000 −0.092

0.029 0.012 0.064

−0.067** −0.082*** 0.143**

0.029 0.012 0.060

−0.015 −0.027* 0.377***

0.034 0.014 0.067

−0.236***

0.055

0.156***

0.053

−0.044

0.065

0.005

0.012

0.053***

0.012

−0.032**

0.015

0.015

0.013

0.027**

0.013

−0.062***

0.015

−0.053** −0.027***

0.026 0.010

−0.314*** 0.055***

0.026 0.011

−0.068** 0.019*

0.030 0.011

−0.188 −0.038*

0.144 0.020

0.556*** 0.097***

0.136 0.019

1.112*** 0.057**

SE

0.149 0.022

Continued


Exposure to climatic stress Average rainfall shortfall (1983–2011) Log (coefficient of variation of rainfall for rainy season (1983–2011)) Log (coefficient of variation of temperature (1989–2010)) Number of years growing season the maximum average temperature was over longterm maximum temperature Biophysical sensitivity Silty soil (reference: sand) Clay soil Rocky soil Topography—hill (reference: flat) Topography—gentle slope Topography—steep slope Topography—valley Nutrient availability constraint (1–4 scale, 5 = mainly nonsoil) Log (total land area) Number of plot owned Plot cultivated during dry season System-level variables At least one cooperative exist in the community Distance from the nearest market Distance from the Agricultural Extension Office Distance to dowelling Log (road density: length in metres in a 10-km radius) Household-level variables Wealth Index Index on Agricultural implements

Organic fertiliser

657

Technology Adoption under Climate Change Table 5:

Continued Crop residues Coefficient

SE

Organic fertiliser

Modern inputs

Coefficient

Coefficient

SE

−0.047 −0.056 0.018 −0.125* −0.038 0.085*** 0.051***

0.043 0.067 0.074 0.068 0.024 0.027 0.020

−0.019

0.055

2.038*** 1.173*** −0.161 0.673*** −0.069 −0.725*** 1.286*** −14.214***

0.372 0.280 0.172 0.189 0.177 0.171 0.218 1.498

*** p < 0.01, ** p < 0.05, * p < 0.1.

equation, which are jointly equal to zero is rejected. Also the likelihood ratio test of the null hypothesis that the error terms across equations are not correlated is also rejected, as reported in Table 5. We find that the estimated correlation coefficients are statistically significantly different from zero and positive in all the three pair cases, suggesting that the propensity of using a practice is conditioned by whether another practice in the subset has been used or not. Besides justifying the use of MVP in comparison to the restrictive single equation approach, the sign of the coefficients supports the notion of interdependency among the input use decision of different farm management practices, which may be attributed to complementarity or substitutability among the practices. We find that the use of crop residue, organic fertiliser and modern inputs are all complementary to each other. The positive correlation coefficient between modern inputs (inorganic fertiliser and improved seed) and organic fertiliser is the highest among all (23.9%). The positive correlation between modern inputs and the use of organic fertiliser indicates that given the very low soil fertility of most farmland in Niger currently, low-cost fertility-improving inputs are still complements and not yet substitutes. The


Livestock size (TLU) −0.052 0.035 0.068* 0.035 Female farm 0.093* 0.053 −0.088 0.055 Log (age of the user) 0.096 0.061 0.070 0.061 Log (household size) 0.021 0.057 0.036 0.057 Sex ratio 0.048** 0.019 −0.012 0.020 Dependency ratio −0.003 0.023 0.001 0.023 Log (highest education in the −0.007 0.017 0.050*** 0.017 household) Household owns the plot 0.299*** 0.049 0.279*** 0.050 Region fixed effect (reference: Niamey) Agadez −0.135 0.313 2.116*** 0.317 Diffa 1.807*** 0.233 1.405*** 0.226 Dosso −0.200 0.175 −0.162 0.161 Maradi 1.211*** 0.182 1.083*** 0.170 Tahoua −0.224 0.178 0.775*** 0.161 Tilliberi −0.049 0.172 −0.522*** 0.160 Zinder 1.483*** 0.199 1.522*** 0.188 Constant −9.994*** 1.153 −12.975*** 1.168 /atrho21 0.094*** 0.024 /atrho31 0.099*** 0.029 /atrho32 0.239*** 0.029 Likelihood ratio test of ρ21 = ρ31 = ρ32 = 0: Χ2(3) = 95.4929 p > Χ2 = 0.0000 Number of observation 5340 Log-Likelihood −8613.0604 2025.66 Wald Χ2(114)

SE

658


8 We have not included the long-run mean rainfall and temperature due to collinearity problems.


use of multiple fertility-enhancing inputs also indicates that for many households, different constraints are binding on the different fertility-enhancing inputs. The MVP results reported in Table 5 also show that decisions to use different farm management practices are quite distinct and, to a larger extent, the factors governing the decision of each of them are also different suggesting the heterogeneity in the use of farm management practices. Results show the importance of climatic variables, i.e., exposure, in explaining the probability of farm households’ decision to use different agricultural practices. We find that greater variability in rainfall and maximum temperature during the growing season increases the use of risk-reducing practices. For instance, in regions with greater variability in rainfall and temperature, more crop residue measures are used. However, the probability of using modern inputs and organic fertiliser is negatively correlated with the variability in rainfall and temperature. Weather shocks as represented by average shortage of rainfall negatively affect the propensity to use crop residue and organic fertiliser, although for crop residues this result is not statistically significant. We also find that in communities where temperature is higher (i.e., the number of decades the average maximum temperature was over 35°C), farm households are more likely to use crop residues but less so organic fertilisers.8 Our results are consistent with the findings of Kassie et al. (2010) and Teklewold et al. (2013a), who found that yield enhancing technologies like improved seeds and inorganic fertiliser provide a higher crop return in wetter areas than in drier areas. Our findings are also consistent with Arslan et al. (2013), who found a positive relationship between the use of conservation agriculture and weather variability. Overall, our findings suggest that farmers are responding to weather patterns in terms of their adaptation strategies, but the responses are heterogeneous depending on the practices and the type of climatic variable considered, and that weather variability should thus be an integral part of promotion activities. Biophysical plot characteristics are also found to be important determinants of most of the practices. Total land holdings and number of plots owned are negatively correlated with the use of organic fertiliser and modern inputs. As expected, the topography of the plots and the types of soils in the plots also play a crucial role in explaining the input use decision. Soil characteristics mainly influence the use of crop residues and organic fertilisers, but most of the coefficients are not significant for modern inputs use. The use of crop residues is more pronounced on silty soil and less on clay soil, whereas organic fertilisers are less often used in clay and rocky soil compared to sand soil. The propensity of using crop residue is also higher in plots located in valley (in an area of cultivable land between the slopes of a hill) and less on plots located on a hill compared to plots located on flat land. Use of modern inputs is also lower on gentle and steep slopes but higher in valleys, in reference to flat slope land types. We also find that farm households with less fertile soils or high nutrient availability constraints implement crop residue and organic fertiliser more often. Overall, our results about the role of soil quality are in line with the findings of Teklewold et al. (2013a). At the system level, results show the key roles played by rural institutions and transaction costs, largely confirming the existing literature (Asfaw et al., 2014; Arslan et al., 2013 and Knowler and Bradshaw, 2007). The greater the distance to the nearest agricultural extension officer (AEO), the higher the incentive to use practices requiring less initial capital and less


659

5.3 Impact of technology adoption on crop productivity and income Table 6 reports results for OLS, CMP estimators and IV estimation using heteroskedasticitybased instruments (with additional instruments constructed using Lewbel, 2012 method) for the impact of input use on crop productivity. All estimates are reported accounting for cluster heteroskedasticity standard error at the household level. The simplest approach to investigate the effect of input use consists of estimating an OLS model of crop productivity/net crop income estimates without controlling for any potential endogeneity problems and with a dummy variable equal to 1 if the farmer decided to use the practices on a given plot, 0 if otherwise (Table 6). The OLS results would lead us to conclude that there are significant


skills (i.e., crop residues and organic fertilisers); the opposite holds true for the use of modern inputs. In this case the assistance provided by the AEO in terms of training and information dissemination is crucial for the use of improved seeds and/or inorganic fertilisers. The distance between the dwelling and the plot is also a common element negatively influencing the input choice (i.e., the longer the distance, the higher the transportation costs, the lower the incentive to adopt a technology), which is consistent with the other findings (e.g., Teklewold et al. 2013a). Availability of road infrastructure as proxied by the length of road density in a 10-km radius is positively correlated with the use of organic fertiliser and modern inputs but is negatively correlated with the use of crop residues. Farm households residing at long distances from the nearest periodic or permanent market tend to use more organic fertiliser but those who reside nearby markets tend to use more of modern inputs. Results for wealth indices such as the wealth index, the agricultural implement index and livestock ownership are also in line with expectations and with the existing literature. Wealthier households use practices that require more initial capital both in terms of specific agricultural assets and financial capital. As expected, livestock ownership is positively correlated with the use of organic fertilisers. The level of household wealth measured by the wealth index and the index of agricultural inputs is negatively associated with the use of crop residues, confirming the idea that this practice, requiring a minimal initial investment, is carried out mostly by less wealthy households. However, the level of household wealth measured by the wealth index and index of agricultural inputs is positively correlated with the use of organic fertiliser and modern inputs. We find that farm households who own the land are more likely to use crop residue and organic fertiliser while the effect is not significant for modern inputs. Our results are consistent with a number of studies that have demonstrated that the security of land ownership has substantial effect on the agricultural performance of farmers (e.g., Denning et al., 2009; Teklewold et al., 2013a; Asfaw et al., 2014). To the extent that ownership is associated with greater tenure security than with rental agreements, particularly in the longer term, better tenure security increases the likelihood that farmers use strategies that will capture the returns from their investments in the long run. Household demographics to some extent also play a significant role in explaining the household input use decision. The positive coefficient associated with education for the use of organic and modern inputs confirms its key role already pointed out in much of the literature (e.g., Teklewold et al., 2013a). Moreover, smaller-sized households (or with limited access to labour) and a higher dependency ratio find the use of modern inputs as laboursaving techniques, particularly attractive. Crop residue is also more often used by females compared to male plot users (see Table 5 for details).

660

Table 6: Effect on Productivity and Net Crop Income Log (value of harvest (CFAF/acre)) OLS

CMP

IVREG2H

OLS

CMP

IVREG2H

0.381*** 0.718*** 0.119 0.635*** −0.474*** −0.730*** −0.388** −0.239 −0.012 −0.017 0.027 0.06 −0.288 −0.646*** 0.526** −0.213 −0.424* −0.019 −0.238** 0.687*** −0.810*** −0.06 0.143 0.318** 0.096*** −0.007

0.143*** 0.458*** −0.456** 0.837** −0.421*** −0.462** −0.296 −0.211 0.083 −0.044 −0.039 0.499* −0.195 −0.530** 0.067 −0.045 −0.025 0.257 −0.454*** 1.043*** −0.733*** −0.019 −0.107 −0.152 0.056 0.017

0.103*** 0.446*** −0.314* 1.579*** −0.506*** −0.672*** −0.421** −0.212 0.002 −0.033 0.003 0.079 −0.205 −0.604*** 0.504** −0.151 −0.349 −0.035 −0.232** 0.654*** −0.773*** −0.044 0.042 0.294* 0.086** −0.004

0.228 0.892*** 0.220 3.061*** −0.023 −1.351*** −1.064*** 1.269*** 0.239** −0.608*** 0.080 −0.024 −0.555 −2.127* 0.824 0.036 −0.500 0.439 −0.201 1.261*** −2.097*** 0.012 0.117 0.298 0.039 0.119

0.026 0.112 −0.295*** 0.001 −0.010* 0.004 0.008 0.000 0.011** −0.005 −0.003 0.042** 0.012 0.005 −0.026 0.005 0.024* 0.026** 0.013 0.035** −0.028*** 0.008*** −0.013 −0.033 −0.004 0.003

0.040** −0.018 0.003 0.043 −0.004 −0.012** 0.003 0.001 0.006* −0.005 0.000 0.014 0.004 −0.004 0.003 −0.005 0.000 0.008 0.000 0.015** −0.033*** 0.005*** −0.001 −0.003 −0.001 0.003


Modern inputs Organic fertiliser Crop residues Rainfall during the growing season (mm) Household reports delayed onset of the rainy season in 2011 Female farm Log (age) Log (household size) Sex ratio Dependency ratio Log (highest education in the hh) Silty soil (reference: sandy soil) Clay soil Rocky soil Topography—hill (reference: flat) Topography—gentle slope Topography—steep slope Topography—valley Nutrient availability constraint (1–4 scale, 5 = mainly non-soil) The household owns the plot Log (total land area per household) Number of plot owned by the household Plot cultivated during the dry season At least one cooperative exists in the community Distance from the nearest permanent or periodic market Distance from the nearest Agricultural Extension Office

Log (net crop income (CFAF/acre))


−0.203*** 0.052* 0.32 0.173** 0.298*** YES 0.642

−0.131 0.098*** −0.563 0.048 0.228* YES 4.327*

−0.128 0.064** −0.088 0.140* 0.288*** YES 0.657

−0.503*** −0.279*** −4.067*** 0.150 −0.389* YES −6.972

−0.000 −0.006 −0.044 0.010** 0.004 YES 13.492***

−0.006 −0.003 −0.002 −0.002 0.009** YES 13.278***

N R2 Log-Likelihood Hansen J statistic1 Under identification test1 Weak identification test1

5,340 0.153 −15,041.4

5,340

5,340 0.137 −15,094.7 1.195 294.574*** 13.770***

5,340 0.14 655.7

5,340

5,340 0.11 647.47 0.545 310.53*** 13.96***

−23,537.8 1.091 22.440** 10.495**

15.14** 9.94*


Distance to dwelling Log (road density: length in metres in a 10-km radius) Wealth Index Index on Agricultural implements Livestock size (TLU) Regional fixed effect Constant

*** p < 0.01, ** p < 0.05, * p < 0.1. Tests for instruments validity for the CMP have been obtained by ivreg2h.

1

661


662


9 “The endogeneity test implemented […] is defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where the suspect regressor(s) are treated as endogenous, and one for the equation with the larger set of instruments, where the suspect regressors are treated as exogenous” (Baum, et al., 2007).


observable differences in terms of agricultural outputs between users and non-users. Crop productivity and net crop income is greater for users of modern inputs and organic fertilisers compared to the non-users (the coefficient for crop residues is also positive but not significant). This approach, however, assumes that the uses of these agricultural practices are exogenously determined in the production function, while the endogeneity test9 determines the three dummies to be endogenous. Therefore, the estimation via OLS would yield biased and inconsistent estimates. The impact estimates presented further on use CMP techniques to account for this problem and IV techniques boosted by the method by Lewbel (2012) (ivreg2h) to correct for weak instruments and heteroskedasticity problems. Before turning to the causal effects of adoption on crop productivity, we briefly discuss the quality of the selection instruments used. To probe the validity of our selection instruments, we looked at two major tests: the weak identification test and over identification tests. The test results support the choice of the instruments, as do the F-test values for all of the specifications (bottom of Table 6). The F-statistic of joint significance of the excluded instruments is greater than 10, thus passing the test for weak instruments. The null hypothesis in the case of the over identification test is that the selection instruments are not correlated with the yield error term, and we fail to reject the null in all the cases. Despite the strong performance of the selected instruments, we would like to acknowledge that no IV strategy is perfect and thus our identification strategy cannot truly distinguish causations from correlation in spite of our best efforts. Therefore, results presented below should be considered as suggestive, though important. As we can see from Table 6, results for the impact on crop productivity are quite consistent across different estimation strategies. As expected, results show that, on average, use of modern inputs is positively correlated with crop productivity. The use of organic fertilisers also positively associated with crop productivity, and this result is consistent for both estimation strategies. However, the use of crop residues does not seem to increase crop productivity— the coefficient is not statistically significant in the case of OLS estimator, but ivreg2h and CMP estimators report negative and significant correlation of crop residue use with crop productivity. One possible explanation for the negative correlation of crop residue is that the yield benefit of use of SLM practices such as crop residues often accrues slowly over time compared to other agricultural practices, such as modern inputs, which tend to have shortterm returns. The evidence is just one piece of the puzzle, and the finding would have to be confirmed through other types of studies. We have also not estimated the impact on reducing yield variability in the face of variable weather conditions, so the results should be interpreted with these caveats in mind. Our data also did not fully capture when crop residue incorporation started and the frequency with which it was used, so perhaps, this can also provide an important lesson for design of future surveys. For crop net income, the results are quite similar with crop productivity with the exception of organic fertiliser. The use of modern inputs is positively correlated with net crop income although the coefficient is significant in ivreg2h estimators. The use of crop residues does seem to have a negative effect on crop net income in the case of CMP estimator.


663

6. Conclusions and policy recommendations Climate change alters the agriculture production conditions and food security, increasing the frequency and depth of risk to agricultural production and incomes in developing countries at large and in African countries in particular. Policy-makers need assistance in identifying risk management options in the agricultural sector that allow them to effectively respond to the climate risks they face, while maintaining and enhancing agricultural policy objectives. With this study, we aimed not only to understand farmers’ incentives and conditioning factors that hinder or accelerate use of farming practices with adaptation benefits but also to provide rigorous evidence of its impact on crop productivity and crop net income. Results clearly indicate that while the use of modern inputs and organic fertilisers are positively correlated with crop productivity, the effect of using crop residues is not statistically significant, which might be a consequence of the fact that the yield benefits of some


As described in the earlier section, agriculture in Niger is largely rain-fed. Thus, crop productivity in Nigerien households is strongly correlated with weather variables. This is perfectly reflected in our results: the amount of rainfall during the growing season, as expected, positively influences crop productivity of the plot. Furthermore, a late onset of the rainy season negatively and significantly affects value of production as well as net crop income, consistent with the findings of Verdin et al. (1999). Soil characteristics, as expected, also explain plot productivity. Availability of soil nutrients also increases productivity and income but it’s important to keep in mind the course nature of the soil nutrients measures in interpreting this result. Being an essential factor of production, access to land (both in terms of tenure’s stability and in terms of number of plots owned) is a key factor in explaining the differentials in productivity. Land tenure status also explains variation in plot productivity. Secure land tenure implies better plot management in Niger, which in turn positively influences agricultural productivity and net crop income confirming the results from Clay et al. (1998). Results also show an inverse relation between land size and crop productivity, which is consistent with many other findings in the literature. The coefficient of land size is negative and highly significant in all the specifications for both crop productivity and crop net income. One explanation of inverse farm size productivity is related to errors in land measurements. However, contrary to earlier conjectures, Carletto et al. (2013) found that the empirical validity of the inverse relationship hypothesis is strengthened, not weakened, by the availability of better measures of land size collected using GPS devices in Uganda. Given that we also used plot measurements collected using GPS devices, our findings are consistent with Carletto et al. (2013). Gender is a significant variable explaining productivity differences. Crop productivity and crop net income from plots managed by women tends to be significantly lower than their male-managed counterparts. Our findings are consistent with many studies that show that productivity on plots managed by women is lower than on those managed by men, which is often attributed to differences in access to productive resources (Quisumbing et al., 2001; Peterman et al., 2011). Moreover, crop productivity decreases with the age of the farmer, which is in line with the findings from Tauer (1995) who provides evidence of the lower productivity of older farmers. Households with a larger stock of farming-related assets (both tools and livestock, the latter measured in TLU) are significantly more productive (see Table 6 for detailed results).

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SLM practices such as crop residues often accrue slowly over time compared to other agricultural practices, like modern inputs, which in turn tend to have short-term returns. It is however important to note that we have not addressed in this article the impact of the use of these practices on reducing yield (or income) variability in the face of variable weather conditions. Increasing productivity is just one of the reasons to use these technologies, but reducing downside loss can be another reason. Therefore, the results should be interpreted with this caveat in mind. In order to understand why adoption rates of practices that are effective adaptation strategies are low, we performed an MVP analysis. What emerges clearly from the analysis is that farmers’ decision to adopt practices that could provide them with adaptation benefits varies with climatic stress and agro-ecological conditions, biophysical sensitivity to such disruptions and the nature of the household and system-level capacity required. We find that use of crop residue is higher in areas of greater weather variability, as represented by the coefficient of variation of rainfall and temperature as well as rainfall shocks measured by average rainfall shortfall increases. These practices have low investments but higher labour requirements, and involve longer periods to realise benefits. In contrast, modern input use is higher in areas of lower climatic variability, and its adoption is affected by proximity to extension services and markets. It is however important to point out that many of the determinants of adoption of crop residues and organic fertiliser have different signs (e.g., weather variability). This defies the notion that organic fertiliser and crop residue as similar technologies (risk-reducing inputs) in contrary with the use of modern inputs. Overall, these analyses generate three important findings relevant for the emerging body of literature: (i) weather change related effects are important determinants of the practices farmers select, but these effects are heterogeneous across agro-ecologies and thus so are the practices selected; (ii) farm practice selection is an important means of adaptation that farmers are already practicing as demonstrated by the effects across a range of practices, exposure and sensitivity to weather change and (iii) both household and community-level factors are important determinants of adoption of agricultural technology. Some key policy implications emerge from this study for policy makers and institutions in Africa at large and in the Shale in particular. First of all, given the strong role of extension in facilitating adoption of practices that are risk reducing, the support provided by extension organisations should be strengthened and improved where already in place and expanded to include a larger share of farmers. As some of the practices analysed require high upfront costs which often constitute a severe constraint, access to credit should be guaranteed in order to make adoption of farming practices affordable for even the poorest of farmers. Most importantly, the results in this article provide very strong arguments for better targeting agricultural practices to respond to weather risk exposure and sensitivity, and then building household and system-level capacity to support different interventions. Overall, this article argues for much greater awareness of heterogeneity in the exposure to weather risk and sensitivity and the implications for which agricultural practices will lead to improved productivity, as well as the types of interventions and to whom they should be targeted.


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Appendix Table A1: MVP Estimates—Determinants of Agricultural Technology Adoption—Householdlevel Analysis

Exposure to climatic stress Average rainfall shortfall (1983–2011) Log (coefficient of variation of rainfall for rainy season (1983–2011)) Log (coefficient of variation of temperature (1989–2010)) Number of years growing season average maximum temperature was over long-term maximum temperature Biophysical sensitivity Log (total land area) Number of plot owned Plot cultivated during dry season

Crop residues

Organic fertiliser

Modern inputs

Coefficient

Coefficient

Coefficient

SE

SE

0.009

0.008 −0.038*** 0.008 −0.009

0.907**

0.402

SE

0.010

2.370*** 0.630 −1.087*

0.585

−1.259*** 0.765 −2.004*** 0.980 −2.933*** 1.018 0.063**

−0.011 0.056** −0.184

0.021 −0.014*** 0.002 −0.004*** 0.002

0.050 0.024 0.118

0.030 0.052 0.132*** 0.027 0.260** 0.124

0.038 0.058 0.070** 0.027 0.428*** 0.125

Continued


Suri, T. (2011) ‘Selection and comparative advantage in technology adoption’, Economtrica, 79 (10): 159–209. Sylwester K. (2004) ‘Simple Model of Resource Degradation and Agricultural Productivity in a Subsistence Economy’, Review of Development Economics, 8: 128–40. Tauer L. (1995) ‘Age and Farmer Productivity’, Review of Agricultural Economics, 17 (1): 63–9. Teklewold H., Kassie M., Shiferaw B. (2013a) ‘Adoption of Multiple Sustainable Agricultural Practices in Rural Ethiopia’, Journal of Agricultural Economics, 64 (3): 597–623. Teklewold H., Kassie M., Shiferaw B., Kohlin G. (2013b) ‘Cropping System Diversification, Conservation Tillage and Modern Seed Adoption in Ethiopia: Impacts on Household Income, Agrochemical Use and Demand for Labor’, Ecological Economics, 93: 85–93. UNDP. (2013) ‘The Rise of the South: Human Progress in a Diverse World’, The 2013 Human Development Report, UNDP, New York. Verdin J., Rowland J., Klaver R., Reed B., French V., Lee F. (1999) ‘A comparison of methods for estimating start-of-season from operational remote sensing products: first results’. Proceedings of the Pecora Symposium. Denver CO.: American Society for Photogrammetry and Remote Sensing. Wilby. (2008) ‘Constructing climate change scenarios of urban heat island intensity and air quality’, Environment and Planning B: Planning and Design, 35: 902–19. World Bank. (2013) Data-Niger. Retrieved September 11, 2013, from http://data.worldbank.org/ country/niger

669

Technology Adoption under Climate Change Table A1:

Continued Crop residues

Organic fertiliser

Modern inputs

Coefficient

Coefficient

Coefficient

SE

SE

*** p < 0.01, ** p < 0.05, * p < 0.1.

0.116

0.107

−0.047* 0.025 −0.091*** 0.025 −0.045 0.016*

0.067 0.009

1.072*** 0.065* −0.099 −0.035 0.088 −0.119 −0.040 0.050 0.039 −0.145

0.355 0.038 0.069 0.108 0.121 0.118 0.037 0.044 0.033 0.114

1.180*** 0.630** −0.094 −0.862*** −0.434 0.492*** 0.324

0.395 0.318 0.234 0.203 0.273 0.180 0.719


System-level variables At least one cooperative exist in −0.321*** 0.096 −0.123 0.097 the community Distance from the nearest market −0.002 0.021 0.058*** 0.021 Distance from the Agricultural 0.045** 0.021 0.012 0.022 Extension Office Distance to dowelling −0.098* 0.057 −0.268*** 0.059 Log (road density: length in −0.012 0.017 0.061*** 0.020 metres in a 10-km radius) Household-level variables Wealth Index −0.007 0.330 0.235* 0.155 Index on Agricultural implements −0.011 0.038 0.191*** 0.044 Livestock size (TLU) −0.009 0.057 0.147** 0.061 Female farm 0.033 0.093 −0.073 0.096 Log (age of the user) 0.074 0.104 0.160 0.107 Log (household size) −0.046 0.103 −0.150 0.106 Sex ratio 0.027 0.032 −0.047 0.034 Dependency ratio 0.032 0.039 0.074* 0.040 Log (highest education in the hh) 0.012 0.030 0.077** 0.031 Household owns the plot 0.100 0.102 0.100 0.106 Region fixed effect (reference: Niamey) Agadez −0.678** 0.345 1.379*** 0.385 Diffa 0.889*** 0.240 0.606** 0.257 Dosso −0.998*** 0.183 −0.550*** 0.205 Tahoua −1.539*** 0.166 −0.089 0.159 Tilliberi −0.909*** 0.221 −0.849*** 0.239 Zinder 0.271* 0.151 0.339** 0.161 Constant 0.361 0.578 0.604 0.629 /atrho21 0.108** 0.044 /atrho31 0.021 0.048 /atrho32 0.241*** 0.051 Likelihood ratio test of ρ21 = ρ31 = ρ32 = 0: Χ2(3) = 29.594 p > Χ2 = 0.0000 Number of observations 2,430 Log-Likelihood −2,846.179 1,038.320 Wald Χ2(108)

SE