Rice productivity in Bangladesh: What are the benefits of irrigation?

Land Use Policy 48 (2015) 1–12

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Rice productivity in Bangladesh: What are the benefits of irrigation? Andrew Reid Bell c,∗ , Elizabeth Bryan a , Claudia Ringler a , Akhter Ahmed b a

International Food Policy Research Institute, 2033 K St., Washington DC 20006, USA International Food Policy Research Institute – Dhaka, House 10A, Road 35, Gulshan 2, Dhaka 1212, Bangladesh c Department of Environmental Studies, New York University, 285 Mercer St., New York, NY, 10003, USA b

a r t i c l e

i n f o

Article history: Received 7 March 2014 Received in revised form 2 May 2015 Accepted 16 May 2015 Keywords: Bangladesh Irrigation Groundwater Rice production

a b s t r a c t Green Revolution technologies transformed Bangladesh’s agricultural system through the introduction of high-yielding rice and wheat varieties, chemical fertilizers and pesticides, and the expansion of tubewellirrigated area, enabling crop production during the dry season. However, serious challenges continue to plague the agriculture sector, including scarcity of land due to high population density, unbalanced use of fertilizers and pesticides, and great variation in water supply across seasons – from drought to stagnant flood conditions. Further expansion of irrigated area – including through the continued development and improvement of surface water systems – is being eyed by Bangladesh’s Ministry of Agriculture to address many of the remaining challenges facing the country. However, such expansion is not without risks or consequences, and a careful analysis of who benefits from irrigation, and how, must guide development priorities. We examine plot-level data for rice production during Bangladesh’s three rice seasons – aus, boro, and aman – across a nationally-representative household survey in Bangladesh. While rainfall is the most important determinant of rice yield during aus and chemical inputs are most important during aman, access to irrigation has the greatest influence on boro rice yield during the dry season, particularly for the coastal south. The government of Bangladesh is planning massive investments in the southern region for the improved provision of surface water irrigation. The expected decline in groundwater, coupled with our econometric findings, suggests that expanding boro production in the south may not be a good strategy to promote, for the region. Whether brackish shrimp aquaculture can provide an equitable and sustainable livelihood alternative should continue to be a focus of research. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Agricultural production in Bangladesh has undergone dramatic changes over the past several decades. Green Revolution technologies transformed the agricultural system in the country through the introduction of high-yielding rice and wheat varieties, chemical fertilizers and pesticides, and the expansion of tubewell-irrigated area, which enabled crop production during the dry season (e.g., Hossain, 1988; Sen et al., 2004; Timmer, 2005). As a result, yields of key staples (namely rice but also wheat) have increased, as has food availability. However, serious challenges continue to plague the agricultural sector. These include (i) scarcity of land due to high population density, (ii) unbalanced use of fertilizers and pesticides, (iii) great variation in water supply across seasons from drought to

∗ Corresponding author. Tel.: +1 2028624644. E-mail addresses: [email protected] (A.R. Bell), [email protected] (E. Bryan), [email protected] (C. Ringler), [email protected] (A. Ahmed). http://dx.doi.org/10.1016/j.landusepol.2015.05.019 0264-8377/© 2015 Elsevier Ltd. All rights reserved.

stagnant flood conditions, (iv) resource degradation due to overapplication of chemicals and intensive year-round cultivation of rice, and (v) climate change and related extreme climatic events. The expansion of irrigated areas contributed to the gains in production during the Green Revolution, and now further expansion of irrigated area – including through the continued development and improvement of surface water systems – is being considered by Bangladesh’s Ministry of Agriculture to address several of the challenges facing the country (Asaduzzaman et al., 2010). However, such expansion is not without risks or consequences, including reduced water availability and quality for other uses (Alauddin and Quiggin, 2008), waterlogging, and exacerbation of income inequality; all while poor farmers face disproportionate barriers to adoption of irrigation technologies (Smith, 2004). While there may yet be potential for irrigation development to improve livelihoods across Bangladesh, a careful analysis of who benefits from irrigation, and how, must guide development priorities. Bangladesh’s agricultural sector is the most intensive in South Asia, with the greatest expansion in irrigated area in recent decades

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among South Asian nations (Alauddin and Quiggin, 2008). Importantly, Bangladesh has seen the greatest shift from surface-water to groundwater irrigation within the region – a 63% growth in irrigated area served by groundwater in the years leading up to the millennium, compared with 26% for the region as a whole (Shah et al., 2006). South Asia in turn stands far apart from its neighbors in Southeast and East Asia in terms of the fraction of renewable water resources consumed for agriculture (27% compared to 6% for Southern and Eastern Asia as a whole), and more specifically for renewable groundwater resources (49% compared to 7% for Southern and Eastern Asia as a whole) (Siebert et al., 2010). Thus, research on navigating irrigation development challenges where they are most acute (as in Bangladesh) may provide a bellwether for addressing development, climate, or other stressors faced across the region in the decades that come. This paper examines the relative contribution of agricultural inputs (including access to irrigation) to rice productivity across Bangladesh by cropping season, using multivariate regression analysis. Particular attention is paid to comparing outcomes for rich and poor farmers, as well as outcomes for farmers residing in northern (Dhaka, Rajshahi, Rangpur, and Sylhet) and southern (Barisal, Chittagong, and Khulna) divisions of the country (Fig. 1), due to differences in agro-ecological conditions and production systems. The data used for the analysis were collected as part of the Bangladesh Integrated Household Survey (BIHS), which was implemented from late 2011 to early 2012. The next section provides background on agricultural production in the northern and southern parts of the country and on the role of irrigation. Section 3 describes the data and methods used for the analysis and Section 4 presents the results of our analyses. Section 5 discusses the implications of the findings for Bangladesh’s farmers and policymakers. 2. Agricultural production in Bangladesh and the role of irrigation Agriculture remains a key economic sector in Bangladesh, accounting for 18 percent of gross domestic product (GDP) in 2011 (World Bank, 2012) and more than half of total employment as recently as the early 2000s (World Bank, 2012). Intensification of agricultural production has led to major changes in cropping patterns. In particular, the area under irrigated, short-duration, high-yield varieties (HYV) of staple cereals, such as rice and wheat, increased, while production of other non-rice crops such as pulses and oilseeds, as well as fisheries, declined (Husain et al., 2001; Rahman, 2010). Changing cropping patterns have been accompanied by an increase in intensive cultivation of rice over the entire year and an accompanying rise in input use (fertilizer, pesticide) intensity (Hossain and Kashem, 1997; Rahman, 2010). Rice production now covers 77% of total agricultural lands and rice is grown in up to three growing seasons: aman, boro, and aus (Ahmed et al., 2013). Aman grows during the monsoon season and is mainly rainfed; boro is grown during the dry season and requires irrigation; and aus production takes place during the spring rains before the monsoon and generally requires supplementary irrigation (Shahid, 2010; Ruane et al., 2013). Intensive cultivation of rice takes place in the northern part of the country, particularly the northwest where boro rice is the main cultivated crop. In the coastal south, where intrusion of saline sea water occurs during high tides and storm surges, continuous crop production is hindered by soil salinity. A common farming system in this region involves rice cultivation during the rainy season followed by aquaculture, namely shrimp, during the dry season (Alam et al., 2010). However, the flooding of crop lands for shrimp production reduces yields of rice in subsequent seasons and has contributed to a reduction of agricultural land over time (Karim, 2006; Alam et al., 2010). In addition, the poor are

increasingly excluded from engaging in this livelihood activity as previously open fisheries are converted into aquaculture using enclosed water bodies (Toufique and Gregory, 2008). This exacerbates income inequality in the region, as income from paddy rice production is very low compared to the income earned from shrimp farming (Karim, 2006). Irrigation has contributed to large increases in agricultural productivity across the country (Ahmed and Sampath, 1992; Asaduzzaman et al., 2010; Hossain et al., 2005; Palmer-Jones, 2001). The share of cultivated area equipped for irrigation has expanded rapidly from 16% in 1978 to 58% in 2008 (FAO, 2012). Area expansion was closely tied to the spread of groundwater irrigation using both shallow and deep tubewells—in 2008, more than 5 million hectares of land were irrigated during the dry season of which 79% was irrigated using groundwater (Turner and Shajaat Ali, 1996; World Bank, 2005; Shah et al., 2006; BADC, 2012; FAO, 2012). Groundwater aquifers are hydraulically connected to major waterways and are replenished during the monsoon (Chowdhury, 2010), with recharge potential greatest in the northern parts of the country (Shamsudduha et al., 2009). However, the withdrawal of surface water upstream reduces groundwater recharge and exacerbates salinity problems in the coastal region (Chowdhury, 2010). Moreover, groundwater in areas receiving less recharge can dry up during the boro season. Only aquifers underlying new surface systems would receive more water. The majority of irrigation schemes are small-scale in nature; only 10 percent of all irrigation is delivered via large-scale surface schemes, which generally suffer from lack of maintenance and investments (Asaduzzaman et al., 2010). Surface water systems also depend on water from trans-boundary rivers which makes the quantity available for irrigation less certain (Chowdhury, 2010). Groundwater irrigation is generally more flexible than surface water irrigation, given that farmers tend to have more control over its use than surface water. Used conjunctively with surface water resources, groundwater use can lead to increases in water use efficiency, given that farmers bear the costs of groundwater extraction (Asaduzzaman et al., 2010). At the same time, there are downsides to heavy dependence on groundwater. First, groundwater is very costly, with pumping costs typically greater than surface water pumps and requiring access to diesel or electricity. Furthermore, while groundwater development has been smallholder-driven and, therefore, has been relatively equitable, there are still significant gaps between relatively richer smallholders who have been able to draw down groundwater sources and smallholders who cannot afford deeper tubewells or to maintain surface water irrigation systems (for e.g., Sadeque, 2000). While Bangladesh has made significant progress over the past several decades in terms of economic growth, poverty reduction, and human development; an estimated 35 million people still live in extreme poverty and are highly vulnerable to livelihood shocks (World Bank, 2005). Several studies have shown that agricultural growth is key to reducing poverty in the country (Irz et al., 2001; Loayza and Raddatz, 2010; Thirtle et al., 2003; Timmer, 2005). However, the poor face greater challenges to improving agricultural productivity and increasing their income from agricultural production given difficulties accessing key inputs, such as irrigation equipment, and water and land resources (Namara et al., 2010; Smith, 2004).

3. Data collection and methodology This study uses data from the Bangladesh Integrated Household Survey (BIHS), which was conducted from October 2011 to March 2012. The survey was designed and supervised by senior

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

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Fig. 1. Sampled sites within the Bangladesh Integrated Household Survey (BIHS).

researchers at the International Food Policy Research Institute (IFPRI) and implemented by a Bangladeshi consulting firm with expertise in conducting complex surveys and data analyses, Data Analysis and Technical Assistance Limited (DATA). The BIHS was designed to provide data for several studies planned under the USAID-funded Bangladesh Policy Research and Strategy Support Program (PRSSP). The BIHS sample is statistically representative at the following levels: (a) nationally representative of rural Bangladesh; and (b) representative of rural areas of each of the 7 administrative divisions of the country: Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, and Sylhet. An additional sub-sample, representative of USAID’s Feed the Future (FTF) Zone, was collected in the same effort but is not included in the present analysis. The total BIHS sample size is 5500 households in 275 primary sampling units (PSUs). BIHS follows a stratified sampling design in two stages—selection of PSUs and selection of households within each PSU—using the sampling frame developed from the community series of the 2001 population census. In the first stage, a total sample of 275 PSUs were allocated among the 7 strata (divisions) with probability proportional to the number of households in each stratum, which resulted in the following distribution: 21 PSUs in Barisal, 48 in Chittagong, 87 in Dhaka, 27 in Khulna, 29 in Rajshahi, 27 in Rangpur, and 36 in Sylhet. In the 2nd stage, 20 households were randomly selected from each PSU, for a final BIHS sample of 5500 households. Sampling weights were adjusted using the sampling frame of the 2011 population census.

3.1. Data The BIHS questionnaire includes several modules that together provide an integrated data platform to answer a variety of research questions. This study relied primarily on data collected using the module on agricultural production and costs, which captured plotlevel data on land and soil quality, crops grown, area planted, crop yields, input use and costs, agricultural technologies used, and access to agricultural extension services, among other information. The survey covered the agricultural production year from December 1, 2010 to November 30, 2011. Analyses based on this dataset suffer from two key limitations. The first, common to any cross-sectional (single time point) analysis, is that there may be unresolved endogeneity in the data – i.e., it is not possible to resolve to what extent a positive coefficient for a particular input reflects the benefit of using that input, and to what extent it reflects higher motivation or efficiency on the part of the farmer who tends to use that input. The other, and more significant limitation of these BIHS data is that while crops grown, area planted, irrigation use, and crop yields were reported for all plots, other inputs and costs were only recorded for the main plot of the household. The main plot was defined as the largest plot cultivated by the household, with two exceptions. First, if two or more plots had the same cropped area then the plot with non-rice cultivation was used as the main plot. Second, if the respondent had rented plots in addition to those he or she owned, then separate ‘main’ plots were selected from both those owned and those rented. A lack of agricultural input data across all plots precludes an econometric

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analysis of production at the household level. The analysis in this paper, therefore, is based on a dataset that consists almost exclusively of a single main plot per household, with clustering applied at the household level to account for cases where multiple main plots per household were collected. Analysis at the plot level has previously yielded econometric studies on the adoption of soil and water management practices (e.g, Bekele and Drake, 2003), as well as on crop-specific input use (e.g., Qaim and Traxler, 2005) and the role of specific inputs across specific demographic groups (e.g, Holden and Lunduka, 2010). Our approach is most similar to this last example as we examine the relative roles of different inputs on production, across higher and lower income groups, with particular interest in the role of irrigation for crop productivity. 3.2. Analytical methods All variables in the analysis are constructed directly from the BIHS dataset with the exception of the seasonal rainfall variables. Seasonal rainfall variables were created by summing daily rainfall data for available rainfall gauges, provided by the Bangladesh Meteorological Department, and creating spatial maps using ordinary kriging. These maps were then used to estimate seasonal rainfall at household locations. Labor is reported on a cost basis by multiplying the person-days of both hired and family labor by the average daily wage rate reported for each labor task (e.g., planting, weeding, or harvesting) in the survey. The analysis focuses on inputs, costs, and yield specific to a particular rice crop in a particular season, while non-rice crops are excluded from the analysis (descriptive statistics are presented in Table 2). Specifically, the set of explanatory variables includes: (i) household controls, including age and education of the respondent, exposure to extension services and household borrowing from different sources; (ii) seasonal rainfall over the recall period and the seasons immediately preceding the recall period; (iii) labor and other inputs; (iv) ownership of the plot and any irrigation infrastructure, disaggregating by region; and (v) the type of rice planted. These groups of variables capture a range of variables commonly incorporated into production function analyses, as well as variables specific to irrigation access and ownership that act as best-available proxies to address the irrigation questions of interest in our study. Additionally, we include (vi) quadratic terms to evaluate possible diminishing returns to inputs; and (vii) interaction terms to disaggregate the role of different forms of irrigation infrastructure by region. All analyses are weighted for sample selection at the division level, clustered at the household level to account for cases where more than one rice crop was reported by the household, and filtered to remove outliers greater than three standard deviations away from the mean. The functional form of our regression analysis is given by: Yi = ˇi,0 + ˇi,x X + ˇi,I I + ˇi,F F + ˇi,I2 I 2 + ˇi,IF IF + i

(1)

where Yi is the total production (yield) per acre for plot i, X is a vector of household and environmental control variables; I is a vector of labor, chemical, and other inputs costs; F is a vector of dummy variables describing characteristics of the particular rice plot (including variety and water source); the vectors I2 and IF represent any quadratic terms or interaction terms of interest (in this case, diminishing marginal returns to fertilizers, and the interaction of irrigation method with fertilizer use); and i is a residual error term implicitly incorporating both random disturbance and any technical inefficiencies. We refrain from labeling these ‘production function’ analyses in the strict economic sense due to the larger number of dummy variables (and their interaction terms) in the model than might commonly enter into a production function. The choice of multivariate regression with quadratic terms (as opposed

to forms such as Cobb–Douglas or translog that are common to production function analysis) allows for a vector of dummy variables (without forcing production to 0), and provides easily-interpreted results – the coefficients ˇi in the least squares analysis represent the marginal effect of a unit change in the variable of interest. While a criticism of quadratic functional forms (and other functions built from Taylor–series expansion) is that the gradients of the true functional form they approximate may not be well captured, estimates from such analyses have been found to be satisfactory for large samples (Griffin et al., 1987), such as we have in this study. 4. Results 4.1. Production differences Rice yield (kg/acre) values are shown in Table 1 for plots included in our sample, disaggregated by cropping season (aman, aus, and boro), by region (North, South), and by expenditure level of the household managing the plot (low, high). In this analysis, per-capita expenditure levels are used as proxies to identify relatively poorer and wealthier households. The two classes were generated by breaking expenditures into quintiles. The lower 2 expenditure quintiles represent the poorer households (low) and the upper 3 expenditure quintiles represent the richer households (high). While the definition of these two groups is based on expenditures, we refer to these groups below as poorer and wealthier households. The results in Table 1 show that boro rice yields are much higher than rice yields in other seasons – not necessarily surprising since boro is an irrigated crop. Furthermore, boro is the only rice crop for which there are significant differences in production across income categories and regions. Boro production is lower on plots managed by poorer households in both the North (p = 0.0257) and the South (p = 0.0005). It is also lower in the South than in the North for plots managed by poorer households (p = 0.0000) as well as those managed by wealthier households (p = 0.0015). There are no statistically significant differences between regions or wealth levels for either aman or aus. These results provide a first suggestion that water for irrigation is a determining factor in agricultural success in Bangladesh, and invite a deeper look into the factors explaining variation in rice productivity across seasons, regions, and wealth classes. In the following section, we present multivariate regression results examining the factors that explain differences in production intensity (kg/acre) of rice across the three rice cropping seasons. Each table shows results for the full sample, filtered for outliers, as well as results for the data subsets of poor and wealthy households. 4.2. Multivariate regressions Several factors shape rice production (measured as per-acre yields) similarly across all seasons. For example, as expected, hybrid and high-yielding varieties greatly out-produce local varieties across all three rice-growing seasons. Additionally, plots managed by older farmers tend to have lower production (aman and boro), as do plots managed by farmers relying on informal loans from kin (all seasons). In the latter case, informal borrowing may be an indicator of lack of access to formal credit (and possibly to other inputs critical to production). It may be that this result also partly reflects farmers facing production shocks and looking to family members for assistance to smooth consumption. Panel analysis in future, using further rounds of BIHS data, might shed greater clarity on the nature of this linkage. However, most of the key determinants of successful production differ by growing season. For aus rice, which is primarily rainfed, only the use of pesticides helps explain variation in yields, and

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

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Table 1 Differences in mean production intensity (kg/acre/season) across regions, income classes and rice crops. Aman n Full Lowincome Highincome

North South North South North South

1366 635 529 217 837 418

Boro Mean

S.E.

2860.23 2923.24 2812.88 2870.77 2890.16 2950.48

32.36 50.11 52.91 81.59 40.86 63.28

Aus

n 1558 418 589 119 969 299

Mean

S.E.

n

Mean

S.E.

5521.34 5117.17 5422.58 4719.99 5581.37 5275.24

39.52 75.91 63.94 144.05 50.20 87.79

159 224 78 86 81 138

2621.69 2612.08 2583.41 2530.02 2658.55 2663.23

80.68 74.73 111.57 121.19 116.88 95.01

Table 2 Multivariate regression results. Aman

Boro

Aus

Variables

Full

Low

High

Full

Low

High

Full

Low

High

HH head age HH head education HH total assets Extension visit YN Loans banks Loans lenders Loans NGO Loans kin Feb May 2010 Jun Sep 2010 Oct Jan 2010 Feb May 2011 Jun Sep 2011 Labor Pest herb insecticide costs Fertilizer costs Tools animal costs total Per area total seed cost South own ground water South own plot North own ground water North own plot Rice type aman hybrid Rice type aman local 1 Rice type aman local 2 Rice type boro hybrid Rice type ropa aush hyv Rice type aush local Rice type aush local develop Fert squared Pest herb insect squared Labor squared North XSW M FE North XGW M D FE North XGW M S FE South XSW M FE South XGW M D FE South XGW M S FE Constant Observations R-squared Robust standard errors in parentheses ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

−5.634*** −5.470 −0.00965 64.65 91.22 −82.32 27.78 −264.7*** −0.141 0.0331 −0.473 −0.583 0.0156 0.00158 0.108*** 0.0706*** 0.0162* −0.00178 −326.7 198.3** −41.71 32.31 −63.47 −747.7*** −682.9***

−6.505* −9.889 −1.017** 76.44 71.88 21.89 −21.50 −369.2*** −1.055** 0.580** 0.745 −0.294 −0.200 0.00891 0.0754 0.0718*** 0.00667 0.00329 −381.5 −1.336 −264.7 42.89 −245.7 −921.6*** −537.6***

−5.589** −10.13 0.00881 44.60 94.01 −137.9 66.38 −226.7*** 0.303 −0.289 −1.139** −0.560 0.143 0.000757 0.129*** 0.0740*** 0.0178* −0.00473 −244.9 268.0*** 65.46 30.47 −23.43 −660.6*** −800.1***

−5.743* 17.49* 0.184** 16.71 161.0* −123.9 22.73 −186.2** −0.198 −0.428 −0.0704 0.122 −0.241 0.00311 0.0632 0.0215 0.00943 0.000294 −180.1 −38.56 −36.28 118.9

−6.588 22.78 0.935*** −53.49 69.54 −314.0 98.95 −191.6 −0.0775 −0.296 −0.0114 −0.640 −0.0413 0.00776 0.0582 0.000417 −0.0134 0.0109 −1,360*** 220.6 −85.91 100.4

−7.623** 7.121 0.126** −4.404 189.2* −19.12 -1.116 −221.6** −0.559 −0.443 −0.510 0.645 −0.257 0.00354 0.0243 0.0242 0.0129* −0.00349 243.7 −176.9 6.811 101.8

−2.704 −12.17 0.0332 170.7 157.9 −341.6* 35.83 −0.902 0.514 −0.153 −1.812** 1.008 0.496 −0.000322 0.142* 0.0354 0.00122 0.0140 −726.0** −17.33 −218.4 −475.2***

1.964 0.453 −0.180 −62.70 46.01 −248.3 23.02 −485.5** 0.171 0.298 −1.401 0.661 0.233 −0.0106 −0.0589 0.101 0.0115 0.0207 −1,364** −164.5 −1,386** −502.0**

−7.108 −27.40 −0.0233 238.5 213.1 −487.3* -24.52 285.5* 0.815 −0.649 −1.952* 1.821* 0.810* 0.00534 0.310*** 0.00821 0.00347 0.00950 −667.3* 70.62 560.5 −490.7**

861.6***

997.5***

734.9*** 329.3* −550.5*** −223.5 −3.54e-07 −4.23e-06 −8.94e-09

648.8** −688.8** −396.1 −3.03e-06 3.57e-05 5.69e-09

377.5 −269.7 −143.8 2.21e-07 −2.21e-05* −1.92e-08

154.8 634.0*** 420.4 −4.753 637.8** 1.740*** 383 0.343

297.7 866.7** 865.2 −1.920** 353.5 1.766** 164 0.395

−263.3 277.7 364.3 1.280 769.1*** 1.383** 219 0.444

−1.55e-06*** −3.35e-06** −8.37e-10 −544.9 323.8*** −36.74 −173.5 −6.388 118.9 3.162*** 2.001 0.229

−1.28e-06** −3.16e-06 −5.74e-08 −1.184*** 477.7** −110.1 −244.9 23.45 93.02 2.822*** 746 0.259

−1.75e-06*** −4.06e-06*** −2.26e-10 133.6 201.1 −4.794 −233.3 −95.01 114.1 3.323*** 1.255 0.245

appears to be driven by plots managed by the wealthier households. For that season, we also see a clear influence of the strength and shape of the monsoon. More rainfall over the period February–May 2011 led to improved production, while more rainfall over the preceding period (October–January) had a significant negative impact on production. This latter result may reflect a disruption in the start of the aus season due to flooding. The specific mechanism by which climate impacts production is difficult to tease out in this analysis because climate is fairly auto-correlated across seasons (wet places tend to continue to be wet, dry places tend to continue to be dry). Fortunately, multi-collinearity is not a significant issue apart from the climate variables (a complete correlation matrix is included as Appendix A). Finally, the results suggest that supplemental irri-

−2.07e-07 −2.14e-06 −7.08e-09 220.2 240.0 325.1* −537.5* −692.8*** −27.00 5.868*** 1.976 0.114

6.73e-08 1.10e-05 −4.35e-08 −182.5 −349.5 −196.5 −1,285*** −1.173** −933.2** 6.223*** 708 0.138

−2.07e-07 −8.65e-07 −7.18e-09 307.6 449.5* 468.2** −236.8 −414.6 251.2 6.019*** 1.268 0.128

gation from shallow tubewells is important for aus production, though they also show that ownership of the irrigation system, i.e. a tubewell, has a negative effect on rice production. It should be noted that there is no theoretical expectation for groundwater irrigation to have a significant effect during the aus season, and we note in our descriptive statistics that the fraction of the aus plots that make use of groundwater is very small compared with aman and boro plots. Taken together with the small sample size for aus, we do not place strong weight on this finding. Foraman rice, we observe that tools/animals, and chemical inputs significantly shape rice yields. Specifically, aman yields from plots managed by poorer farmers depend on fertilizer inputs, with diminishing returns to these inputs. In contrast, among plots man-

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Table 3 Variable descriptions. Variable

Unit

Production HH head age HH head literacy

kg/acre Year

HH head education HH total assets

Year Tk

Extension visit YN HH sum extension visits Loans banks Loans lenders Loans NGO Loans kin Feb May 2010 Jun Sep 2010 Oct Jan 2010 Feb May 2011 Jun Sep 2011 Labor Pest Herb Insecticide Costs Fertilizer Costs ToolsAnimalCosts Total Per Area Total Seed Cost SouthOwnGroundwater SouthOwnPlot NorthOwnGroundwater NorthOwnPlot Rice Type aman hybrid Rice Type aman local 1 Rice Type aman local 2 Rice Type boro hybrid Rice Type ropa aush hyv Rice Type aush local Rice Type aush local develop NorthXSW M FE NorthXGW M D FE NorthXGW M S FE SouthXSW M FE SouthXGW M D FE SouthXGW M S FE

mm mm mm mm mm Taka/acre/season Taka/acre/season Taka/acre/season Taka/acre/season Taka/acre/season

Notes Age of household head Literacy of household head (1 – Cannot read or write; 2 – sign only; 3 – can read but not write; 4 – can read and write) Education of household head Total household assets, calculated as sum (Qty × % ownership by household × current market value) for all assets 1 if household has been visited by extension services Number of visits in recall period Fraction of HH loans granted by Banks Fraction of HH loans granted by lenders Fraction of HH loans granted by NGOs Fraction of HH loans granted by kin/friends Sum of rainfall over season Sum of rainfall over season Sum of rainfall over season Sum of rainfall over season Sum of rainfall over season Imputed total cost of family and hired labor for all production purposes Total cost of pesticides, herbicides, insecticides Total cost of fertilizers Total cost for rental/purchase of tools, machinery, or animals Total cost for seeds 1 if irrigation method is owned by respondent (South) 1 if plot is owned by respondent (South) 1 if irrigation method is owned by respondent (North) 1 if plot is owned by respondent (North) 1 if plot crop is hybrid aman rice 1 if plot crop is local aman rice (1st type) 1 if plot crop is local aman rice (2nd type) 1 if plot crop is hybrid boro rice 1 if plot crop is hybrid aus rice 1 if plot crop is local aus rice (1st type) 1 if plot crop is local aus rice (2nd type) 1 if plot relies on mechanical surface water irrigation (North) 1 if plot relies on deep tubewell irrigation (North) 1 if plot relies on shallow tubewell irrigation (North) 1 if plot relies on mechanical surface water irrigation (South) 1 if plot relies on deep tubewell irrigation (South) 1 if plot relies on shallow tubewell irrigation (South)

aged by wealthier farmers, expenditures on pesticides, tool and animal rentals, and fertilizers are all significant, with diminishing returns to both pesticides and fertilizers. The consistent diminishing returns to fertilizers across all income groups in the sample likely suggests that adequate quantities of fertilizer are used by even the poorer households due to available subsidies for urea, which cost the government nearly 50 billion Taka in 2010, or 0.7% of GDP (Mujeri et al., 2012). If we consider adequate to mean the level at which the marginal benefit from application equals the marginal cost of fertilizer (the economic optimum), then as a coarse test, we can solve for such a break-even point and examine how much of our sample falls above it. Farmers in our sample received an average of 15–17 Taka per kg of rice, across all varieties of aman rice; if the regression coefficients for the effect of fertilizer on aman yield are reasonable estimates (.0706 X − 1.55e-06 X2 , where X is spending on fertilizers in Taka per acre), then this corresponds to break-even spending on fertilizers to be in the range of 1250–3750 Taka per acre. In our sample, 85% of aman farmers spent more than 1250 Taka per acre on fertilizers and 60% spent more than 3750 Taka per acre. While coarse, this estimate provides reasonable support for the idea that fertilizer is at least adequately applied by a majority of farmers in the sample, and likely over-applied. Agronomic analysis of the same data suggest that farmers tend to over apply urea to aman rice, which is more heavily subsidized, and under-apply other fertilizers, namely TSP and MoP (Ahmed et al., 2013) (Tables 3 and 4 ). Turning to the irrigated boro season, we observe no significant effects of rainfall nor chemical agricultural inputs in explaining

variation across our sample, apart from the weak positive impact of tool and animal rental among wealthier farmers. Irrigation access has the greatest role in explaining boro production outcomes. Not surprisingly, in the northern districts of Bangladesh, rice plots with access to groundwater from shallow or deep tubewells perform better than plots reliant on non-mechanical surface or groundwater irrigation. However, in the lower-lying coastal south, plots reliant on surface water or groundwater fare significantly worse than those reliant on rainfall or non-mechanical forms of irrigation, especially among low-income farmers. As with the aus and aman analyses, the baseline category includes rainfed plots and plots using non-mechanical surface irrigation, which account for a very small component of the sample for boro rice. In order to compare production outcomes for plots irrigated with surface water with plots irrigated by shallow or deep tubewells more directly, we conducted an additional analysis for the boro season that drops the small number of rainfed plots and plots using non-mechanical surface irrigation and compares groundwater directly against a baseline category of surface water (Appendix B). In addition, we ran the analyses separately for the northern and southern divisions to tease out differences in terms of the effectiveness of irrigation sources in these two regions. This additional analysis demonstrates that irrigation performs much better in the north than in the south, with no significant differences among irrigation sources in northern divisions (i.e., surface water, shallow or deep tubewells). This result is not surprising given the pattern of boro cultivation, with most boro production taking place in the north (79% of boro plots in our sample). Rather, what

Table 4 Descriptive statistics – weighted mean and standard deviation Variable

Aman

Boro

Aus

Full sample

Low income

High income

Full sample

Low income

High Income

Full sample

Low income

High income

n = 2001

n = 746

n = 1255

n = 1976

n = 708

n = 1268

n = 383

n = 164

n = 219

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

Production 2955.37 1222.93 2900.85 1222.44 2989.17 1222.51 5517.10 1559.14 5374.67 1596.12 5599.95 1531.79 2648.75 1084.04 2601.30 1066.04 2684.58 1098.53 45.87 12.99 44.61 12.98 46.65 12.94 45.34 12.83 43.67 12.44 46.30 12.96 46.59 13.89 46.34 13.24 46.77 14.39 HH head age 2.75 1.25 2.48 1.21 2.91 1.24 2.71 1.24 2.41 1.20 2.89 1.23 2.72 1.25 2.56 1.21 2.84 1.26 HH head literacy 3.46 4.04 2.26 3.26 4.20 4.30 3.31 3.95 2.10 3.13 4.01 4.19 3.25 3.78 2.42 3.22 3.87 4.04 HH head education 56.63 366.69 23.60 83.51 77.10 460.92 66.79 411.85 28.22 169.45 89.22 500.30 34.80 203.31 19.30 46.81 46.50 265.92 HH total assets 0.08 0.27 0.04 0.21 0.10 0.30 0.08 0.27 0.05 0.21 0.10 0.30 0.09 0.28 0.05 0.22 0.11 0.32 Extension visit YN 0.24 1.31 0.19 1.59 0.27 1.10 0.22 1.03 0.12 0.77 0.28 1.15 0.31 1.75 0.23 1.34 0.38 2.00 HH sum extension visits 0.23 0.42 0.19 0.39 0.25 0.43 0.22 0.41 0.17 0.38 0.25 0.43 0.28 0.45 0.23 0.42 0.32 0.47 Loans banks 0.11 0.31 0.13 0.33 0.10 0.29 0.11 0.31 0.12 0.32 0.10 0.30 0.10 0.30 0.12 0.33 0.08 0.27 Loans lenders 0.29 0.46 0.32 0.47 0.28 0.45 0.31 0.46 0.35 0.48 0.29 0.46 0.36 0.48 0.44 0.50 0.29 0.46 Loans NGO 0.25 0.43 0.23 0.42 0.27 0.44 0.25 0.43 0.22 0.41 0.26 0.44 0.22 0.41 0.16 0.37 0.26 0.44 Loans kin 360.14 218.76 353.45 208.55 364.28 224.84 387.24 226.34 384.71 228.73 388.71 225.02 345.68 234.93 360.66 255.17 334.37 218.33 Feb May 2010 1159.40 398.39 1170.70 387.10 1152.40 405.23 1156.46 371.38 1176.84 374.08 1144.60 369.44 1129.97 427.55 1139.53 450.28 1122.76 410.46 Jun Sep 2010 239.77 109.76 234.39 115.29 243.11 106.09 225.64 80.20 215.06 79.10 231.79 80.22 302.48 153.83 297.67 152.08 306.11 155.39 Oct Jan 2010 361.02 90.87 355.42 86.40 364.49 93.40 372.04 91.59 370.82 88.55 372.76 93.34 366.57 114.11 366.40 111.85 366.70 116.05 Feb May 2011 1573.04 373.96 1576.75 360.97 1570.74 381.92 1512.52 290.44 1509.94 268.26 1514.02 302.69 1658.69 470.46 1679.43 509.07 1643.03 439.62 Jun Sep 2011 25438.56 23893.32 25283.66 15119.50 25534.56 27988.74 32599.89 24587.54 31841.20 19925.69 33041.17 26928.21 25289.56 21659.56 23284.57 13947.98 26803.32 25941.16 Labor 1199.56 1524.68 1158.79 1364.15 1224.82 1616.21 1747.59 1567.39 1646.67 1431.35 1806.29 1639.04 1492.01 1695.27 1244.56 1298.45 1678.83 1923.86 Pest herb insecticide costs 6273.45 5030.72 6449.10 5339.97 6164.58 4828.17 11829.86 6167.20 11614.08 5983.63 11955.37 6270.47 6719.03 4993.05 6226.81 4545.33 7090.65 5286.04 Fertilizer costs 5136.89 3236.23 5083.85 2820.72 5169.76 3469.57 5386.27 4840.28 5196.84 3765.25 5496.45 5365.78 5857.91 4970.83 5740.63 4526.22 5946.46 5290.36 Tools animal costs total 4463.69 4323.72 4456.13 3435.23 4468.37 4793.76 5416.57 4315.85 5601.77 5079.09 5308.85 3800.27 4356.16 3480.64 3845.08 2718.58 4742.02 3922.27 Per area total seed cost 0.02 0.15 0.02 0.14 0.02 0.15 0.02 0.15 0.02 0.13 0.03 0.16 0.07 0.26 0.06 0.23 0.08 0.28 South own ground water 0.16 0.37 0.12 0.32 0.18 0.39 0.10 0.30 0.06 0.23 0.13 0.33 0.29 0.45 0.19 0.39 0.36 0.48 South own plot North own ground water 0.04 0.21 0.03 0.18 0.05 0.22 0.08 0.27 0.06 0.23 0.10 0.30 0.01 0.09 0.00 0.05 0.01 0.11 0.37 0.48 0.32 0.47 0.39 0.49 0.40 0.49 0.35 0.48 0.42 0.49 0.20 0.40 0.19 0.40 0.20 0.40 North own plot 0.02 0.12 0.01 0.11 0.02 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rice type aman hybrid 0.23 0.42 0.24 0.43 0.22 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rice type aman local 1 0.04 0.20 0.04 0.20 0.04 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rice type aman local 2 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.29 0.08 0.27 0.10 0.31 0.00 0.00 0.00 0.00 0.00 0.00 Rice type boro hybrid 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.33 0.09 0.29 0.15 0.36 Rice type ropa aush hyv Rice type aush local 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.38 0.20 0.40 0.16 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.35 0.15 0.36 0.14 0.35 Rice type aush local develop 0.01 0.09 0.01 0.10 0.01 0.08 0.05 0.23 0.06 0.23 0.05 0.22 0.00 0.00 0.00 0.00 0.00 0.00 North XSW M FE 0.09 0.29 0.09 0.28 0.09 0.29 0.15 0.36 0.16 0.37 0.15 0.36 0.07 0.25 0.09 0.29 0.05 0.23 North XGW M D FE North XGW M S FE 0.23 0.42 0.26 0.44 0.22 0.41 0.55 0.50 0.59 0.49 0.52 0.50 0.08 0.28 0.07 0.26 0.09 0.29 0.03 0.16 0.02 0.14 0.03 0.17 0.06 0.23 0.05 0.22 0.06 0.24 0.11 0.31 0.09 0.29 0.13 0.33 South XSW M FE 0.00 0.04 0.00 0.03 0.00 0.05 0.01 0.11 0.02 0.13 0.01 0.09 0.01 0.09 0.01 0.09 0.01 0.10 South XGW M D FE 0.06 0.23 0.05 0.22 0.06 0.24 0.14 0.35 0.10 0.30 0.17 0.37 0.10 0.30 0.06 0.24 0.13 0.33 South XGW M S FE

7

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A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

appears to matter for boro outcomes in the north is the wealth of the household—boro yields are higher for plots managed by farmers with a greater level of household assets, which may enable these farmers to invest in maintenance of irrigation equipment and infrastructure. Among households cultivating boro rice in the south, most irrigate using shallow tubewells or surface water and there are no differences in boro yield outcomes between these two sources of irrigation water across the full sample. However, for wealthier households, shallow tubewells perform much better than surface water. The results also show that households using deep tubewells in the south fare much worse; possibly because they have difficulty coping with the cost of extracting water from deep tubewells or because these tubewells do not provide freshwater reliably. This result is true particularly for poorer households, while there is no difference between deep tubewells and surface water for the rich. A final observation from our results is that in neither the boro nor aman seasons (where we observe the greatest degree of groundwater irrigation) do we see a significant effect of ownership of the irrigation system. That is to say, plots managed by farmers who rent in their irrigation water (by renting pumps/shallow tubewells, or contractual arrangements between irrigation equipment owners and non-owner farmers for water sharing in exchange of cash or crop share) fare no worse, and in some cases perform better, than those managed by farmers who own, and thus have preferred access to irrigation water. A possible interpretation of this is that in the Bangladesh context (where groundwater is ample), informal markets in groundwater irrigation may act to improve access and equity for irrigating farmers. 5. Discussion Early in 2013, the Bangladesh Ministry of Agriculture launched its Master plan for Agricultural Development in the southern region of Bangladesh, jointly with the Food and Agriculture Organization of the United Nations (FAO) (Bangladesh and FAO, 2013), outlining (among other things) prioritized spending across the agricultural sector, including water management and drainage for the coming decade. Across a total of 578 billion Taka in planned spending (∼USD 7.4B), the plan allocates about 75 billion Taka (13% of the total) to water management. This includes 41 billion Taka for surface water augmentation, mainly canal excavation and rehabilitation focused in Barisal and Khulna, as well as 34 billion Taka for increased irrigation capacity, mainly on-farm water management and double-lifting technologies focused on Barisal and Chittagong. This is to say, most spending over the next decade in Southern Bangladesh on agricultural water development will be for enhancing surface water irrigation. Our results suggest that the coastal south may not be wellsuited for boro rice production. Currently there is very little boro production in the area compared to the north, while other livelihood activities, such as aquaculture, are more prominent. Given the limitation of boro production in the south, it is unclear whether significant gains may be realized from the planned massive investment in surface water irrigation. A recent study suggested that with good governance, storage of surface flows in polders could increase planted boro area by around 15% in at least 50% of years (depending on climate). This increases to 40% with investments in surface water infrastructure (Sharifullah et al., 2009). However, the pre-condition of good governance in water management is itself an area requiring research and development. Outside of the boro season, access to surface water does not appear to offer benefits in terms of aman or aus yields compared to rainfed plots, while access to groundwater shows some positive benefits. This result is not surprising, as aman and to some extent aus production is typically rainfed with groundwater used for supplemental purposes when needed. However, any

current benefits from groundwater may be ephemeral, with rising sea levels expected to raise water tables in the coastal south with concomitant degradation in groundwater quality (Shamsudduha et al., 2009). Our observation that informal groundwater markets appear to be functioning well across the country – in the north and the south – may have divergent implications for these two different areas. Coupled with the observation that groundwater recharge rates improve with abstraction (Shamsudduha et al., 2011), an implication for the north is that groundwater markets may be a means of equitable and sustainable development in agriculture. However, we acknowledge that seasonal depletion of shallow groundwater has also become more common. For the south, an expected decline in availability of good-quality groundwater may mean that any current success stories in groundwater reflect only a capacity to hold on marginally longer to an agricultural practice that is and will become increasingly difficult. In the context of our findings, we must view the plans for development in Bangladesh’s coastal south in one of two ways – either they are well-targeted investments to correct the problems facing boro production in the region, or they require large-scale spending capable only of temporarily propping up a livelihood strategy that is headed toward collapse due to pressures from both natural (via sea level rise) and social (via the rise of shrimp aquaculture) pressures. The structure of our data precludes us from comparing the relative importance of aquaculture and crop production to the households in our survey. It is clear however that a deeper understanding of the interplay between these two important livelihood strategies in the coastal south – and how benefits from either are shared across rich and poor households – is critical to judging the potential for Bangladesh’s planned investments in coastal surface water irrigation to address agricultural and livelihoods challenges in the region. In some other parts of the Southern and Eastern Asian region, concerns regarding the balance of fresh and brackish surface water, and use alongside groundwater are addressed at least in part by agricultural zoning (with Vietnam being the exemplary case; Kam et al., 2006), an approach that at best can be said to be ‘emergent’ in Bangladesh (Islam, 2006). This is unfortunate as within the South Asian subregion, Bangladesh may need such zoning the most. While Pakistan has irrigated the greatest fraction of its arable land (83% in 2002 compared to 56% in Bangladesh), irrigation intensity is greatest in Bangladesh (165% compared to 110% in Pakistan). This intensity arises from Bangladesh’s multiple cropping seasons of rice, compared to elsewhere in South Asia where agriculture is focused to a greater extent on wheat and cotton (Alauddin and Quiggin, 2008). Bangladesh thus sits somewhat uniquely in the region, with water-use intensity that leads South Asian nations, but with cropping patterns and coastal water-use conflicts that better match those of Southeast Asian nations where water-use intensity has yet to emerge as a constraint. Vietnam has experienced relative success in managing fresh and brackish water in its coastal delta regions via strong zoning and operational rules for its system of sluice gates (Kam et al., 2006), but the intensity of water demand in Bangladesh – expected to increase in the coming decades (Shahid, 2010) – may keep investments in surface water irrigation from yielding similar results.

6. Summary and conclusions We examined plot-level data for rice production across a nationally-representative household survey in Bangladesh. We observe some results common to all three seasonal rice crops – aus, boro, and aman; namely, that plots tended to have higher yields if they were managed by younger farmers, by farmers not relying on

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

informal loans, and if they were planted with improved or hybrid rice varieties. We also observed a range of results particular to each cropping season. For aus rice, pesticide use was the only significant determinant of rice yield, and then only for wealthier households. Overall, rainfall was the most important factor explaining variation in aus production. For aman rice, labor and fertilizer use explain variation in yields for plots managed by poorer households; while pesticides, fertilizer, and the use of mechanical inputs and animals explain variation in plots managed by the wealthy. Across all households, significant and diminishing returns to fertilizer spending are observed, possibly demonstrating over-application of urea. Our findings for boro rice have perhaps the greatest policy significance. Rather than inputs, access to different forms of irrigation has the greatest role in explaining variation in boro yield, particularly in the coastal south region of Bangladesh. Rising sea levels, flooding, and lack of access to high-quality groundwater have hampered agricultural production in this region in recent decades, with brackish shrimp aquaculture becoming an increasingly important part of rural livelihoods. In response, the government of Bangladesh is planning massive investments in the region for the improved provision of surface water irrigation. Our results show generally poorer

9

returns to boro rice production in the south, with some exception in the case of plots fed by shallow tubewells. The expected decline in groundwater accessible from these sources, coupled with our econometric findings, suggests that boro production in the south may not be a good strategy to promote for the region. Whether the emerging alternative of brackish shrimp aquaculture can provide an equitable and sustainable alternative should continue to be a focus of research for the region. Acknowledgements This work was funded by the Bangladesh Policy Research and Strategy Support Program (BPRSSP) of the Government of Bangladesh, International Food Policy Research Institute (IFPRI), and United States Agency for International Development (USAID) and forms part of the CGIAR Research Program on Policies, Institutions and Markets. Appendix A. Correlation Coefficients among Regression Variables – Aman Season

10

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

Correlation Coefficients among Regression Variables – Boro Season

Correlation Coefficients among Regression Variables – Aus Season

Appendix B Supplemental regressions for North and South regions, comparing groundwater irrigation against a surface water irrigation baseline South

North

Full sample Coeff

High-income sample

Full sample

Low-income sample

High-income sample

p

Coeff

RSE

p

Coeff

RSE

p

Coeff

RSE

p

Coeff

RSE

p

Coeff

RSE

p

6.43 112.5 35.37 0.37 357.06 115.33

0.17 0.08 0.21 0.22 0.39 0.69

−16.06 137.89 −72.2 −1.95 −1669.68 −16.78

10.6 233.74 90.75 1.78 1350.39 157.24

0.13 0.56 0.43 0.28 0.22 0.92

−11.52 150.9 −38.8 0.39 −676.38 266.69

7.6 134.57 42.14 0.42 368.3 83.08

0.13 0.26 0.36 0.36 0.07 0

−3.51 −66.12 38.31 0.16 62.96 −43.65

3.37 63.95 19.01 0.09 194.1 47.81

0.3 0.3 0.04 0.06 0.75 0.36

−3.44 −249.85 112.24 0.94 334.63 −92.11

5.78 105.45 40.11 0.11 368.71 72.72

0.55 0.02 0.01 0 0.37 0.21

−3.72 22.52 4.76 0.11 3.33 −28.93

4.24 83.38 22.27 0.06 221.34 49.33

0.38 0.79 0.83 0.06 0.99 0.56

216.61 232.44 204.36 200.95 1.07 0.74 1.32 1.31 0.44 0.01 0.09 0.04 0.02

0.03 0.37 0.65 0.99 0.06 0.93 0.69 0.2 0.12 0.33 0.37 0 0.02

−602.39 −820.87 374.73 541.59 3.6 1.23 −2.64 −4.13 −0.91 −0.02 −0.05 0.19 0.01

433.48 591.82 358.88 505.21 2.14 1.88 2.83 3.16 1.05 0.02 0.24 0.07 0.02

0.17 0.17 0.3 0.29 0.1 0.51 0.35 0.2 0.39 0.14 0.83 0.01 0.51

−413.09 −52.67 −175.52 −201.4 1.34 −0.45 2.03 −1.2 −0.55 0 0.05 0.11 0.04

261.37 281.55 260.68 225.47 1.25 0.9 1.63 1.49 0.5 0.01 0.1 0.05 0.02

0.12 0.85 0.5 0.37 0.29 0.62 0.21 0.42 0.28 0.67 0.6 0.02 0.09

295.13 −85.13 38.79 −231.04 −2.74 0.42 2.38 3.23 −0.09 0.01 0.03 −0.02 0

106.06 141.21 99.8 103.83 1.14 0.51 1.34 1.88 0.71 0 0.05 0.02 0.01

0.01 0.55 0.7 0.03 0.02 0.41 0.08 0.09 0.9 0.04 0.58 0.38 0.58

119.29 −249.06 71.8 −331.08 −4.39 1.22 5.59 4.46 0.28 0.01 0.05 −0.03 −0.04

182.67 198.73 159.75 155.7 1.78 0.84 2.24 3.02 1.1 0.01 0.15 0.05 0.02

0.51 0.21 0.65 0.03 0.01 0.15 0.01 0.14 0.8 0.4 0.74 0.55 0.03

352.05 46.13 19.37 −184.28 −1.1 0.09 −0.43 1.31 −0.98 0 −0.05 0 0

129.56 187.09 129.15 133.15 1.58 0.62 1.71 2.52 0.94 0 0.06 0.02 0.01

0.01 0.81 0.88 0.17 0.49 0.88 0.8 0.6 0.3 0.4 0.42 0.82 0.91

0.01 274.26

0.98 0.55

0 −856.21

0.01 675.16

0.89 0.21

−0.02 107.17

0.03 282.18

0.46 0.7

0

0.01

1

0.01

0.02

0.72

0

0.02

0.78

148.64

0.91

67.28

317.74

0.83

−147.34

174.51

0.4 −23.36

162.2

0.89

−77.22

343.44

0.82

9.61

175.03

0.96

113.15

79.09

0.15

111.71

134.1

0.41

95.11

98.08

0.33

253.27

0.1

−8.55

593.91

0.99

462.69

283.95

0.11

1000.76

163.08

0

1141.33

294.88

0

925.19

194.32

0

0 0 0

0.02 0.85 0.49

0 0 0

0 0 0

0.02 0.46 0.5

0 0 0

0 0 0

0.11 0.73 0.75

0 0 0 18.29 46.53

0 0 0 256.5 230.46

0.22 0.98 0.15 0.94 0.84

0 0 0 −51.32 −118.84

0 0 0 401.89 340.2

0.74 0.69 0.84 0.9 0.73

0 0 0 266.48 294.08

0 0 0 348.58 317.05

0.24 0.49 0.54 0.45 0.35

293.99 218.43 762.11

0.04 0.13 0

−1154.94 −181.87 6114.08

491.99 324.82 1569.39

0.02 0.58 0

−336.61 558.3 4749.72

394.78 296.87 907.95

0.4 0.06 0

4539.62

1255.34

0

3110.24

1970.62

0.12

6604.99

1712.75

0

A.R. Bell et al. / Land Use Policy 48 (2015) 1–12

HHHead age −8.89 HHHead literacy 198.79 −44.11 HHHead education 0.46 HHTotal assets −307.09 Extension visit YN 46.79 HHSum extension visits Loans banks −464.78 Loans lenders −210.56 Loans NGO −92.68 Loans kin −1.79 Feb May 2010 2.03 0.07 Jun Sep 2010 0.52 Oct Jan 2010 −1.69 Feb May 2011 −0.67 Jun Sep 2011 −0.01 Labor Pest herb insecticide costs 0.08 0.14 Fertilizer costs 0.04 Tools animal costs total 0 Per area total seed cost South own ground −165.58 water −17.3 South own plot North own ground water North own plot Rice type aman hybrid Rice type aman local 1 Rice type aman local 2 417.44 Rice type boro hybrid Rice type ropa aush hyv Rice type aush local Rice type aush local develop 0 Fert squared Pest herb insect squared 0 0 Labor squared North XGW M D FE North XGW M S FE −617.45 South XGW M D FE 328.54 South XGW M S FE 4489.02 Constant

Low-income sample RSE

11

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