1 Introduction

Potato (Solanum tuberosum) is a starchy food and a root vegetable native to the Americas. It is perennial plant belonging to the family Solanaceae. Potato is fifth major crop of Nepal in terms of coverage and second in the terms of production with total production of 3,521,794 tons in 2023 (MoALD, 2023/24). In Rasuwa, potato was cultivated in 2,629 ha area with production of 42,466 tons (MoALD, 2023/24). Growing scope of potato in the form of vegetables, fries, chips and other foods has provided a motivation to cultivated potatoes in rural areas of Nepal. Maize is a rich source of carbohydrates, fiber, vitamin B, minerals (mg, P) belonging to the family Poaceae.

Solo cropping and mixed cropping are two prevalent agricultural practices with varying implications on crop yield and economic returns. Solo cropping, where only one crop is cultivated in a field, has been a traditional practice in the region. However, mixed cropping, involving the simultaneous cultivation of two or more crops in the same fields, has gained attention for its potential benefits such as increased yield stability, soil fertility and income diversification. Rasuwa District, is known for its diverse agricultural practices and challenging terrain, relies heavily on agriculture for livelihoods, with Potato and Maize being key crops cultivated by local farmers. Maize is most compatible crop with potato for their contrasting phenology, Maize is widely spaced crop and there is ample scope to grow short duration intercrops in the interspaces (Begum et al., 2016). The maximum advantage in terms of yield and Land Equivalent Ratio can be obtained from potato and maize inter-cropping when both the crops are planted with 75% of their normal population, Net profit and benefit: cost ratio were also maximum under this treatment (Fan et al., 2016). Potato intercropped with maize can reduce the population and the damage of PTM by enhancing the number of parasitoids and parasitism rate (Zheng et al., 2020). On sloping land, the maize and potato intercropping can decrease the surface runoff and the soil evaporation, increase the soil moisture content and increase the yield of crops (Fan et al., 2016). In low Potato yielding areas, the intercropping is beneficial but when planting 60% Maize only caused more yield reduction of potato due to exhaustive crop and shade during growth (Farooq et al., 1995).

In Rasuwa district, farmers faced challenges in optimizing crop production practices due to a lack of comprehensive economic analysis. However, limited data on profitability, cost differences, yield, and market dynamics hindered farmers' ability to make informed decisions. So, the study was conducted to assess the yield variation of solo and mixed potato cropping, to analyze the resource efficiency and income generation in Rasuwa district, and to assess the socioeconomic traits of farmers. The study aimed to provide clear insights into the profitability, cost implications, and yield differences between the two practices, enabling farmers to make informed decisions. The study end with two innovations - household-level cost-benefit and regression-based evaluation, and provision of transferable economic evidence for rainfed hill regions.

2 Materials and Methods

2.1 Site selection

The research was be conducted in Rasuwa district of Nepal. Its area is 1,544 km². Its territory ranges from 614 to 7,227 meters above mean sea level. There are 5 rural municipalities in Rasuwa. The specific municipality in which the study will be carried out in Kalika municipality and Gosaikunda municipality. The site was chosen due to its ideal climatic conditions, availability of more farmers, and inclusion in the PMAMP potato zone.

2.2 Preliminary survey

The survey research was stared with a preliminary survey to assess the feasibility of the research, gathering details about the demographics and sociocultural aspects of the study area. Survey conducted informal interviews with farmers to gain insight into the potato and maize zone from various angles, helping to build rapport and design the questionnaire effectively. To determine sample, guidance from agriculture officials.

2.3 Sample and sampling techniques

To ensure a comprehensive study, this research was focused on potato and maize growers in Kalika and Gosaikunda municipalities. 90 farmers were selected by simple random sampling technique to draw a representative sample from the sampling frame of 900 farmers who were practicing both solo potato and mixed potato with maize cropping system.

2.4 Source of data

Primary data were collected through face-face interview schedule, key informant interviews, focus group discussions, field observations, market research, and direct interactions with farmers, traders, and consumers. Secondary data was gathered from various sources like ADS, MoALD, ADO of Nuwakot, annual reports, Central Bureau of Statistics, Nepal Agriculture Research Council, Journal articles, PMAMP, and online resources.

2.5 Data collection techniques

A simple random sampling technique selected 90 farmers from a sampling frame of 900 growers practicing both solo potato and mixed potato-maize systems. Primary data were systematically collected via a pre-tested, face-to-face structured household survey for quantitative analysis, supplemented by Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs) with stakeholders (e.g., local experts, agriculture officers) to gather comprehensive qualitative context. Secondary data were sourced from national agricultural bodies (e.g., ADS, MoALD, NARC).

2.6 Pretesting of questionnaire

Pretesting of questionnaire was performed with 5 farmers from each municipality before actual household survey to identify any potential issues with question clarity, respondent understanding, or survey flow. This process helps to refine the survey instrument before full scale implementation, ensuring data accuracy and reliability. Respondents involved in pretesting were not included in final household survey.

2.7 Data analysis techniques

2.7.1 Analysis of socio- economic data from the survey

The survey’s socio-economic data, including both qualitative and quantitative information, was analyzed using software like SPSS and Microsoft Excel.

2.7.2 Economic analysis

The analysis of production costs, gross margins, gross income, benefits-cost ratios, etc from potato production in the study area are all included in this section.

2.7.3 Cost of production

For the purpose of analysed the cost of production, only the variable cost components for potato and maize production were considered. Seeds, organic manure, chemical fertilizers, micronutrients, pesticides irrigation, manpower, and tractor power were among the variable cost elements. The total cost of production was determined by adding up all variable input costs.

2.7.4 Benefit-cost analysis

It is the ratio between the gross return and total cost of any business. It provides information about the investment done on the resources will give a profitable return or not.

Mathematically, B/C ratio= GR/TVC

Where, B/C ratio= Benefit cost ratio, GR= Gross revenue, TVC= Total variable cost

2.8 Logistic regression

In order to identify the main socioeconomic determinants affecting access to subsidies, logistic regression analysis was performed for data analysis and to determine the main factors affecting access to subsidies, such as gender, cooperative membership, farm size, and educational attainment. A binary logistic regression analysis (Tranmer and Elliot, 2016) was used to find out the odds of determinants of receiving subsidies. The following model was used to find the variables:

P(Y) = β0 + β1X1 + β2X2 +, ..., + βnXn

Where; X1, X2, ..., Xn are explanatory variables; β0, β1, β2, βn are unknown factors; and P(Y) represents the chance that Y will occur.

3 Results

3.1 Socio-demographic characteristics of study area (a)

The survey was conducted among 90 farmers out of which 50 farmers were small producers and 40 farmers were larger producers. The results showed that the overall average age of participants was 49 years, with small producers averaging 44 years and large producers at 46 years (Table 1). The standard deviation for the overall age was 17.72, indicating considerable variability within the population which was not statistically significant (p=0.577), suggesting that age distribution did not differ markedly between small and large producers.

Table 1 Socio-demographic characteristics of study area (a) Note: *** indicate significant at 1% level

The average number of family members was 7, with small producers having 7 members and large producers averaging 8. The overall standard deviation was 2.79, reflecting moderate variability in family size. The mean difference of 0.24 was not significant (p = 0.682), indicating that family size did not differ significantly between the two groups. The overall average dependency ratio was 0.45, with small producers slightly higher at 0.46 and large producers at 0.43. The standard deviation of 0.19 suggested low variability in the dependency ratios among participants. The mean difference of -0.03 was not statistically significant (p=0.450), indicating comparable dependency levels across small and large producers.

A notable difference was observed in the amount of irrigated land, with small producers averaging 3.21 ropani and large producers averaging 11.72 ropani, leading to a significant mean difference of 8.51 (p<0.001). The overall standard deviation was 6.63, highlighting substantial variability in land ownership, particularly among large producers, who had a standard deviation of 6.86.

The average rainfed land for small producers was 4.26 ropani, while large producers had an average of 8.72 ropani, resulting in a significant mean difference of 4.46 (p<0.001). The overall standard deviation of 6.32 indicated considerable variability in rainfed land holdings, with small producers exhibiting lower variability (3.827) compared to large producers (7.84).

Total land area further illustrated significant disparities, with small producers averaging 7.60 ropani and large producers at 20.33 ropani, resulting in a significant mean difference of 12.72 (p < 0.001). The standard deviation of 7.57 indicated considerable diversity in land ownership, with small producers showing lower variability (3.458) than large producers (4.84).

The average male employment rate was 3.02, with small producers at 2.94 and large producers at 3.12. The overall standard deviation of 1.64 indicated moderate variability, while the mean difference of 0.18 was not significant (p = 0.600), suggesting comparable employment levels for males in both groups. The average male unemployment rate was 1.28, with small producers experiencing 1.38 and large producers 1.15. The standard deviation of 0.90 suggested low variability in unemployment rates, and the mean difference of -0.23 was not statistically significant (p = 0.230), indicating similar unemployment levels across the two groups. The average female employment rate was 2.17, with small producers at 2.22 and large producers at 2.10. The overall standard deviation of 1.08 reflected moderate variability, and the mean difference of -0.12 was not significant (p = 0.604), showing comparable female employment rates between small and large producers.

3.2 Socio-demographic characteristics of study area (b)

The analysis of the socio-demographic variables revealed key insights into gender, ethnicity, religion, education, types of agriculture, annual family income, and major economic activities among participants (Table 2). The overall male representation was 61.11% and female 38.89%, with no significant differences found (chi-square = 0.058, p = 0.809). Ethnic groups showed no significant differences either, with Brahmin/Chettri at 58.89% and Janjati at 41.11% (chi-square = 0.036, p = 0.848). In terms of religion, Hindu participants comprised 58.89% and Buddhists 41.11%, with no significant difference (chi-square = 0.387, p = 0.533).

Table 2 Socio-demographic characteristics of study area (b) Note: *** indicate significant at 1% level

The level of education was notably diverse, but primary education showed no significant difference (chi-square = 2.596, p = 0.627). A significant finding emerged in the types of agriculture, where subsistence farming was predominant among small producers (52.00%) compared to only 10.00% among large producers, resulting in a highly significant chi-square value of 23.621 (p < 0.001).

Regarding annual family income, a significant difference was noted, with the majority of small producers (70.00%) earning less than 2.5 lakh, contrasting with only 15.00% of large producers; this yielded a significant chi-square of 32.304 (p < 0.001). Major economic activities primarily revolved around agriculture (74.44%), but no significant differences were found in mixed activities (chi-square = 5.818, p = 0.213).

Overall, significant differences were observed at the 1% level for types of agriculture and annual family income, while other parameters did not reach significance. In average 16.67% had received training, while 83.33% had not. Breaking it down by producer size, small producers 10% had received training, compared to large producers 25%. This p-value suggests that the difference in training received between small and large producers not statistically significant.

3.3 Comparison of costs for various agricultural inputs between small and large producers for mixed cropping (per hectare)

The analysis revealed differences in costs associated with various inputs between small and large producers (Table 3). Overall, the average mixed mini-tiller cost was 750 NRs/ha (SD=1,605.79), with small producers averaging 739 NRs/ha (SD=978.73) and large producers at 759 NRs/ha (SD=1,980.13), showing no significant difference.

Table 3 Comparison of costs for various agricultural inputs between small and large producers for mixed cropping (per hectare) Note: *** indicate significant at 1% level

For mixed bullock costs, the overall average was 14,577 NRs/ha (SD=7,355.06); small producers spent an average of 17,042 NRs/ha (SD=7,874.74) while large producers spent 11,497 NRs/ha (SD=5,293.36), with a statistically significant. The average mixed organic manure cost was 65,048 NRs/ha (SD=36,679.25), with small producers at 75,819 NRs/ha (SD=44,586.32) and large producers at 51,585 NRs/ha (SD=15,424.48), which was significant difference.

For mixed chemical fertilizer, the overall average was 19,711 NRs/ha (SD=6,968.24), with small producers at 19,594 NRs/ha (SD=8,309.16) and large producers at 19,857 NRs/ha (SD=4,901.36), showing no significant difference. The maize seed cost averaged 12,544 NRs/ha (SD=4,774.77), with small producers at 10 787 NRs/ha (SD=2,380.97) and large producers at 13,949 NRs/ha (SD=5,688.70), showing a significant difference.

For mixed pesticide costs, the overall average was 3,073 NRs/ha (SD=1,960.62), with small producers spending 3,758 NRs/ha (SD=2,299.62) and large producers 2,217 NRs/ha (SD=878.61), showing a significant difference. The mixed potato tuber cost averaged 24,320 NRs/ha (SD=13,527.46), with small producers at 21,035 NRs/ha (SD=4,838.09) and large producers at 26,948 NRs/ha (SD=1,725.39), showing no significant difference.

For mixed irrigation costs, the overall average was 5,210 NRs/ha (SD=5,143.46), with small producers at 7,591 NRs/ha (SD=5,884.01) and large producers at 2,235 NRs/ha (SD=728.95), with a significant difference. The mixed transportation cost averaged 2,504 NRs/ha (SD=609.29), with small producers spending 2,231 NRs/ha (SD=285.33) and large producers at 2,722 NRs/ha (SD=707.31), with a significant difference. The mixed storage cost averaged 1,075 NRs/ha (SD=861.84), with small producers at 716 NRs/ha (SD=623.83) and large producers at 1,362 NRs/ha (SD=922.12), showing significant difference.

Finally, mixed labour requirement costs averaged 44,526 NRs/ha (SD=26,975.43), with small producers at 27,991 NRs/ha (SD=10,097.99) and large producers at 57,755 NRs/ha (SD=28,961.42), showing significant difference. Overall, small producers generally incurred higher costs for organic manure and labours, while large producers had higher costs for several inputs like transportation and irrigation.

3.4 Compared the costs of various agricultural inputs of solo cropping

The analysis showed that the overall average costs for various inputs (Table 4). For solo mini-tiller costs, the overall average was 875 (SD=1,972.02), with small producers averaging 758 (SD=999.61) and large producers averaging 968 (SD=2,499.61). The solo bullock cost had an overall average of 14,453 (SD=7,749.05), with small producers at 16,818 (SD=8,598.00) and large producers at 11,497 (SD=5,293.36); showing significant difference. The solo organic fertilizer cost averaged 56,742 (SD=28,928.84) overall, with small producers averaging 65,295 (SD=34,639.37) and large producers at 46,051 (SD=13,829.39), also showing significant difference.

Table 4 Compared the costs of various agricultural inputs of solo cropping Note: *** indicate significant at 1% level

For solo chemical fertilizer costs, the overall average was 15,266 (SD=5,671.77), small producers at 14,410 (SD=6,146.34), and large producers at 16,335 (SD=4,882.31), showing no significant difference (p=0.110). The average for solo potato tuber costs was 30,733 (SD=14,876.96), with small producers averaging 25,628 (SD=3 153.24) and large producers at 34,817 (SD=18,862.54), showing significant difference. Solo pesticide costs had an overall average of 2,979 (SD=2,072.56), small producers at 3,845 (SD=2,366.14), and large producers at 1,895 (SD=775.92), with a significant difference. The solo irrigation costs averaged 5,506 (SD=6,365.28) overall, with small producers at 8,124 (SD=7,578.22) and large producers at 2,234 (SD=730.40), also showing significant difference.

For solo transportation costs, the overall average was 4,363 (SD=3,228.07), with small producers averaging 2,334 (SD=833.65) and large producers at 5,985 (SD=3,511.14), showing significance difference. Solo storage costs averaged 1,048 (SD=696.75) overall, small producers at 1,425 (SD=734.89), and large producers at 578 (SD=142.95), showing significant difference. Finally, the solo labour requirement cost had an overall average of 44,992 (SD=21,509.08), with small producers averaging 32,259 (SD=10,478.55) and large producers at 55,178 (SD=22,687.10), also showing significant difference.

In summary, costs for bullock, organic fertilizer, potato tubers, pesticides, irrigation, transportation, storage, and labour were significantly different at 1%, while chemical fertilizer costs showed no significant difference.

3.5 Comparison of solo potato production and mixed potato production between small and large producers

The analysis of potato production revealed that the overall average solo potato production was 4,216 kg with a standard deviation of 1,465.91 (Table 5). For small producers, the average was 4,452 kg (SD = 1,824.46), while large producers had an average of 3,922 kg (SD = 749.61). In terms of returns, the overall average was 293 646 NRs/ha (SD = 99,757.64), with small producers averaging 308,899 NRs/ha (SD = 123,893.77) and large producers averaging 274,580 NRs/ha (SD = 52,473.30). The total cost averaged 193,123 NRs/ha (SD = 76,445.36) overall, with small producers averaging 226,641 NRs/ha (SD = 83,901.62) and large producers at 151 225 NRs/ha (SD = 35,366.01). Benefits averaged 100,523 NRs/ha (SD = 77,747.09) overall, with small producers averaging 82,257 NRs/ha (SD = 88,574.74) and large producers averaging 123,355 NRs/ha (SD = 54,539.54). The Benefit-Cost (BC) Ratio was 1.62 (SD = 0.54) overall, with small producers at 1.41 (SD = 0.49) and large producers at 1.89 (SD = 0.50) showing significant difference. The total cost showed significant differences at the 1% level, benefits at the 5% level, and returns at the 10% level, while production quantities were not statistically significant.

Table 5 Solo potato production between small and large producers Note: *, **, *** indicates significant at 10%, 5%, 1% level respectively

For maize production, the overall average was 4,636 kg (SD=1,963.68), with small producers averaging 4,630 kg (SD=2,195.85) and large producers at 4,643 kg (SD=1,655.73), showing no significant difference (p=0.977) (Table 6). In contrast, mixed potato production had an overall average of 4,718 kg (SD=2,936.22), with small producers at 5,428 kg (SD=3,770.23) and large producers at 3,831 kg (SD=601.41); this difference was significant.

Table 6 Maize and mixed potato production between small and large producers Note: *, **, *** indicates significant at 10%, 5%, 1% level respectively

For potato returns, the overall average was 330,300 NRs/ha (SD=205,535.47), with small producers averaging 379,986 NRs/ha (SD=263,916.12) and large producers at 268,192 NRs/ha (SD=42,098.93), also showing significant difference. Mixed maize returns had an overall average of 278,172 NRs/ha (SD=117,820.87), with small producers at 277,844 NRs/ha (SD=131,751.58) and large producers at 278,583 NRs/ha (SD=99,343.84), showing no significant difference (p=0.977).

The mixed benefits averaged 379,915 NRs/ha (SD=238,739.91), with small producers at 389,911 NRs/ha (SD=307,945.91) and large producers at 367,420 NRs/ha (SD=103,126.20), which was not significant (p=0.660). The mixed total cost averaged 228,557 NRs/ha (SD=91,591.87), with small producers at 267,919 NRs/ha (SD=102,234.49) and large producers at 179,355 NRs/ha (SD=39,282.02), showing significant difference.

Lastly, the BC ratio had an overall average of 2.77 (SD=0.88), with small producers at 2.40 (SD=0.87) and large producers at 3.15 (SD=0.75), showing significant difference. In summary, mixed potato production, potato returns, mixed total costs, and the BC ratio showed significant differences at 1%, while maize production, mixed maize returns, and mixed benefits did not.

3.6 Benefits of mixed agricultural practices

The ranking illustrated a detailed analysis of the benefits associated with mixed agricultural practices (Table 7). The highest severity benefit was the reduction in the risk of crop failure, which was deemed critically important. Its significance was underscored by a high index value of 0.99, indicating that it was the most impact benefit in the analysis.

Table 7 Benefits of mixed agricultural practices

In second place, the effective utilization of space was recognized as a valuable contributor, though it ranked lower in severity compared to crop failure risk. It had a moderate index of 0.77, showing that while it was important, it did not carry the same weight as the top-ranked benefit. The third-ranked benefit was the increase in soil moisture content, which had a lower severity. The varied scoring distribution suggested that its impact, while relevant, was less pronounced compared to the top two benefits, reflected in an index of 0.51. Fourth in severity was the decrease in runoff, which, despite being beneficial, exhibited only a modest contribution to agricultural sustainability, as indicated by an index of 0.46. Lastly, reducing pest and disease management (PTM) ranked last in terms of severity. Its low impact was evident, as it had the weakest index value of 0.25, revealing that it was the least significant benefit among those assessed.

Overall, the analysis demonstrated a clear hierarchy of impacts, emphasizing the need to prioritize strategies that effectively mitigate crop failure risk and enhance space utilization in agricultural practices.

3.7 Ranking of mixed agricultural drawback

The ranking for mixed agricultural drawbacks provided a thorough examination of various challenges, highlighting their severity (Table 8). The most severe drawback identified was "exhaustive nutrient", which represented a critical concern in mixed farming systems, reflected by a high index of 0.90. The "shading effect" followed closely, recognized as a significant challenge that could hinder crop performance, particularly for sunlight-dependent plants, and was slightly less severe than nutrient depletion. The third drawback, "pest and disease", while noteworthy, was deemed less impactful than the top two issues, indicating it disrupted agricultural productivity but was not as urgent as nutrient exhaustion or shading. "Water resource stress" ranked next, representing a notable concern regarding water availability for crops but lacking the severity of the previously mentioned drawbacks. Lastly, "more labour requirement for harvesting" emerged as the least severe challenge, suggesting that while labor intensity was a factor, it posed minimal impact compared to the other issues. Overall, the analysis emphasized the critical need to address nutrient management and shading effects, while recognizing the lesser challenges associated with pests, water stress, and labor requirements in mixed agriculture.

Table 8 Ranking of mixed agricultural drawback top of form bottom of form

3.8 Ranking of marketing problems

The ranking for marketing problems provided a thorough assessment of the challenges encountered in agricultural marketing, focusing on their severity (Table 9). The most severe issue identified was "seasonal price variation", which emerged as a critical concern due to its substantial impact on farmers’ profitability and market stability, reflected in its high index of 0.99.

Table 9 Ranking of marketing problems

Following this, "monopoly in trade" represented a significant challenge, as market control by a few entities could undermine competition and adversely affect pricing structures. The third most severe issue was "distant market", which posed a notable challenge for farmers trying to access larger or more lucrative markets due to geographical barriers. The fourth issue, "inadequate market infrastructure", was acknowledged as a relevant problem, though its impact was deemed less severe compared to the top three challenges, indicating a clear need for improvements in logistics and market access. Lastly, "postharvest loss" ranked as the least severe problem, highlighting the ongoing issue of food waste after harvest, but its impact was recognized as minimal compared to the other marketing challenges.

Overall, the analysis underscored the importance of addressing seasonal price fluctuations and monopolistic practices while also acknowledging the need for enhanced market access and infrastructure to mitigate postharvest losses.

3.9 Regression analysis based on solo cropping and mixed cropping costs

The regression analysis on solo cropping costs revealed significant findings regarding various cost factors (Table 10). The estimated coefficient for chemical fertilizer cost was 0.334, indicating a strong positive relationship with the dependent variable, supported by a t-value of 4.61 and a p-value of 0.000, which highlighted its statistical significance. Similarly, potato tuber cost also showed a significant positive effect, with an estimated coefficient of 0.467, a t-value of 4.31, and a p-value of 0.000. In contrast, other variables such as tillage cost, organic manure cost, irrigation cost, pesticide cost, and labour cost exhibited no significant impact, as indicated by their higher p-values. The overall model was robust, as evidenced by an F-value of 40.95 and an R² of 0.777, suggesting that approximately 77.7% of the variability in solo cropping costs was explained by the included variables, with an adjusted R² of 0.758 indicating a good fit of the model. The regression analysis on mixed cropping costs yielded several significant insights (Table 11). Notably, the estimated coefficient for mixed chemical fertilizer cost was 0.411, indicating a strong positive relationship with overall cropping expenses, supported by a t-value of 4.48 and a p-value of 0.000, which confirmed its statistical significance.

Table 10 Regression analysis based on solo cropping costs

Table 11 Regression analysis based on mixed cropping costs Note: *, ** and *** indicate significant at 10%, 5% and 1% level respectively

Additionally, mixed potato tuber cost demonstrated a marginally significant effect with a coefficient of 0.184 and a p-value of 0.065, suggesting its potential relevance. The maize seed cost also approached significance with a coefficient of 0.368 and a p-value of 0.074. In contrast, other variables, including tillage cost, mixed organic manure cost, mixed irrigation cost, mixed pesticide cost, and mixed labour cost, were found to have no significant impact on costs, as reflected by their high p-values. The model exhibited a strong overall fit, with an F-value of 57.52 and an R² of 0.866, indicating that approximately 86.6% of the variability in mixed cropping costs was explained by the included variables, while the adjusted R² of 0.851 further underscored the robustness of the model. These findings suggest that focusing on chemical fertilizers, potato tubers, and maize seeds may be crucial for managing costs in mixed cropping systems.

4 Discussion

The potato productivity in Rasuwa district was 16.15 t/ha (MoALD, 2023/24). The BCR of solo potato cropping on our study site was 1.62 which was justified by the BCR of 1.84 of Nuwakot district (PMAMP, 2020/21). The BCR of mixed potato-maize cropping of our study area was found to be 2.77 which was justified by Islam et al. (2014). The mixed potato cropping reduce the risk of crop failure received the highest score of 89.8 by decreasing surface runoff on slopping land (Manrique, 1996). Mixed potato cropping also Increase soil moisture content (Fan et al., 2016).

Maize is most compatible crop with potato for their contrasting phenology highest maize equivalent yield and yield advantage. Mix cropping increase BCR, Gross return and Gross margin combining than all other intercropping system as well as than solo cropping of potato and maize (Begum et al., 2016). Intercropping of winter maize with potato irrespective of its varieties with 50% recommended fertilizer dose of potato as additional to maize was found more productive and advantageous on a system basis in calcareous soil of North Bihar (Singh et al., 2015). Hybrid maize sown 30 days after potato planting was found the most productive and profitable intercropping system for getting higher potato equivalent yield and monetary advantages without affecting the main crop yield (Begum et al., 2017).

Intercropping increase soil water storage deficit degree in the deeper soil layer, which means that is consume less soil water than expected from the crop yield obtained in mono cropping which was beneficial towards promoting crop yield and water use efficiency in the semi-arid environment. Potato is a shade loving crop and thus above ground shading from intercropped maize might be beneficial for the growth and yield and promoting land productivity and water use efficiency when compare to mono cropping (Xie et al., 2021). Maize and potato are the most adoptable and major crops as food and cash crop , both are of equal importance to the farmer because they can gain comparable economic returns or can satisfy subsistence requirement equally (Kidane et al., 2017). Seasonal price variation was the major market problem (Kalwar et al., 2024). The result is consistent with the R² value of 0.72 reported by Raina et al. (2024) in the solo cropping system. Yield increase under solo cropping opposed to intercropping treatments might be attributed to the higher plant population and decrease in their competition among plants.

Maize and Potato are the most adaptable and major crops as food and cash crops (Anon, 2017). Both crops are of equal importance to the farmer because they can gain comparable economic returns or can subsistence requirement equally, Given the unpredictable rainy season and different water requirement of each crop, planting maize and potato together gives the farmer a better chance that either crop will survive.

5 Conclusion

The profitability and resource efficiency of solo and mixed potato cropping were assessed, revealing that mixed cropping with maize resulted in a significantly higher benefit-cost ratio (BCR) of 2.77 compared to 1.62 for solo potato cropping. The analysis of yield variation, input costs, and income generation indicated that while the total cost of solo cropping was NRs/ha 193,123, mixed cropping incurred a total cost of NRs/ha 228,557. However, the average benefits from mixed cropping reached NRs/ha 379,915, compared to NRs/ha 100,523 from solo potato cropping. Additionally, the socioeconomic traits of farmers were examined, highlighting the potential for increased economic returns through mixed cropping practices in the Rasuwa district. Overall, the findings demonstrated that mixed cropping was more economical than solo potato cropping, emphasizing the advantages of diversification in agricultural practices for enhanced profitability and resource utilization.

Authors’ contributions

PY conceptualized the research, collected, entered and analysed data, and prepared manuscript. SY involved in methodology, data entry and analysis, preparing manuscript, editing and proofreading the manuscript. BC entered and analysed data, prepared and proofread the manuscript. SP involved in methodology, preparing manuscript, editing and proofreading the manuscript. All authors have read the final version of the manuscript.

Fund Statement

No funding was obtained for this research.

Conflict of Interest

The authors declare no conflict of interest.

References

Anon., 2017, Maize and potato intercropping: A technology to increase productivity and profitability in Tigray, Open Agriculture, 2(1): 411-416.

https://www.degruyter.com/document/doi/10.1515/opag-2017-0044/html

Begum A.A., Bhuiya M.S.U., Hossain S.M., Khatun A., and Das S.K., 2016, Effect of planting system of potato and plant density of maize on productivity of potato-hybrid maize intercropping system, Bangladesh Journal of Agricultural Research, 41: 397-409.

https://doi.org/10.3329/bjar.v41i3.29711

Begum A.A., Bhuiya M.S.U., Hossain S.M.A., Khatun A., Das S.K., and Sarker M.Y., 2017, System productivity of potato + maize intercropping as affected by sowing date, Bangladesh Agronomy Journal, 19(2): 11-20.

https://doi.org/10.3329/baj.v19i2.31848

Fan Z., An T., Wu K., Zhou F., Zi S., Yang Y., Xue G., and Wu B., 2016, Effects of intercropping of maize and potato on sloping land on the water balance and surface runoff, Agricultural Water Management, 166: 9-16.

https://doi.org/10.1016/j.agwat.2015.12.006

Farooq K., Hussain A., Jan N., Bajwa K.A., and Mahmood M.M., 1995, Effect of maize, beans and potato intercropping on yield and economic benefit, Pakistan Agricultural Research Council - National Seminar on Research and Development of Potato Production in Pakistan, Islamabad.

Islam M.N., Akhteruzzaman M., and Alom M.S., 2014, Optimization of fertilizer rate based on farmers practice in potato-hybrid maize relay cropping system, Bangladesh Journal of Agricultural Research, 39(2): 351-357.

https://doi.org/10.3329/bjar.v39i2.20443

Kalwar S.K., Thapa K., Sapkota R., and Sah P.K., 2024, Economics of production and marketing of potato (Solanum tuberosum) in Rasuwa District, Nepal, International Journal of Horticulture, 14(3): 186-194.

https://doi.org/10.5376/ijh.2024.14.0021

Kidane B.Z., Hailu M.H., and Haile H.T., 2017, Maize and potato intercropping: A technology to increase productivity and profitability in Tigray, Open Agriculture, 2(1): 411-416.

https://doi.org/10.1515/opag-2017-0044

Manrique L.A., 1996, The potato in multiple cropping systems, Journal of Plant Nutrition, 19(1): 215-243.

https://doi.org/10.1080/01904169609365118

MOALD, 2023/24, Statistical information on Nepalese agriculture, Singhadurbar, Kathmandu, Nepal: Planning and Development Cooperation Coordination Division.

PMAMP, 2020/21, Socioeconomic study of tomato and potato cultivation in Nuwakot district of Nepal, Nuwakot: Project Implementation Unit.

Raina A., Sharma P., Gupta G., and Kumari S., 2024, An economics of potato production in Kangra district of Himachal Pradesh, Potato Journal, 51(1): 97-108.

https://doi.org/10.56093/potatoj.v51i1.152422

Singh S.K., Lal S.S., and Singh R.K., 2015, Production potential of potato and maize inter-cropping as influenced by spacing, planting pattern and staggered sowing of maize, Journal of AgriSearch, 2(3): 167-174.

Xie J., Wang L., Li L., An S., Luo Z., Zechariah E., and Fudjoe S.K., 2021, Yield, economic benefit, soil water balance, and water use efficiency of intercropped maize/potato in responses to mulching practices on the semiarid loess plateau, Agriculture, 11(11): 1100.

https://doi.org/10.3390/agriculture11111100

Zheng Y., Zhang L., Chen B., Yan N., Gui F., Zan Q., Du G., He S., Li Z., Gao Y., and Xiao G., 2020, Potato/maize intercropping reduces infestation of potato tuber moth, Phthorimaea operculella (Zeller) by the enhancement of natural enemies, Journal of Integrative Agriculture, 19(2): 394-405.

https://doi.org/10.1016/S2095-3119(19)62699-7