Yield gap and resource use efficiency of chilli cultivation in Tapi district of Gujarat

Introduction

Chilli is a essential commercial spice crop in South Gujarat, with the Tapi district recognized as a premier region for dry chilli cultivation within the state. The region is known for producing high-quality, pungent chillies that are in demand both locally and for regional trade. As a high-value crop, chilli holds considerable promise for augmenting farmers' incomes and bolstering the economic landscape of the area. However, the efficient utilization of resources in chilli cultivation remains a crucial aspect deserving thorough investigation to ensure sustainable productivity and profitability. Against this backdrop, this research endeavors to comprehensively examine the resource use efficiency and yield gap prevalent in chilli cultivation within Tapi district, Gujarat. The primary objective is to quantitatively evaluate the effectiveness of resource allocation in chilli farming, discerning the inputs contributing significantly to productivity. By meticulously measuring resource use efficiency, the study aims to pinpoint areas for enhancement and formulate strategies to optimize resource allocation in chilli cultivation. Additionally, the research seeks to assess the yield gap existing in chilli production within the Tapi district. Through a comparative analysis of actual yields against potential yields achievable under optimal conditions, the study endeavors to quantify the extent of productivity shortfall. Such an assessment not only illuminates the prevailing inefficiencies within chilli cultivation but also serves as a foundation for devising interventions aimed at narrowing the yield gap and enhancing overall agricultural performance. By addressing these critical aspects, this research aspires to contribute significantly to the understanding of chilli cultivation dynamics in the Tapi district. Through actionable insights derived from the pursuit of its objectives, the study aims to support sustainable agricultural development and foster economic prosperity in the region.

Data & Methodology

For the present investigation, primary data were collected using multistage random sampling method from Tapi district of South Gujarat. Out of the 7 talukas of Tapi District, 4 talukas were selected. Then 4 villages per taluka were selected. Thus total 16 villages were selected. Then 20 farmers were randomly selected per village. Total 320 chilli growers were selected for the present study.

Resource use efficiency

Yield Gap

Sample farmers in the study area were classified into two categories based on their okra yield levels: best farmers and average farmers. The top five farmers who obtained the highest yields were categorized as best farmers, while the remaining farmers were considered average farmers. The farm-level yield gap was then estimated by comparing the mean yield of the best farmers with that of the average farmers.

Results and Discussion

To full fill the objectives of the study, four talukas from Tapi district viz. Vyara, Valod, Dolvan and Nozar were selected. A list of villages was prepared from the selected talukas from the revenue records of the office of TDO. Four villages from every selected talukas were selected. Thus total sixteen villages were selected randomly from each selected taluka. The lists of villages are presented in Table-1. The lists of respondents were prepared from each village at the third stage. From this list, 20 farmers were selected from each village and finally320 respondents were selected by random sampling technique. Data were collected using pretested and well-structured interview schedule.

Input utilization

Per hectare quantities of different inputs/resources are given in table-2. The table presents the input utilization pattern of chilli cultivation across different farm-size groups, namely small, medium, large and overall farms. The analysis indicates considerable variation in the use of inputs among the different categories of farmers. Among all the inputs, the use of seedlings was highest on large farms (18,180.85 seedlings), followed by medium farms (17,880.80 seedlings), while small farms used comparatively fewer seedlings (15,580.75 seedlings). The overall average use of seedlings was 16,300.

The application of FYM (Farm Yard Manure) also increased with farm size. Large farmers applied the highest quantity of FYM (83.92 quintals), followed by medium farmers (74.40 quintals) and small farmers (59.28 quintals). The overall average application was 66.30 quintals. A similar trend was observed in fertilizer consumption. Large farms applied higher quantities of nitrogen (112.50 kg), phosphorus (63 kg) and potash (60 kg) compared to medium and small farms. Small farmers used the least amount of fertilizers, with nitrogen, phosphorus and potash application recorded at 85.32 kg, 43.54 kg and 45.55 kg, respectively. The overall average fertilizer use was 91.84 kg of nitrogen, 49.77 kg of phosphorus and 49.18 kg of potash. The number of irrigations provided was slightly higher on large farms (14.05) than on medium (13.50) and small farms (13.15), with an overall average of 13.37 irrigations.

Similarly, the number of plant protection chemical sprays was highest on large farms (12.50 sprays), followed by medium farms (12.15 sprays) and small farms (11.50 sprays).

Regarding labour utilization, small farms relied heavily on family labour, employing man-days, compared to 104.47 and 65.41 man-days on medium and large farms, respectively. In contrast, the use of hired labour increased with farm size. Large farms employed the highest hired labour (361.30 man-days), followed by medium farms (321.14 man-days) and small farms (156.63 man-days). The overall average use of family and hired labour was 172.88 and 244.65 man-days, respectively. Overall, the table reveals that larger farms utilized higher quantities of most modern inputs such as fertilizers, FYM, irrigation and plant protection chemicals, whereas small farms depended more on family labour for cultivation activities.

Resource use efficiency

The regression analysis presented in Table 3 explains the effect of different production variables on the cultivation of okra. The estimated coefficients indicate the direction and magnitude of the influence of each input on okra yield, while the significance levels reveal whether the effects are statistically reliable. The intercept value was estimated at 2.27986, indicating the baseline level of output when all explanatory variables are held constant. Among the variables included in the model, seedling quantity showed a positive and statistically significant effect at the 1 percent level, with a regression coefficient of 0.050. This implies that an increase in the number of seedlings used in cultivation contributed positively to okra production. The result highlights the importance of maintaining optimum plant population for achieving higher productivity. Human labour had a positive coefficient of 0.212 and was significant at the 5 percent level. This finding indicates that labour-intensive operations such as interculturing, harvesting, irrigation management and plant protection practices contribute significantly to increased output in chilli cultivation.

On the other hand, farmyard manure (FYM), phosphorus, irrigations, pesticides spray and bullock labour was found to be statistically non-significant. Although FYM and irrigation had positive coefficients, their effects on yield was not statistically significant, indicating that variations in their use among farmers did not substantially influence output. Phosphorus, pesticides spray, and bullock labour exhibited negative coefficients, implying an inverse relationship with output; however, these effects was also statistically nonsignificant. The negative coefficient of pesticide spray may indicate excessive or inefficient use of chemicals, while the negative effect of bullock labour suggests the declining relevance of traditional labour inputs in modern chilli cultivation practices. Overall, the regression results reveal that seedlings, nitrogen, potash and human labour was the major determinants significantly influencing chilli production in the study area. Efficient and balanced use of these inputs can help farmers improve productivity and achieve better economic returns from chilli cultivation.

Yield gap

As described in methodology, the present analysis has been attempted to estimate only farm level yield gap for chilli in the study area. For this the sample growers were classified into two categories viz., best and average farmer accordingly to the level of their chilli productivity. The highest yield of crop obtained by the best five sample farmers was considered as the potential yield of the chilli in the area. This was treated as a farm level potential yield which can be realized through proper inputs and management. This farm level potential yield was then compared with the average yield obtained by remaining farmers (average farmers). Therefore, the yield gap in the present study was calculated between the best and the average sample farmer. The result pertaining to yield levels and yield gap is presented in Table 5. It can be observed from the Table 5 that the average yield of best farmers for chilli were considerably high (177.56 Qt/ha) than the average yield levels (158.66Qt/ha) of the average farmers. The results revealed that there was significant difference between the yield of the best farmer and average farmers, which was about 18.90 quintals per hectare absolute terms. The findings clearly indicate that best farmers adopted relatively higher levels of critical inputs such as seedlings, FYM, fertilizers, irrigation and family labour compared to average farmers. These differences in resource use patterns likely contributed to the higher productivity achieved by best farmers. The results suggest that improved management practices, balanced nutrient application, proper crop supervision and timely execution of farm operations are key factors influencing chilli productivity. The study further implies that average farmers can enhance their yield levels by adopting the input use pattern and management practices followed by best farmers.

Conclusion

The study revealed that the use of inputs per hectare in chilli cultivation tended to increase with the increase in farm size. Larger farmers was found to utilize relatively higher quantities of critical inputs such as seedlings, fertilizers, irrigation and labour compared to smaller farmers. The resource use efficiency analysis indicated that the application of fertilizers, particularly nitrogen and potash, as well as the use of seedlings, was below the economically optimum level in the study area. Therefore, increasing the use of these inputs within the recommended limits could enhance chilli productivity and profitability. The findings suggest that adoption of balanced nutrient management practices and optimum plant population would improve resource use efficiency and contribute to higher productivity and economic returns from chilli cultivation.

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