Are Lentil (Lens culinaris) Farms Productive, Profitable, and Efficient in Resource Allocation? A Cross‐Sectional Study From Nepal

The study was based on primary data from 473 lentil farmers selected randomly to analyze productivity, profitability, efficiency, and sensitivity of lentil farms in Nepal. Methods like benefit–cost, break‐even, margin safety, and sensitivity analysis, scaling technique, Cobb–Douglas type of production function, and stochastic frontier were adopted to derive farm economics, allocative, and cost efficiency levels. With average productivity of 672 kg/ha, lentil farmers in the study area were earning about 41% profit as of gross return with a profitability index of 0.78. About 45% margin of safety and estimates of benefit–cost ratio above one on all sensitivity measures is indicative of low risk and robust enterprise. Resources allocated in lentil production were found inefficient, and to achieve maximum return, expenses on land preparation, seed, nutrient, and plant protection cum irrigation should be increased by 27.6%, 80%, 33.1%, and 97%, respectively. Similarly, expenses on labor and harvesting activities need to be decreased by 30.1% and 23.6%. Labor cost and seed cost were the most important variables, and a 1% increase would surge the total production costs by 0.42% and 0.19%, respectively. The cost efficiency was estimated as 1.137 mean value, meaning that over 13.7% of the costs in lentil farms is wasted while comparing best‐practiced farm. Only about 48% of farms is fairly efficient at efficiency levels 1.0 to 1.09, but the majority is inefficient, which needs to minimize the waste of resources. Although suffering from climatic risks and production‐related problems, lentil enterprise is profitable, less risky, less sensitive, and fairly to inefficient in resource use. Wise attention is need on the part of farm management and resource utilization. Farmers are suggested to maintain farm size around 0.5 ha or below 1 ha, use only improved varietal seed, cut labor expenses with the use of machinery, and perform adequate tillage during sowing followed by effective disease management practices.

in iron, zinc, and other micronutrients supporting nutritional security to low-income people (Darai et al. 2017).With 212,876-ha area and 262,835 tons of production (MoALD 2021), Nepalese lentil accounts 4.35% of area and production globally being world's fifth largest producer (Ghimire et al. 2022).
In Nepal, lentil crop is mostly curbed to Terai districts accounting almost 90% of the total production in Nepal (CRS 2018) grown throughout the country except some Himalayan regions showing immense potentiality in the aspect of market demand and national and international trade performance.Based on soil type, irrigation availability, and farm size, lentils generally for grain and seed purposes using local home saved seed or improved seed are grown with methods like tillage, relay, sole, and mixed.In the mixed cropping method, lentil is generally mixed with rapeseed, mustard, wheat, and other winter grain legumes (Adhikari et al. 2018).Nepali lentil possessing exceptional characteristics of small size, bright pink color, unique taste, good cooking quality, and high nutritive value is importantly demanded in national as well as overseas markets.Lentils are locally grown in inadequate nutrient and rainfed farms.
Despite of higher market demand, numbers of constraints are restraining the yield and potential availability of lentils in the end market.Lacking availability and accessibility of improved seed to farmers, traditional types of production technology, high incidence of disease pests, climatic hazards, and weed infestation were the major constraints.Also, Stemphylium blight, Fusarium wilt, and root/foot rot are the major fungal disease that are seen in epidemic form; besides this, Botrytis grey mold and rust are seen in the sporadic form in lentil crop in Nepal (Pokhrel, Aryal, and Poudel 2019) causing huge loss and also crop failure sometimes.For producing lentil, almost 700,000 numbers of farmers in Nepal are of small scale using small parcels of land lacking modern production technology including post-harvest practices resulting lower yield, post-harvest loss, and low profitability (USAID 2011).In Nepal, very little attention is given to lentil crop in the aspect of resource utilization and management compared cereals and vegetables.Farmers do not give attention in the efficient use of available resources in lentil crop (Gautam et al. 2022).There exists challenge in the part of farmers in deciding farm management activities associated to the apportioning of resources (Salassi and Deliberto 2011).Also, yield of lentil crop is restricted due to biotic and abiotic factors (Sehgal et al. 2021).Achieving a given amount of the product should use the least of resources available (Ajoma, Ezihe, and Odoemenem 2016), and the resource efficiency is much concerned in the developing nations where production is low, small scale, traditionality, and high climatic risks.Productivity of any crops is largely reliant on the efficiency of resource used (Asmatoddin et al. 2009), and efficient agriculture farm produces maximum output with minimized cost and optimized resources at a given time.Farm efficiency is a key aspect in production economics, and the production system has been hindered hugely due to incapability of farmers in using their resources efficiently.With a goal to maximize the profit, allocative efficiency quantifies how near a farm is using the optimal combination of inputs (Richetti and Reis 2003) as farm efficiency is an important means for growth of output (Kuwornu, Amegashie, and Wussah 2012).As price, market demand, and climatic conditions are beyond control, farmers in the production area have no option than to struggle to maximize the use of available productive resources to achieve optimal return.Thus, the examining the farm production level, economics, resource used, and their efficiency in lentil production needs detailed investigation to derive productivity and profitability.Goni, Umar, and Usman (2013) stated that the farm improvement with efficient utilization of inputs is the most significant way of achieving higher output particularly for poor farmers who are responsible for the large consumable supply in the country (Goni, Umar, and Usman 2013).Judicious use of productive inputs in recommended quantity, quality, and time affects lentil output, which in turn exerts a momentous effect on farm income.
Being an export-prioritized commodity, the study of lentil from the aspect of farm economics, resource allocation, and cost efficiency has not been explored much, but several studies on biological research were found done.In this backdrop, this study critically examines the profitability, sensitivity, allocative efficiency of inputs used, cost efficiency levels, motivating factors, and constraints in lentil production.Investigating the cost efficiency levels and allocative efficiency of resource-used in the Nepalese lentil farming system is a strength of this study.Results of this study would provide farmers knowledge about the utilization pattern of the resources in lentil farm and their adjustment needed in the current production system enhancing productivity, profitability and efficiency in the future.It also guides related stakeholders and policymakers to reallocate programs and budget knowing the production performance and efficiency level of lentil farms.Aligning the background context to conclusion, this paper follows the logical structural pattern of methodology, results, discussions, and conclusion with appropriate recommendations.

| Gross Margin and Benefit-Cost Analysis
Analysis of gross margin is generally used to evaluate the capability of farms to allow justifiable decisions and is one of the methods of estimating cost-effectiveness of small enterprises (Olukosi, Isitor, and Ode 2006).In this study, total cost accounts for the sum of all the variable costs including costs on labor, land preparation, organic manure, chemical fertilizers, disease pest management (plant protection), irrigation, harvesting, and postharvest activities.
Gross Margin = Gross farm income − Total Variable cost.Here, total variable cost is the sum of all variable inputs, and gross farm income represents market value as per produced lentil.
Likewise, farms' benefit-cost analysis is the easiest method estimated using ratio of gross return to total variable costs for estimating farm's economic performance.For easiness in interpretation, profit per kilogram and profit margin on lentil production were also calculated.
The profitability index measures how efficiently the lentil farm utilized its total costs, which covered the investment to produce revenue.As followed by Sharma et al. (2016), the profitability index was estimated as

| Break-Even and Margin Safety
Break-even analysis of the farm enterprise is used to estimate the least price and output level allowing firm in the state of neither profit nor loss.It is applied to estimate the value point where price and outputs are sufficient to cover exact production related costs (Cook et al. 2012).Condition when a unit farm gate price is higher than a break-even price, the farm is in economic profit (Abera et al. 2019) and was calculated by using the formulas: Also, following Singh, Bhatt, and Kiran (2020), margin of safety for lentil production was calculated to assess the level of risk, which specified the amount of revenue that can be dropped before the beginning of loss and higher value of margin of safety indicates lower risk of production and is an indicative of a firm's strength (Ara et al. 2020).

| Sensitivity Analysis
In order to determine the rate at which farm's profitability is probable to be affected by various variations in the key parameters like cost price and yield, farm sensitiveness analysis was conducted (Baksh 2003).It is significant to know that the degree of farm performance level is sensitive to changes regarding to simplifying assumptions made (Hasan 2008) Following Prajneshu (2008), in this study, due its wider applicability and convenient in the aspect of comparing the partial elasticity coefficient, the extended form of the Cobb-Douglas production function was used as described below: The above-mentioned function was log transformed as where Y = the gross revenue from lentil (NRs), X 1 = labor cost (NRs), X 2 = land preparation cost (NRs), X 3 = cost on seed (NRs), X 4 = cost on nutrient management (NRs), X 5 = plant protection cum irrigation cost (NRs), X 6 = harvesting and post-harvest cost (NRs), u = error term, a = intercept, e = natural logarithm base, and β1, β2, … …, β6 were coefficients of respective independent variables.The elasticity of production as a response of output to a change in inputs (Umar and Abdulkadir 2015) and RTS as a measure of success of farm in producing maximum output level from a given inputs combination (Ojo, Salami, and Mohammed 2008) was estimated by summing coefficient of independent variables used in the model as RTS = β1 + β2 + β3 + β4 + β5 + β6.

| Allocative Efficiency Analysis.
Resource allocative efficiency used in the production process of lentil was estimated by the ratio of marginal value product (MVP) to marginal factor cost (MFC)/price per unit input of each variable inputs with respect to estimated regression coefficient.The efficiency of resources allocated (r) was calculated as r = marginal value product/ marginal factor cost (Iheanacho, Olukosi, and Ogungbile 2002;Rahman and Lawal 2003).
Where MVP = MPPx i × P y and marginal physical product (MPP) of inputs was estimated using the geometric means of the variable inputs (Puozaa 2015).
And MPPx i = d y /dx i = bi ͞ y/X ͞ I, where bi = estimated regression coefficient of input Xi, ͞ y = geometrical mean value of output, and X ͞ i = geometrical mean value of input used.For MFC, prevailing market price of inputs was used as MFC = Pxi (Unit price of input xi).
The basis of estimation for allocative efficiency as a rule that "the slope of the production function (MPP) should equal the inverse of input price at profit maximization point" (Ellis 1998), Break − even price (NRs∕kg) = Total production cost (NRs∕ha) ∕Total production (kg∕ha), Break − even yield (kg∕ha) = Total production cost (NRs∕ha) ∕Selling price (NRs∕kg).
Margin of Safety (MoS) = Revenue at output − Revenue at break − even point.
such that the decision-making rule for the efficiency analysis is described as efficiently used (r = 1), underutilization of resource (r > 1), and overutilization of resource (r < 1).

| Percentage Adjustment in MVP.
The percentage adjustment rate of MVP of each input fitted in the model is vital to estimate in order to acquire value for optimum resource allocation, that is, MVP = MFC.Following Ghimire and Dhakal (2013), the percentage adjustment in MVP of each input was estimated using the following equation: where D is an absolute value of percentage change in marginal value product of each resources (Mijindadi 1980).

| Diagnostic Tests
In econometrics' theory and analysis, serious problem of multicollinearity and heteroscedasticity has been potentially observed (Emmanuel and Maureen 2021).To avoid statistical error on the regression coefficient and correlation, diagnostic test for normality, multicollinearity, and heteroscedasticity on production function-related empirical estimation is an important part of the research analysis (Khanal, Lohani, and Khanal 2022).Variance inflation factor (VIF) test was done to detect multicollinearity where the VIF value larger than 10 exhibits a multicollinearity problem in the data (Gujarati 2004).Also, Breusch-Pagan/Cook Weisberg tests were done to assess heteroscedasticity.Histogram was performed for normality, and RAMSEY RESET test was done to assure omitted variables in the model.

| Stochastic Cost Frontier for Cost Efficiency Analysis
Following Battese and Coelli (1955), stochastic frontier model of cost function was used to analyze the cost efficiency level as follows: Here, g is the suitable functional and Cobb-Douglas in this study, C i represents total cost of production, P i represents the vector variable of input prices, Y i represents value of lentil produced (kg), α is the parameter to be estimated, V i is the random disturbance cost due to the factors beyond the scope of farmers, and U i is one-sided disturbance form representing cost inefficiency and is independent of V i .Thus, U i = 0 implies for a farm whose cost lies on the frontier, U i > 0 farm cost above the frontier, and U i < 0 for farm identically and independently distributed as N(0, σ 2 v).
Again, following estimation model from Ogundari and Ojo (2007), this study used the Cobb-Douglas stochastic frontier with the stochastic frontier cost function model as where C = total production cost, X 1 = labor costs, X 2 = land preparation costs, X 3 = seed costs, X 4 = cost of nutrient, X 5 = cost of plant protection and irrigation, X 6 = costs on harvest and postharvest activities, X 7 = lentil output (kg/ha), and V i represents statistical disturbance term and U i as specific characteristics of farmers and environment related to cost inefficiency.
Furthermore, given the available technology, the ratio of the observed to the corresponding minimum cost is defined as the cost efficiency level of individual lentil farm in this study and expressed as where C b is the actual production cost representing observed cost and C min is the least cost level or the frontier total production cost.Cost efficiency upon estimation takes the values within 1 or higher, and the farm with the value 1 represents cost-efficient farm (Ogundari, Ojo, and Ajibefun 2006).

| Scaling Technique
For identifying major motivating factors and problems of lentil production, indexing method was used based on the response frequencies.Following Ghimire, Dhakal, and Sharma (2016), influencing factors and problems were ranked by 8-point scale method using scale values.Weighted average mean was used to calculate the index value for each statement variable in order to rank by using the following formula: where scale values were taken as 1, and n represents categories in ranking, S i is the scale value, f i is the farmers responses frequency, and N is the total sampled respondents.

| Data and Descriptive Statistics
Top four lentil producing districts in Nepal (Figure 1) were purposively selected for the study sharing 42.5% and 43.9% of total area cultivated and total production at Nepal (MoAD 2018).
Following face-to-face interview method, cross-sectional data were collected in 2022 from 473 lentil farmers using semistructured questionnaire that were pretested.The date collection instruments used for this purpose were developed based upon coordination schema, and their validation and reliability were assured conducting piloting survey, pre-testing method, and data triangulation technique.The potential researcher bias was removed using trained enumerators for household survey, and triangulation of data was done by researcher conducting focus group discussion, key informant surveys, and random surveys.
The author has also taken the consent from the farmers before survey so that no any personal identifiable information will be disclosed and collected data would be used only for academic purposes.
To select the desired sample size, simple random sampling method was used, and the size was calculated using Cochran's (1963) formula n 0 = Z 2 pq/e 2 at 95% desired confidence level and sampling error 0.05.For this study, the total sample size interviewed was 473 comprising 116, 119, 118, and 120 from Kailali, Bardiya, Dang, and Rautahat districts, respectively.
The detailed socio-economic and descriptive characteristics of sampled lentil farmers are presented in Table 1.Among the total households under study, about 68% was headed by male, and the average age of household head was about 49 years with only 3.38 mean years of education.In the study area, most of the lentil farms were small scale.About 52% farmers were involved in organization, 63% was aware of post-harvest loss, 21% has received training related to lentil production, and only 32.7% has their own transport facility.About 74% farmers have access to credit, and 37% was using improved seed.Average family size in the study area was 7.04 having 4.73 economically active members in the household.Majority of the lentil farmers were highly experienced in production (19.24 years).Mean land ownership and mean land area under lentil cultivation were 1.1 ha and 0.38 ha, respectively.The lentil farms were found somehow accessible to technical source and the average distance was 4.68 km.Break-even analysis resulted that to attain the profit level of lentil, farm should produce more than 397 kg of lentils per hectare and receive a market price above 69.66Rs.At this point, current lentil production system can resist a large drop in yield (by 40.8%) and price (by 24.4%) before incurring any losses.Around 45% margin of safety in lentil production is indicative of a strong enterprise with low risk stating as long as sales value does not decrease by more than 45%, and there will not have the risk of suffering a loss.

| Sensitivity Analysis
The sensitivity analysis examined the impact of changes in cost, yield, and price on the existing economic performance of lentils.Benefit-cost ratio of producing lentils on all sensitivity measures remain above 1 from 1.62 to 1.16, indicating robust economic performance against identified risks and assumptions (Table 3).Results revealed that lentil enterprises are less sensitive to an increase in cost but higher sensitive to a decrease in yield and farm gate price as a 10% decrease in both resulting 46% reduction in gross margin.A further reduction in prices by 20% reduced the benefit-cost ratio to 1.42 and profitability index to 42.5% from the base of 78% implying that a slight change in yield and price both can have a significant impact on the enterprise profitability.As farmers ranked climatic hazards and incidence of disease and pests favoring low yield (Table 9), taking into consideration to this, a sensitivity test is performed by assuming a 30% reduction in yield resulted benefit-cost ratio of 1.25 with a profitability index of 24.7%.

| Estimated Results
From the Cobb-Douglas Production Function

| Estimation of Production Elasticity of Inputs in Lentil Production
Independent variables that were included in the model elucidated output variation with F-value of 102.62 significant at 1% level showing good explanatory power.R-squared value of 0.56 indicates that 56% of the difference in gross income from lentil was explicated by the independent variables included in the model (Table 4).
All variables that were included in the model were found with positive coefficients and were statistically significant except for variable cost on nutrient management.Thus, ceteris paribus, increase in labor cost, land preparation, seed, nutrient management, plant protection cum irrigation, harvesting, and postharvest by 100% would cause an increase in gross return from lentil business significantly by 19%, 15%, 41%, 2.1%, 2.2%, and 8%, respectively.
Summing the coefficients of the independent variables yields a scale elasticity of 0.85 indicating that lentil production function exhibits decreasing returns to scale <1 (Table 4) and depict enormous potential to increase their lentil production.The

| Allocative Efficiency of Productive Inputs
Study resulted that majority of variables that were included in model were underused except labor cost and cost incurred in harvest and post-harvest related activities, which were overutilized.As presented in Table 5, the expenses on land preparation, seed, nutrient management, and plant protection cum irrigation have an allocative efficiency coefficient (r) of 1.38, 5.21, 1.49, and 46.89 respectively, meaning underutilization of these resources and with the increment in utilization of these resources would result in an efficient allocation that may optimizes profit in lentil.Also, lower yield was observed in the study area compared to national average.Mbanasor and Kalu (2008) reported that lower level of productivity is achieved with inefficient resource allocation and use.Under use of this productive inputs by lentil farms might be due to timely unavailability, low purchasing power, poor technical know-how, and lack farm managerial skills with regard to the best agronomic practices and farm management.Further, the expenses on labor and harvest as well as postharvest activities were overutilized with efficiency coefficients 0.77 and 0.81, respectively, indicating that deduction in their expenses will optimize profit in lentils.Lacking mechanization and a labor-based farming system might have cause overutilization of these resources.

| Percentage Adjustment in MVP of Resources
Resources were not optimally utilized in the case of lentil cultivation in the study area, and to achieve maximum return, the model revealed that expenses on land preparation, seed, nutrient, and plant protection cum irrigation should be increased by 27.6%, 80.8%, 33.1%, and 97.9%, respectively.About 80% increment in seed expenses has suggested farmers to expense more money to purchase improved high yielding seeds to enhance farm yield and profitability instead of using locally available home-saved seed.Similarly, expenses on labor and harvestingrelated cost should be decreased by 30.1% and 23.6%, respectively, to achieve optimized resource allocation and return.In line with this finding, Gautam et al. (2022) reported underused efficiency of seed, farm yard manure, land preparation, disease pest management, and harvesting, whereas expenses on labor were reported as an overused resource in lentil production in Nepal.

| Diagnostic Tests
To confirm multicollinearity in the model, VIF test was done.Regression analysis result depicted that all the 6 predictors have VIF less than 10 with a mean VIF of 2.15 and a maximum 3.26 confirming that the model does not exhibits a serious problem with multicollinearity as Adnan, Ahmad, and Adnan (2006) mentioned that VIF above 10 was considered problems of multicollinearity in the dataset.A small chi-square value of 1.31 and prob > chi 2 = 0.2518 (p > 0.05), indicating that there was no problem of heteroscedasticity.Again, RAMSEY RESET test was performed and resulted F(3, 463) = 2.28; prob > F = 0.0786 (p > 0.05) indicating that there has no any omitted variables in the model and a linear regression model is sufficient to explain this input-output relationship.Also, the dependent variable total return (gross farm income) from lentils showed normality in the histogram.The test results of all the diagnostics tests performed are presented in Table 4.

| Results from Stochastic Cost Frontier
This research work used stochastic cost function with Cobb-Douglas model and maximum likelihood estimates (MLE) as an estimator.MLE of the parameters of stochastic cost frontier model under the half-normal distribution are presented in Table 6 using computer program STATA 14.The sigma-square value 0.026 showed the distribution of error term of inefficiency (u i ) and which is very small to its normally distributed or, in other words, the value of σ 2 u < 0. With this result, it can be concluded that there is no any evidence that all the farming done by the farmers is 100% efficient (if σ 2 u = 0, means that all farming system done by farmers is 100% efficient).The variation of production contributed by cost inefficiency amounted to only 2.26%.
All the independent variables in the model were with positive and significant coefficients indicating that these variables are the most significant determinants of lentil production.The cost elasticity of production (coefficient of the cost function) explained 1% increment in cost of labor will increase total cost of production by approximately 0.42%.Among the variables, labor cost is the most important variable with higher coefficient value.Abbreviations: GM = geometric mean; MVP = marginal value product; MFC = marginal factor cost; r = efficiency ratio; OU = overutilized; UU = underutilized.
Likewise, 1% surge in cost of land preparation, seed use, nutrient management, plant protection cum irrigation, and harvesting will increase the total cost of lentil approximately by 0.18%, 0.19%, 0.009%, 0.006%, and 0.14%, respectively.Further, 1% increment in the output (lentil) will increase the total cost of production by 0.031%.
Farmers' distribution with mean area, cost, and yield and cost efficiency scores for lentil farms in the study area are presented in Table 7.The estimated mean cost efficiency level of lentil farm was 1.137 ranged between 1.0 and 2.1, meaning that an average lentil farm incurred costs that are about 13.7% above the minimum cost defined by the frontier.This also implies that over 13.7% of farm's cost are wasted when compared with the best practiced farms producing the same output (lentil) and facing the same technology.Further, this result revealed that on an average, 13.7% of the cost incurred can be avoided without any loss in total output.Above 1, the higher the value of cost efficiency level, the more inefficient farm and the farmer (Dia and Zalkuwi 2010).Also, Ghimire et al. (2023) mentioned that overall improvement in technical efficiency will lead to a good cost-efficient direction, which will help to enhance the farmers profit in their study about technical efficiency of lentil production in Nepal.
In this study, the frequencies of the predicted level of cost efficiency between 1.0 and 1.04 representing 22.4% and another 25.16% between 1.05 and 1.09 indicate that about 48% of the lentil farms is fairly efficient in producing at a given level of output.Ogundari, Ojo, and Ajibefun (2006) in their study in Nigeria also mentioned and categorized fairly efficient maize farms (about 83%) with cost efficiency levels in between 1.0 and 1.1 and explained fairly efficient farms are those reflects tendency to abate resource wastage associated with the production process from cost perspective.In the study area, fairly efficient lentil farms (22.41%) with efficiency level in between 1.0 to 1.04 are producing 710 kg/ha of lentil in mean area 0.36 ha from total cost 36,129 NRs/ha, as result specifies that the majority of farms were cost inefficient in the study area and need to minimize the waste of resources associated with lentil production process.

| Influencing Factors and Production-Related Problems in Lentil
Based on farmers perception and rank, higher return from lentil cultivation was found most important influencing factors of lentil farming with index value 0.84 followed by high market demand (0.82), nutritive food (0.67), profitable than other crops (0.65), maintain soil fertility (0.57), land suitability (0.49), adaptation to climate change (0.32), and support from government (0.19).Farmers opinioned government support as a least motivating factor for lentil cultivation (Table 8).Similar to this result, Gautam et al. (2022) reported good return from the lentil cultivation was the most decisive factor with index value 0.784 followed by the high market demand.Similar to this finding, Dhakal (2021) also reported that disease Stemphylium blight is a most serious which may cause loss up to 100% in lentil.Also, Gautam et al. (2022) mentioned lack of technical knowledge was the major problem in lentil production in Nepal.

| Conclusion and Recommendations
Findings from this study have provide an economic strength and viability of lentil production system.In summary, lentil production system showed low productivity but higher profit margin due to higher price and higher market demand.The higher cost of production was shared by labor indicating laborintensive production technology lacking farm mechanization.The higher benefit-cost ratio in all sensitivity measures and a good percentage of margin of safety in lentil production are indicative of low risk and robust enterprise against identified risks.
Although the lentil farms are operating at decreasing RTS, the significant and higher elasticity of production inputs indicated their importance and contribution to lentil production system  The result from cost efficiency analysis can be summarized as production cost is highly influenced by the expenses in labor followed by seed, land preparation, harvesting, and nutrient management.The average cost efficiency level was 1.137, and among the sampled farmers, only 48% (efficiency level 1.0 to 1.09) is fairly efficient indicating that the majority of farms are inefficient and need to minimize the waste of resources associated with lentil production.Although lentil farms are suffering from climatic risks, higher disease incidence, and production-related problems, the conclusion can be made that lentil farmers are getting reasonable profit and enterprise is profitable, less risky, less sensitive, and fairly cost-efficient from the point of economic performance but wise attention is needed in the part of farm management and resource utilization.Based on the findings from this result, the following recommendations can be listed: • Package of practices and technologies should be transferred to already established lentil production pockets rather than increasing its area.Adopting labor-saving technologies and farmer's capacity development is suggested.
• The government should raise awareness among farmers and also provide access to subsidized high-yielding improved lentil seed as seed significantly contributed to gross return.Other inputs like fertilizers, farm machinery, and frequent quality technical services to farmers should be provided.
• Establishing a disease pest alert system with a provision of a rapid response technical team, an early warning system for climate-related hazards, and actions for adaptation to changing climate is very urgent for the lentil production pockets.
• Farmers are suggested to maintain lentil farm size around 0.5 ha or below 1 ha, use only improved varietal seed, cut labor expenses with the use of machineries, and perform adequate tillage during sowing followed by effective disease management practices.
This study was more confined with only lentil production efficiency and profitability confined in Terai region representative four districts.Lentil farm efficiencies in Nepal from the perspective of cultivation methods, soil and environment, climate change and farmers knowledge were the area of future research.
Gautam et al. (2022)e per kilogram sold lentil with an average benefit cost ratio 1.78 and profitability index 0.78.This figure indicates that the lentil-producing enterprise is a profitable business, which resembles with the findings ofGautam et al. (2022)reported as 28% to 36% cost shared by labor and benefit-cost ratio of 1.91, 2.23, and 2.14 for lentil seed producer, improved seed user, and grain producer, respectively, in Nepal.Also, CRS (2018) reported 1.7 benefit-cost ratio in lentil production in Nepal.

TABLE 3 |
Sensitivity analysis on profitability of lentil farms.

TABLE 4 |
Gautam et al. (2022)ated coefficients value for lentil (Cobb-Douglas production function).revealedthatexpenses on all the variable inputs if added by 1% would increase the output level by 0.85%.Here, output additional proportion will be smaller than input additional proportion.From the very past, land degradation and decreasing soil fertility over time due to improper nutrient management and extensive cultivation with less adoption of efficient technology might have caused decreasing RTS in production.Similar to this result,Gautam et al. (2022)resulted decreasing RTS in lentil production in Nepal.

TABLE 5 |
Estimated inputs allocative efficiency level in lentil production.

Table 9
Incidence of disease ranked as the second most serious problem (0.74) followed by unavailability of improved varieties (0.73), lack of government support (0.57), and lack of improved technology (0.55).Major disease reported by lentil farmers in the study area was Stemphylium blight, wilting, and root rot.

TABLE 6 |
Estimated Stochastic cost frontier for lentil production.

TABLE 7 |
Cost efficiencies of lentil farms and farmer's distribution with mean area, cost, and output.

TABLE 8 |
Perception ranking on factors influencing and problems in lentil cultivation (n = 473).

TABLE 9 |
Perception ranking on production problems in lentil cultivation (n = 473).Inefficiency in resource allocation suggests a presence of knowledge gap on farmers and weak agriculture extension service delivery by the government.