Climate and landscape mediate patterns of low lentil productivity in Nepal

RESEARCH ARTICLE Climate and landscape mediate patterns of low lentil productivity in Nepal Gokul P. PaudelID1*, Mina Devkota1¤a, Alwin Keil2, Andrew J. McDonald1¤b 1 International Maize and Wheat Improvement Center (CIMMYT), South Asia Regional Office, Kathmandu, Nepal, 2 International Maize and Wheat Improvement Center (CIMMYT), National Agricultural Science Centre Complex (NASC), New Delhi, India ¤a Current address: International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco ¤b Current address: Section of Soil and Crop Sciences, School of Integrative Plant Sciences, Cornell University, New York, United States of America * g.paudel@cgiar.org Abstract Lentil (Lens culinaris Medik.) is a cool-season pulse grown in winter cropping cycle in South Asia and provides a major source of nutrition for many low-income households. Lentil pro- ductivity is perceived to be sensitive to high rainfall, but few studies document spatial and temporal patterns of yield variation across climate, soil, and agronomic gradients. Using farm survey data from Nepal, this study characterizes patterns of lentil productivity and effi- ciency for two cropping seasons. Additional insights were derived from on-farm trials con- ducted over a 5-year period that assess agronomic, drainage, and cultivar interventions. To contextualize the inferences derived from farm surveys and trials, the Stempedia model was used to simulate the severity of Stemphylium blight (Stemphylium botryosum) risk–the prin-

cipal fungal disease in lentil–with 30 years of historical climate data. Although development efforts in Nepal have prioritized pulse intensification, results confirm that lentil remains a risky enterprise highlighting the prevalence of crop failures (16%), modest yields (353 kg ha- 1), and low levels of profitability (US$ 33 ha-1) in wet winters. Nevertheless, site factors such as drainage class influence responses with upland sites performing well in wet winters and lowland sites performing well in dry winters. In wet winters, a phenomena perceived to be increasing, 76% of surveyed farmers reported significant disease pressure and simulations with Stempedia predict that conditions favoring Stemphylium occur in >60% of all years. Nevertheless, simulation results also suggest that these risks can be addressed through earlier planting. Based on the combined results, gains in yield, yield stability, and technical efficiency can be enhanced in western Nepal by: 1) ensuring timely lentil planting to mitigate climate-mediated disease risk, 2) evaluating new lentil lines that may provide enhanced resistance to diseases and waterlogging, and 3) encouraging the emergence of mechaniza- tion solutions to overcome labor bottlenecks. PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Paudel GP, Devkota M, Keil A, McDonald AJ (2020) Climate and landscape mediate patterns of low lentil productivity in Nepal. PLoS ONE 15(4): e0231377. https://doi.org/10.1371/journal.

pone.0231377 Editor: Vincent Vadez, Institut de Recherche pour le Developpement (IRD), FRANCE Received: December 2, 2019 Accepted: March 20, 2020 Published: April 16, 2020 Copyright: © 2020 Paudel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Part of the data used in this manuscript are online available while other data which are not available online are attached in the supplementary section of this article. The climate data related with this article were purchased from the Ministry of Environment, Department of Hydrology and Meteorology, Government of Nepal and can be found in http:// dhm.gov.np/meteorological-station/. The farm survey data can also be found in the CIMMYT dataverse. URL link is: https://data.cimmyt.org/ dataset.xhtml?persistentId=hdl:11529/10548086.

Introduction In South Asia, climate change impacts are increasingly apparent, with drought [1,2], floods and waterlogging [3–7], and heat waves [8,9] all increasing. Plethora of literature have shown that these climatic anomalies in South Asia will continue in the future [10–20]. With more cli- matic anomalies and progressive climate changes, many studies have documented either an increased occurrence frequency or new degree of severity of abiotic stresses affecting crop growth and yield [21–28]. Climate changes are also influencing the spread and intensity of biotic stresses including diseases, weeds, and pests [25,26,29,30]. Moreover, there can be inter- actions between biotic and abiotic stresses that increase crop damage [21,22,31]. Lentil (Lens culinaris Medik.) is one of the major winter pulse crops in South Asia and is typically sown after rice in the annual cropping rotation. Lentil cultivation in South Asia con- stitutes almost half of the area and one third of the volume of global production [32]. Lentil seed contains high concentrations of protein and micronutrients [33], thereby playing a major role in the food and nutritional security of millions of low-income South Asian families [34], and is also an important contributor to soil health in cereal-based cropping systems due to its nitrogen fixing ability [35,36]. In Nepal, lentil is grown in the Terai, Inner Terai and mid-hills and constitutes 60% of the total grain legume area and production [37]. Globally, Nepal ranks

sixth in terms of lentil production and fifth in terms of export to world markets [32,37]. Global demand for lentil has expanded at a robust annual rate of 6.2% over the past ten years, and it is estimated that export revenue derived from lentil in Nepal could double or even triple if rele- vant actions are taken to boost cultivated area, productivity, and market integration [38,39]. However, in recent years lentil yields in Nepal have stagnated due to factors including low levels of investment in inputs, cultivation of older varieties, and increased disease pressure [40–45]. In Nepal, lentil is cultivated under rainfed conditions after the monsoon rains have retreated, and production is highly sensitive to growing season weather [35,36,46,47]. Lentil productivity, therefore, is dependent on the residual soil moisture available from the late mon- soon season and the amount and distribution of winter rainfall. However, excessive seasonal rainfall increases the chances of waterlogging in lentil particularly in lowland areas with poor drainage facility. In higher rainfall winters, waterlogging both directly impedes lentil growth and development while potentially causing or exacerbating fungal disease outbreaks [36,48]. Erskine and El Ashkar [47] reported that variability in rainfall during the lentil production period is responsible for 41% of the variation in mean yields. Furthermore, in recent years, the fungal disease Stemphylium blight (Stemphylium botryosum), has emerged as a major threat to

lentil production in South Asia, including in Nepal [49,50]. Consequently, lentil yields in Nepal are judged to be highly sensitive to both rainfall deficit and rainfall excess, extremes that some projections suggest may increase in the future [51]. High sensitivity to variable climate risks is also perceived to be a major barrier towards farmer investments in productivity enhancement. Minimizing lentil yield losses has emerged as a development priority for improving liveli- hoods and securing the food and nutritional security in Nepal. In order to meet the demand for lentil, either production or productivity have to be increased. Expanding the land frontier to increase production is not considered a viable option due to low availability of land reserves that could be brought into production with acceptable environmental costs [52,53]. Hence, the most viable option for enhancing lentil production is through improving productivity levels. For the study region in the Mid- and Far-western Terai plain and mid-hill region of Nepal (henceforth ‘western’ Nepal), this study integrates three complementary research activities: 1) household surveys (2014 and 2015 harvests) to assess current productivity levels and drivers of technical efficiencies, 2) on-farm trials to assess the value of new agronomic production PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 2 / 19 Funding: This research was conducted as part of the Cereal Systems Initiative for South Asia

(CSISA) in Nepal, which is funded by the United States Agency for International Development (USAID) (Grant no: BFS-G-11-00002) and Bill & Melinda Gates Foundation (BMGF) (Grant no: OPP1133205), and conducted by the researchers at the International Maize and Wheat Improvement Center (CIMMYT). The funders had no role in the study design, data collection, analysis, manuscript preparation, and decision to publish the manuscript. Competing interests: The authors have declared that no competing interests exist.

practices and varieties (2012/13–16/17), and 3) simulations with the Stempedia model [49] to assess inter-annual variations in Stemphylium disease risk and to establish the risk-reducing value of modifying planting dates. Materials and methods This research uses different types of dataset and followed the standard ethics during the data collection. First, no personal identifiable information was recorded in the experimental trails data. Only farmers’ plots were used to establish experimental trials in the study areas, after tak- ing consent from the farmers to use their plots. Second, for the farm survey, the authors fol- lowed the donor’s standard ethics protocol regarding the human subject research during the data collection. The authors have also taken the consent from the farmers before the survey so that no any personal identifiable information would be disclosed during data sharing. Site description On-farm experiments were conducted over five seasons (2012–2016 harvest years) to evaluate the influence of agronomic interventions and varietal choice on lentil yields in two contrasting ecologies: the mid-hills (Surkhet and Dadeldhura districts) and the Terai plain (Banke, Bar- diya, Kailali, and Kanchanpur districts). The two districts in the mid-hills are located at 28.01˚ to 28.59˚ E latitude and 80.12˚ to 81.36˚ N longitude and 700–1800 m altitude, while the four districts in the lowland Terai are located at 27.51˚ to 29.28˚ E latitude and 80.01˚ to 82.8˚ lon- gitude and range of 100–700 m altitude. The experimental sites in the mid-hills are well-

drained (upland) with maize (April-August) and lentil (September to April) grown in the annual rotation, while the Terai sites are poorly drained (lowland) with rice (June-October) and lentil (October-March) grown in the annual cropping system rotation. On-farm experiments Three categories of on-farm experiments were conducted: (1) evaluation of different crop establishment methods: drainage modification (bed planting), zero tillage, minimum tillage (single pass), and hand broadcasting into conventionally-tilled soil, (2) varietal evaluations using recently released varieties by Government of Nepal, advanced genotypes (in the process of release) and locally available varieties grown by the farmers, and (3) integrated manage- ment, i.e., improved variety, fertilizer application (recommended rates of 20:40:20 kg N:P2O5: K2O ha-1), mechanized seeding contrasted to prevailing farmer practices of hand broadcasting of local seed with conventionally-tilled soil and no mineral fertilizer. Details of the year and location for these experiments are presented in S1 Table. In each of the districts experimental areas were located at 1 to 25 km distance. For each experiment, analysis of variance (ANOVA) was conducted to assess treatment and year effects along with their interaction. Household surveys The survey was conducted for two lentil producing years (2013–14 and 2014–15, hereafter referred as 2014 and 2015, respectively). In 2014, the survey was conducted in four major lentil growing districts: Banke and Bardyia in the Mid-western Terai region along with Kailali and

Kanchanpur in the Far-western Terai region. Based on the lentil acreage in each of the sub-dis- tricts (village development committees or VDCs), 10 VDCs were purposively selected to cap- ture major lentil growing areas. With these VDCs, a total of 193 lentil-growing households were randomly selected for the survey. In 2015, the survey was expanded to six districts by including two additional districts from the mid-hills, i.e., Surkhet (Mid-western region) and PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 3 / 19

Dadeldhura (Far-western region). A total of 43 VDCs and 600 households were surveyed in 2015 using the same selection methodology as in the previous year. Data were collected through face-to-face interviews using a structured questionnaire, con- ducted following lentil harvest. The questionnaire includes sections to elicit information on household demographics, lentil production technologies, inputs, agronomic practices, yields, end uses for harvested lentil, and general perceptions towards lentil cultivation. Households that planted lentil but lost the crop due to biotic or abiotic stress were retained in our analysis. Weather data Daily minimum and maximum temperature (˚C), and rainfall (mm) data corresponding to the household survey periods were collected from the Department of Hydrology and Meteo- rology, Government of Nepal [54]. Rainfall (mm) along with maximum and minimum tem- peratures (˚C) were averaged for each weather station (n = 16 for temperature, n = 58 for precipitation) within our study area over the lentil season (October to March). An inverse dis- tance weighting (IDW) algorithm [55] in ArcGIS v10.3 was used to create gridded weather data at a 1 km resolution. Gridded data were then paired with survey locations. Furthermore, long-term (1985–2015) historic weather data for selected meteorological stations from differ- ent districts of the Nepal Terai were also obtained from the same sources [54], and a historical trend analysis has been carried out for seasonal precipitation. Station data from Banke district

was used to drive the Stempedia model simulations. Stochastic frontier model A production frontier represents the maximum output attainable for a given set of inputs and a given production technology [56]. Failure to attain the frontier output implies that produc- tion is technically inefficient. However, survey data on agricultural production may be heavily contaminated by statistical noise due to measurement errors, variability in climatic and edaphic conditions, or interactions with pests and diseases. In contrast to Data Envelopment Analysis (DEA) that attributes any deviation from the frontier output to inefficiency [57], sto- chastic frontier analysis accommodates statistical noise. We therefore chose this approach for our analysis. The stochastic production frontier was independently proposed by Aigner et al., [58] and Meeusen and van Den Broeck [59] and is defined as follows: Yi ¼ FðXi; bÞexpðVi UiÞ; i ¼ 1; 2; . . . ; N ð1Þ Where; Y = Quantity (or value) of output of the i-th firm F(�) = Suitable production function X = Vector of input quantities β = Vector of parameters to be estimated V = Random error term U = Non-negative error term representing technical inefficiency V is a random variable, assumed to be independently and identically distributed as N(0, σv). U, which captures systematic shortfalls from the frontier due to technical inefficiency, is assumed to follow a particular one-sided distribution. A number of different distributions have been proposed in the literature, namely the half-normal and exponential [58], the truncated normal

[60], and the two-parameter Gamma distribution [61]. The technical efficiency (TE) measure for the i-th household TE = exp(−Ui) 2 [0, 1] is the ratio of the observed output and the maxi- mum attainable output at the frontier. The maximum likelihood (ML) estimation of Eq (1) PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 4 / 19

yields estimates of β and γ where,g ¼ s2 U s2 2 0; 1 ½ �, and s2 ¼ s2 U þ s2 V . Hence, the model sepa- rates the residuals into a normally distributed random error and a one-sided error term reflect- ing technical inefficiency; the latter is related to input management and measures the degree to which a farmer was able to obtain the maximum possible output for a given vector of inputs. Several studies have been previously conducted using the two-stage stochastic production frontier model. In the two-stage stochastic production frontier analysis, the first stage involves the specification and estimation of stochastic frontier and prediction of technical efficiency score, while in the second stage determinants of the technical efficiency score are regressed. However, Battese and Coelli [62] and Wang and Schmidt [63] reported that the two-stage method is inconsistent and a single equation method is preferred because the two-stage method contradicts the assumption of an independently, identically and normally distributed inefficiency effect in the stochastic frontier function. Furthermore, Wang and Schmidt [63] pointed out that the first stage is biased if dependent variables of the first and determinants of the second stage are correlated. Kumbhakar et al. [64] and Reifschneider and Stevenson [65] specified a stochastic frontier model in which inefficiency effects were defined to be explicit functions of firm-specific factors, and all parameters are estimated in a single-stage maximum

likelihood procedure. In this study we applied the Battese and Coelli [62] model for the two years of cross-sectional data to derive unbiased estimates for lentil production in Nepal. Since both years were similar in terms of climatic parameters (wet winter), we amalgamated two years of survey data in order to develop a single production frontier. According to Battese and Coelli [62], the technical efficiency effects of U are obtained by truncation (at zero) of the nor- mal distribution with mean μi = Ziδ, where Zi is the farm specific explanatory variables and δ is the vector of parameters to be estimated. The stochastic frontier production function for lentil farming has a Cobb-Douglas type functional form and the production function estimation adopted in our study is defined as: Ln Yi ¼ b0 þ X 9 j¼1 bjLnðXijÞ þ X 9 k¼1 bkðDikÞ þ X 1 p¼1 X 1 r¼1 bpr Rpri þ Vi Ui ð2Þ Where, Ln Yi = Natural logarithm of output i = i-th farm (i = 1, 2, 3,. . ., 665) β = vector of parameters to be estimated Ln(Xj) = vector of input parameters Dk = vector of dummy variable related with lentil production Rpr = Total amount of rainfall differentiated by lowland production ecology V = N(0, σV distributed random error term U = non negative error term that represents technical inefficiency The non-negative error term is specified as a function of household-specific determinants of technical inefficiency, which can be specified as: Ui ¼ d0 þ X 7 l¼1 dlZli þ Wi ð3Þ Where, δ = vector of parameters to be estimated Zl = vectors of potential inefficiency determinants

W = N(0, σU) distributed random error, where σU is defined such that Ui � 0. PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 5 / 19

The STATA statistical program was used to estimate the maximum likelihood estimation of parameters for Eqs (2) and (3) based on a Cobb-Douglas type functional form. Our dataset contained 128 cases of complete crop failure and, hence, reported zero yield; these cases were omitted from the stochastic frontier analysis in order to avoid biased estimates of the production function. However, to crosscheck the robustness of our findings we esti- mated a Tobit model [66], which can accommodate zero output (yield) data, in addition to the stochastic frontier production function. The Tobit model has been widely applied when the dependent variable contains an accumulation of observations at zero [67–71]. The definitions and summary statistics of all variables included in the model are presented in the Table 1. The quantity of the lentil grain yield harvested by the farmers is the dependent variable, which is regressed with a number of farm input variables. The farm input variables used in the current stochastic frontier analysis are of two types. The first type of variable included in the models are continuous in nature and are: cultivated lentil land, labor, non- labor capital, amount of seed, amount of nitrogen, phosphorus and potassium, and amount of rainfall and average temperature during the lentil production period. The second type of vari- ables included in the models are dummy in nature and are related to soil types, diseases, water- logging, variety, and production ecologies for lentil farming. Finally, we also included the

interaction of rainfall with lowland production ecology in the model. The variables used for technical efficiency analysis are presented in Table 1 and most of the variables are included in the model as suggested by Bravo-Ureta and Pinheiro [72]. Some of the variables used in technical inefficiency determinant analysis are based on site-specific con- siderations. Inclusion of a gender variable in the model differentiates the potential role of gen- der in technical efficiency; in our case almost 80% of households were headed by a male. Furthermore, farming experiences influence the technical efficiency of lentil farming and we expect that farming experience is positively associated with technical efficiency. We included cultivated land and lentil-specialized farms (i.e. >50% of winter cropped area in lentil) as potential technical efficiency determinants. While we expect that cultivated land has an inverse relationship with technical efficiency as farmers with limited land resources may have incen- tives to increase yields through meticulous crop and input management [73], the lentil-special- ized farms are expected to have a positive relationship. Finally, we included the on-farm labor wage rate and labor availability variables; the former is expected to have a negative relationship with technical efficiency while the latter is expected to have a positive relationship. Simulating Stemphylium blight disease severity simulation using the Stempedia model The Stempedia model predicts the potential severity of Stemphylium blight disease and associ-

ated yield reductions in lentil based on sowing date, first flowering date, and daily weather; model performance has been verified in western Nepal [49]. The model assumes that inoculum does not limit disease progression. In our study, Stempedia is used to predict mean and inter- annual variations in disease severity risk in Banke district as a representative production envi- ronment for lentil in western Nepal. Crop characterization data was derived from on-farm and on-station experimental trials conducted in the region. To drive the model, long-term (1985– 2015) weather data for rainfall and minimum and maximum temperature were used from Banke district, with additional data for solar radiation and relative humidity retrieved from NASA’s POWER datasets [74]. Lentil variety Khajura-2, commonly grown by farmers in the western Terai, was used for the simulations. The model was run for nine different seeding dates at 10-day intervals starting from 1st October to 20th December. Seeding time of lentil PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 6 / 19

range from 3rd week of October to end of November, hence the dates bracketed the plausible seeding window for lentil in the rice-based production systems of the Terai. Results Characterization of lentil production across environmental gradients in western Nepal Average rainfall during the lentil growing season (October to March) was 253 mm and daily average temperature was 18.5˚C (Table 1). Seasonal rainfall in the studied year was Table 1. Lentil production characteristics and summary statistics. Variables Variables description Farms without crop failure (N = 665) Full samples (N = 793) Mean Std. Dev Mean Std. Dev Output† Total lentil grains produced (kg) 89.72 165.55 75.24 155.14 Cultivated land† Total cultivated land (ha) 0.70 0.72 Land† Area under lentil production (ha) 0.26 0.31 0.27 0.32 Capital inputs† Total non-labor capital inputs (NRs) excluding seed and fertilizers 1489.32 2930.78 1568.49 2949.26 Household size Number of household members (No.) 7.16 4.01 Gender Dummy, = 1 if household decision maker is male otherwise 0 0.80 Farming experience (years) Years of lentil farming (years) 11.95 12.6 Labor† Total hired and family labor used for lentil production (labor-hours) 101.79 118.54 104.35 117.64 Labor availability Dummy, = 1 if labor is easily available 0.65 Wage rate† On farm wage rate; NRs/day (sub-district level indicator) 306.47 39.21 Lentil specialized farms Dummy, = 1 if farms cultivate lentil on � 50% of their cultivated land 0.38 Input variables Seed† Seed quantity (kg) 11.38 13.72 11.36 13.35

Nitrogen† (N�) Total amount of Nitrogen applied (kg) 0.84 3.27 1.42 4.26 Phosphorus† (P2O5�) Total amount of Phosphorus applied (kg) 1.64 5.6 2.23 6.03 Potassium† (K2O�) Total amount of Potash applied (kg) 0.17 1.68 0.22 1.72 Variety Dummy, = 1 if farms used improved varieties for lentil production otherwise 0 0.08 0.09 Rainfall† Total amount of rainfall during lentil production period October to March (mm), sub-district level 252.89 116.77 253.38 108.76 Waterlogging Dummy, = 1 if lentil growing fields are waterlogged after rainfall otherwise 0 0.27 0.29 Temperature† Average temperature during lentil production (October to March) period (˚C), sub-district level 18.51 1.86 18.6 1.73 Relay seeding Dummy, = 1 if lentil is relay cropped with rice otherwise 0 0.07 0.09 Mixed cropping Dummy, = 1 if lentil is mixed cropped with other crops otherwise 0 0.55 0.57 Tillage method Dummy, = 1 if lentil growing plots are tilled using tractors and/or power tillers otherwise 0 0.35 0.36 Diseases Dummy, = 1 if lentil crop suffered from diseases and/or pest infestation otherwise 0 0.78 0.76 Soil type (sand) Dummy, = 1 if soil is coarse texture otherwise 0 0.20 0.19 Soil type (silt) Dummy, = 1 if soil is medium texture otherwise 0 0.42 0.43 Soil (clay) Dummy, = 1 if soil is fine texture otherwise 0 0.37 0.39 Land type (lowland) Dummy, = 1 if production ecologies lies in lowland otherwise 0 if it is upland 0.60 0.65 Rainfall × lowland Total amount of rainfall (mm) if the production ecology is lowland 131.66 132.39 146.36 131.91

†Variables used in logged form in the production efficiency and technical efficiency analysis. �Urea, DAP and Potash are the fertilizers for the N, P2O5, and K2O used in the model. Exchange rate: 1 US $ = NRs 98.6 and NRs. 102.7, respectively, for year 2014 and 2015 [86]. https://doi.org/10.1371/journal.pone.0231377.t001 PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 7 / 19

significantly higher than the long-term average and is reflected in the survey data with 27% of farmers reporting that their lentil fields were affected by waterlogging. The average land area under lentil production was 0.26 ha with a grain yield of 90 kg ha-1. In the region, 57% of farm- ers cultivated lentil in a mixed cropping system with mustard, wheat and lathyrus, and 7% of farmers practiced relay seeding of lentil with rice. The majority of farmers (91%) were using a local variety of lentil, with very low use of purchased fertilizers with average application rate for nitrogen, phosphorus, and potassium of 3.23, 6.31, and 0.65 kg ha-1, respectively. Sixty-five percent of farmers grew lentil in lowland areas which are dominated by clay to silty loam soils and vulnerable to waterlogging during periods of high rainfall. About 76% of farmers reported that their lentil field suffered from diseases (Table 1). In relatively wet winters included in the survey, lentil productivity in upland ecologies with better drainage (397 kg ha-1) was significantly higher than in lowland ecologies (329 kg ha-1) (Table 2). On the other hand, although still modest, investment in fertilizer was significantly higher in lowland ecologies (US$ 12.8 ha-1) than in upland (US$ 2.4 ha-1). The total labor cost involved in lentil cultivation was significantly higher in the upland ecologies (US$ 201 ha-1) than in lowland (US$ 117 ha-1). The higher labor cost in upland ecologies is associated with limited mechanization options. However, the non-labor capital cost was significantly higher in

lowland (US$ 57.8 ha-1) than in upland ecologies (US$ 46.6 ha-1). The total variable cost (US$ 295 ha-1 in upland vs. US$ 237 ha-1 in lowland) and gross margin (US$ 53.5 ha-1 vs US$ 22.2 ha-1) were significantly higher in the upland than in lowland ecologies. Despite having signifi- cantly higher yields and gross margins, the benefit–cost ratio is significantly lower in upland environments due to higher production costs. The spatial distribution of rainfall and average temperature during lentil production season for surveyed years are presented in Fig 1. There was a high within and across seasonal variabil- ity in rainfall among sampled districts. Winter rainfall variability in 2014 ranged from 19 mm to 340 mm, while the variability widened to 86 mm to 657 mm in 2015. There was less variabil- ity in average temperature during surveyed years (Fig 1). Long-term trend analysis for seasonal rainfall shows a positive slope indicating winters are becoming wetter (S1 Fig). Similar results were reported in earlier studies conducted in western Nepal [40,75]. Table 2. Lentil production characteristics differentiated by production ecologies. Variables Upland (N = 278) Lowland (N = 515) Sig. Overall (N = 793) Lentil yield (kg ha-1) 397.3 329.2 ��� 353.1 Seed cost ($ ha-1) 44.5 49.8 ns 47.9 Fertilizer cost ($ ha-1) 2.4 12.8 ��� 9.1 Labor cost ($ ha-1) 201.3 116.8 ��� 146.5 Non-labor capital ($ ha-1) 46.6 57.8 �� 53.9 Total variable cost ($ ha-1) 294.7 237.3 ��� 257.4 Gross Revenue ($ ha-1) 348.2 259.5 ��� 290.6

Regression results Model specification tests. In order to test potential multicollinearity among the explana- tory variables, variance inflation factors (VIFs) were calculated. Myers [76] suggested that VIFs values for each of the explanatory variables should not cross the limit of 10, in order to be free from multicollinearity. The mean VIF for the stochastic frontier model was 2.10 and ran- ged from 1.06 to 5.92, indicating no signs of multicollinearity. Additionally, the VIF value Fig 1. Within season and across year’s spatial variability in climatic parameters during lentil producing time (Oct–March) in study areas: a) rainfall in 2014, b) temperature in 2014, c) rainfall in 2015, and d) temperature in 2015. https://doi.org/10.1371/journal.pone.0231377.g001 PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 9 / 19

ranges from 1.09 to 7.18, with a mean value of 2.12 in the case of the Tobit model, indicating no signal detection for multicollinearity. Furthermore, the Cobb-Douglas type production function in agriculture is expected to follow a constant returns to scale (CRS), i.e., doubling all of the inputs increases the output by the same proportion [77]. However, since environment and/or climate related variables (rainfall and temperature) are not under the farms direct con- trol and doubling these inputs may result excessive waterlogging and/or extreme drought in crop leading to crop senescence, we excluded these variables while conducting test against con- stant returns to scale. This means that the sum of partial production elasticities should be one and the test on respective regression coefficient failed to reject the null hypothesis that their sum is one, indicating the existence of constant returns to scale in the production function. Parameter estimates in the stochastic production function for lentil farming in western Nepal. The maximum likelihood estimates for the parameters in the stochastic frontier model as defined in Eq (2) are presented in Table 3. The slope coefficient defines the output elasticity of inputs and the estimated signs of parameters are as expected. The result from max- imum likelihood estimates for the parameters in the stochastic frontier model and Tobit model indicated that lentil production in the wet years was significantly and negatively affected by higher rainfall and waterlogging (Table 3). These results indicate that a 1% increase in sea-

sonal rainfall decreases lentil yield by 0.40% in the stochastic frontier model and the impact magnitude is even higher (1.57%) in the Tobit model. There is a significant interaction effect of rainfall with land type on grain yield, where a 1% increase in rainfall in lowland areas decreases lentil yield by 0.22% in the stochastic frontier model and by 0.36% in the Tobit model (Table 3). The stochastic frontier model shows that farms that suffered waterlogging had a 16.7% lower yield and the impact magnitude in the Tobit model is much higher at 43.6%. A more modest temperature effect was observed with a 1% increase in the average tem- perature decreasing lentil yield by 1.25% in the stochastic frontier model and by 3.4% in the Tobit model. The stochastic frontier model showed that the occurrence of disease in lentil fields signifi- cantly reduces grain yield. Farmers who reported the presence of disease had a 31.5% lower lentil yield than the farmers who did not report disease problems. However, the coefficient of disease was not significant in the Tobit model (Table 3). Among the farmers who do not lose their crop, 78% reported incidence of disease and harvested 206 kg ha-1 lower yields. In gen- eral, farmers could not identify the specific diseases that affect their fields; based on observa- tions from the pathologists from the Nepal Agricultural Research Council (NARC), Stemphyllium leaf blight is the most common and damaging disease of lentil in western Nepal. As expected, our findings are consistent with economic theory that shows that the partial

production elasticities for land, labor, and capital inputs are positively associated with output, and these variables are statistically significant (P< 0.05) in both of the models. However, the magnitude of the elasticities for land, labor, and capital varies across two models. While the magnitude of elasticities for land is larger in the stochastic frontier model (0.798) than in the Tobit model (0.397), the elasticities for labor and non-labor capital are smaller in the stochastic frontier model than in the Tobit model. Seed rate and soil type had a significant positive effect (P < 0.01) on lentil productivity in both models. Yield reduction was significantly higher in the field where improved seed varieties were grown. Similarly, seeding method had significant effect on grain yield, where grain yield was significantly lower for farmers who practiced relay seeding rather than other seeding methods. Further, the model coefficient for nitrogen is positively associated with yield in the stochas- tic frontier model, while this coefficient sign is reversed in the Tobit model, likely because pro- portionally more farmers who applied nitrogen also experienced crop failure. Since this association is unlikely to be causal since applied fertilizer rates were low in all surveyed fields, PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 10 / 19

we consider estimates from production function are more reliable than those from the Tobit model. Parameters estimates for technical efficiency. The parameters for the technical effi- ciency model as specified in Eq (2) are presented in Table 3. In order to correctly interpret the Table 3. Maximum likelihood estimates of the parameters in the stochastic production frontier for lentil production in western Nepal. Variables Stochastic frontier model Tobit model Coefficient Std. error Coefficient Std. error Production frontier Lentil land (ha) 0.798��� 0.075 0.397��� 0.121 Labor (hours) 0.069�� 0.036 0.153�� 0.063 Non-labor capital (NRs.) 0.019�� 0.009 0.060��� 0.016 Seed (kg) 0.147��� 0.057 0.310��� 0.102 Nitrogen (kg) 0.040��� 0.015 -0.111��� 0.025 Phosphorus (kg) -0.018 0.014 -0.021 0.023 Potash (kg) -0.038 0.021 -0.033 0.031 Rainfall (mm) -0.400��� 0.116 -1.571��� 0.204 Waterlogging (1 = yes) -0.183�� 0.089 -0.572��� 0.147 Temperature (˚ C) -1.257��� 0.473 -3.382��� 0.857 Variety (1 = improved) -0.246� 0.132 -0.491�� 0.225 Relay cropping (1 = yes) -0.495��� 0.150 -1.180��� 0.249 Mixed cropped (1 = yes) -0.067 0.090 0.198 0.145 Tillage method (1 = tractors) 0.110 0.095 0.333�� 0.152 Diseases (1 = yes) -0.379��� 0.093 0.182 0.156 Sandy soil† (1 = sandy) 0.085 0.102 0.516��� 0.175 Clay soil (1 = clay) 0.459��� 0.100 0.632��� 0.150 Lowland (1 = lowland) 0.324��� 0.102 -0.013 0.168 Rainfall × lowland -0.221��� 0.024 -0.360��� 0.041 Constant 12.01��� 1.828 20.25��� 3.286 s2 v 0.536��� 0.052

Wald/LR ch2 [19] 701.54 357.03 Log likelihood -916.85 -1414.07 Technical inefficiency Cultivated land (ha) 0.351��� 0.101 Household size (no) -0.005 0.018 Gender of household head (1 = male) 0.017 0.169 Farming experience (years) -0.020��� 0.006 Labor availability (1 = easily available) -0.242� 0.141 Wage rate (NRs) 0.174��� 0.043 Lentil specialized farms (1 = yes) 0.859��� 0.167 Mean technical efficiency (TE) score 0.408 TE in upland (N = 261) 0.431� TE in lowland (N = 404) 0.395 No. of observations 665 793 ��� indicates significant at 1% level, �� indicates significant at 5% level and � indicates significant at 10% level. †The base category is the silt soil. TE stands for technical efficiency. Exchange rate: 1 US $ = NRs 98.6 and NRs. 102.7, respectively, for year 2014 and 2015 [86]. https://doi.org/10.1371/journal.pone.0231377.t003 PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 11 / 19

efficiency determinants, it should be noted that the dependent variable is the one-sided error term reflecting technical inefficiency. Therefore, positive signs of the coefficient indicate tech- nical efficiency-reducing factors and negative signs reflect technical efficiency-enhancing fac- tors. Our results showed that the farm holding size, years of farming experience, farm labor availability, labor wage rate, and production specialization affect the technical efficiency of len- til production (Table 3). The results showed that more experience in lentil farming and timely availability of farm labor increases technical efficiency. Similarly, technical efficiency in lentil production decreased with increases in the on-farm wage rate. Our results are plausible because when the on-farm labor wage rate increases, pro- pensity to use labor decreases due to capital constraints, which ultimately affects crop manage- ment practices and technical efficiency. In the same vein, household labor availability is positively associated with technical efficiency. We also found strong evidence that technical efficiency increases with decreasing farm size (cultivated land). Surprisingly, lentil-specialized farms are negatively associated with technical efficiency. The histogram of the technical efficiency score estimated from the stochastic frontier model showed that on average lentil producing farms are 41% efficient (S2 Fig), indicating significant scope for improvement with the caveat that factors such as drainage class are best viewed as

intrinsic field characteristics that can only partially be modified by management. In wet win- ters, technical efficiency is higher in upland ecologies (43%) than in the lowland ecologies (39%). Results from on-farm trials Multi-locational trials were conducted across all districts in the region of interest over a five- year period (i.e. crop harvests in 2012, 2013, 2014, 2015, and 2016) to explore how crop estab- lishment practices (zero tillage), local modification of drainage (bed planting), choice of lentil variety, and integrated better bet agronomic management (for example: improved variety, alternative seeding method, timely weed management, fertilizer management etc.) affect yield and yield stability across years. None of interventions or their interactions were statistically sig- nificantly, hence we combined data across all treatments to explore location and year effects. Lentil yields varied across the years and production ecologies. Across western Nepal, wet winters were experienced in lentil season of 2012–13 and 2014–15 with poor yields attained in lowland ecologies (i.e. grain yield < 300 kg ha-1) (S2 Table). However, yields in upland sites (Dadeldhura and Surkhet) exceeded 1.0 t ha-1. In the drier winter years of 2015–16 and 2016– 17, lentil yielded more than 1.2 t ha-1 in Terai ecologies, while there was no or very poor yields in the upland sites in the mid-hills (S3 Fig). All of the results from on-farm experiments indi- cated that in wet winters, lowland areas are risky for lentil production and upland areas have

high yields, a dynamic that is reversed in dry winters. Simulating Stemphylium blight disease risk in lentil Simulation results from the Stempedia model driven by historical climatic data (1985–2014) confirms that Stemphylium disease risk varies across years but that 73% of years are likely to have significant Stemphylium blight disease outbreaks in fields where inoculum is present (Fig 2A). These results are consistent with the stochastic frontier model that has shown a negative effect of diseases with lentil production (Table 3) and Stemphylium blight could be a major rea- son. Moreover, it is important to note that local differences in drainage class and soil type influence the likelihood of disease, but these factors are not simulated in the Stempedia model and the result are best interpreted as general trends as governed by weather conditions. Results also suggest that disease severity increases with delayed crop establishment, i.e., seeding after PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 12 / 19

the 1st week of November (Fig 2B). This suggests that the risks posed by Stemphylium blight can be greatly reduced through earlier planting in October. Discussion Although development efforts in Nepal have highlighted the importance of pulse crop intensi- fication to food, nutritional, and income security, our results confirm that lentil remains a risky enterprise with survey data from two seasons highlighting the prevalence of crop failures (16%), modest yields (353 kg ha-1), and low levels of profitability (US$ 33 ha-1), particularly in wet winters. Nevertheless, site factors such as drainage class influence responses with upland sites performing well in wet winters and lowland sites performing well in dry winter (S3 Fig). The dominant production area for lentil in Nepal is in the Terai plain where poorly drained conditions are common. Lentil is particularly sensitive to the direct effects of waterlogging and moisture deficit dur- ing flowering and pod-filling stage [48,78]. Although available soil moisture and rainfall are important for lentil production in South Asia, waterlogging in lentil also damages the root sys- tems thereby limiting crop capacity for water uptake when conditions become drier [79]. Per- haps more importantly, significant negative effects of high rainfall can increase disease severity, particularly for fungal diseases [80]. In our study, 78% of farmers reported significant disease incidence in their fields in the comparatively wet winters of 2014 and 2015 (Table 1)

and disease incidence had a large negative impact on lentil production (Table 3). While, some wilt-related diseases are prolific when there is drought [81], other blight and fungal diseases are more prominent when there is high moisture in the soil and the broader environment [82,83]. Due to increasing winter precipitation levels in Nepal, the fungal disease Stemphylium blight has become a serious threat [49,50]. High and stable yields of lentil are only likely to be achieved in Nepal when the threats posed by diseases mediated by winter rainfall are effectively and economically managed. Fig 2. Stemphylium blight simulations with the stempedia model with 30 years of historical weather data: A) severity across years, B) severity across years as a function of planting time. https://doi.org/10.1371/journal.pone.0231377.g002 PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 13 / 19

Although fungicides provide a plausible response strategy, they remain out of reach for most farmers due to cost and limited availability in the market. Through on-farm trials conducted across many sites in western Nepal, our study evaluated the potential role of alternative crop establishment (zero tillage), drainage modification (bed planting), and varietal choice among cultivars currently available in Nepal as possible entry points for increasing yield and yield sta- bility. Unfortunately, statistically significant gains were not associated with any of these inter- ventions. That said, work outside the region does suggest that there is significant genetic variation in lentil resistance to waterlogging tolerance [48] and Stemphylium blight that can be leveraged by bringing new lines to Nepal for testing and registration [84]. Perhaps most promising as a near-term intervention is the possibility of adjusting planting dates to reduce the risks posed by Stemphylium. Results from the Stempedia model simulations suggests that planting lentil within October can greatly minimize Stemphylium blight disease severity (Fig 2). Nevertheless, the majority of lentil in Nepal is grown in rice-based cropping system [35,36] and most of the rice varieties grown are medium to long-duration. To benefit lentil in rice-based systems, efforts are required to encourage earlier rice planting (i.e. permit- ting earlier rice harvest) and to facilitate transitions to shorter-duration rice varieties where there is an opportunity to do so while enhancing the performance of the cropping system as a

whole. Even without progress in addressing climate-based risks, our survey results suggest that len- til producing farms are only 41% efficient, with wide variability (0.2% to 85%) (S2 Fig). This indicates significant scope to increase lentil productivity and technical efficiency in Nepal through better practices, although the opportunity for efficiency gains are not uniform across production ecology gradients (e.g. drainage class). We observed a significant positive effects of farming experience on technical efficiency (Table 3), suggesting knowledge bottlenecks are constraining production. Hence, more extension efforts through public and private sector channels is likely warranted. On the other hand, limited labor availability and increasing on- farm wage rates have negative effects on the technical efficiency of lentil production and this could be the reason for negative association of lentil specialized farms with technical efficiency (Table 3). Moreover, in Nepalese context, labor out-migration has created an acute labor shortage that has affected the timely crop establishment. In this context, availability of scale- appropriate farm mechanization can be a viable option to cope with the problem of labor shortages that increases the labor wages and affect the farm productivity and technical effi- ciency. However, smallholder farmers may not be able to purchase farm mechanization due to capital constraint. They could, however, hire the mechanization services if service providers rent-out the services [85]. Hence, supporting the emergence of mechanized service provision

through small and medium-sized entrepreneurs is a scalable means to address these downward forces on technical efficiency [85]. Conclusion Since the majority of lentil in Nepal is produced in the Terai plain of western Nepal, research and development efforts should first be focused on the unique challenges and opportunities present in this region. Based on the combined insights emerging from our survey, field trial, and simulation results, gains in yield, yield stability, and technical efficiency can be made by: 1) ensuring timely lentil planting to mitigate climate-mediated disease risk, 2) evaluating new lentil lines that may provide enhanced resistance to diseases and waterlogging, and 3) encour- aging the emergence of mechanization through service provision. By addressing these founda- tional constraints, more farmers will likely be poised to adopt other good management practices. Finally, as the skill of seasonal weather forecast improves, provision of climate PLOS ONE Climate and landscape mediate low lentil productivity PLOS ONE | https://doi.org/10.1371/journal.pone.0231377 April 16, 2020 14 / 19

services information may provide a useful guide to farmers with respect to matching manage- ment intensity and investment to likely yield outcomes. Supporting information S1 Fig. Historic winter rainfall trends from selected meteorological stations in the Nepal Terai. (TIF) S2 Fig. Distribution of technical efficiency score for overall farms. (TIF) S3 Fig. Lentil yield (kg ha-1) from a series of on-farm evaluation across different ecologies and years in western region districts of Nepal from 2012/13-2016/17. (N inside the figure indicate the total number of samples included). (TIF) S1 Table. Details on agronomic practices, experiment year, district and number of loca- tions included in each district for on-farm experiments. (DOCX) S2 Table. Districts wise lentil yield and seasonal precipitation during lentil growing time (October-March) in study areas. (DOCX) S1 Data. (RAR) Acknowledgments We gratefully acknowledge the farmers for their willingness to participate in the survey and on-farm experiment. The authors would like to thank Salin Acharya, Lokendra Khadka, and Anil Khadka for their support during farm survey data collection and Cynthia Mathys for lan- guage editing of this paper. Author Contributions Conceptualization: Gokul P. Paudel, Mina Devkota, Andrew J. McDonald. Data curation: Gokul P. Paudel, Mina Devkota. Formal analysis: Gokul P. Paudel, Mina Devkota. Methodology: Gokul P. Paudel, Alwin Keil. Validation: Alwin Keil, Andrew J. McDonald. Writing – original draft: Gokul P. Paudel, Mina Devkota.