The Economic Efficiency of Coffee Growers in the Department of Caldas, Colombia
Abstract
:1. Introduction
2. Methodology
2.1. Efficiency Measures
2.2. Stochastic Boundary Analysis of Panel Data
2.3. Location and Study Area
2.4. Study Population
2.5. Information Capture
2.6. Research Variables
3. Results
Economic Efficiency
4. Discussion
4.1. Classification of Producers by Level of Productivity
4.2. Efficiency Scores
4.3. Economic Efficiency and Productivity
4.3.1. Economic Efficiency Less Than 85%
- Characteristics of production systems. The average area of the farms established with resistant varieties was 82%, the average planting density was 5655 trees per hectare, the average age of the trees was 3.63 years, and the average percentage of area for renewed coffee (unproductive stage) was 16%.
- Resource allocation. The application of 807 kg of fertilizer per hectare per year stands out, an amount 32% lower than the application of the group of coffee growers with an efficiency greater than 90%, but at a cost greater than 21% due to their criteria for carrying out this work—mainly, the sources of fertilizer used and a greater fractionation (frequency) of application. The coffee growers grouped at this level of efficiency stated that they did not apply lime to adjust the acidity of the soil.
4.3.2. Economic Efficiency Greater Than 85% and Less Than 90%
- Characteristics of production systems. The average area sown with resistant varieties was 91%, the average planting density was 6615 trees per hectare, the average age of the trees was 3.65 years, and the average area for renewed coffee (vegetative growth) was 18%.
- Resource allocation. The coffee growers grouped at this level of economic efficiency applied 964 kg of fertilizer on average per hectare per year, mainly using fertilizers with a recommended grade for coffee cultivation and physical mixtures of urea, dap and KCl. They performed soil analysis every 2 years; based on these results, they corrected the acidity of the soils using dolomite lime. For renewal by sowing and replanting, they used plant material supplied by the Coffee Growers Committee (28%) and certified seed (72%) for the purpose of preparing the seedlings on their farms.
4.3.3. Economic Efficiency Greater Than 90%
- Characteristics of production systems. The following averages were found: established area with resistant varieties, 96%; average planting density, 6695 trees per hectare; average age of trees, 3.27 years; and average area for renewed coffee, 21%. These figures reflect production systems with a lower risk of coffee rust and greater productive potential due to their greater number of trees per hectare and the lower age of these trees, confirmed by production cycles defined in fifths due to their renovated area or in lift. In northern Rwanda (Ngango and Kim 2019), the use of resistant varieties mainly has a positive effect on the technical efficiency of coffee growers. The characteristics of this group with higher levels of productivity and efficiency coincide with the results of Araque Salazar and Duque (2019), i.e., maximum partial elasticities correspond to the variables coffee plantation density (trees/ha) and fertilization level (kg ha-year-1 of fertilizer). Likewise, crop age, the percentage of resistant varieties and the growing area are determinants of productivity for the group of coffee farms analyzed. The results for this group of efficient coffee growers coincide with the technical characteristics of small coffee growers in Vietnam who have developed sustainable coffee farming and for whom the renewal of crops and the use of resistant varieties and fertilization practices are fundamental in the productive performance and economic efficiency of their crops (Hung Anh et al. 2019).
- Resource allocation. This group presented the highest amounts of fertilizer (1067 kg), applied on average per hectare per year; however, its fertilization costs were 21% and 10% lower than those of the coffee growers with low and medium efficiencies, respectively. Fertilization decisions were based on the results of soil analysis and the recommendations of an extension service technician (79%). The application of fertilizer was divided into two applications during the year, and the sources used were mainly a mixture of urea, dap, KCl and formulas with degrees for the cultivation of coffee (78%). In the same sense, decisions on the execution of agronomic practices such as the renewal of coffee plantations, replanting, weed management, sanitary management, correction of soil acidity, and the opportunities to apply them, among this group of coffee growers were framed by technical recommendations.
4.4. Socioeconomic Characteristics and Resource Allocation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Average | Standard Deviation | Maximum | Minimum |
---|---|---|---|---|
Unit cost | USD 1.44 | USD 0.22 | USD 2.32 | USD 1.17 |
Productivity | 2585.75 kg cps | 1027.21 kg cps | 7785.63 kg cps | 438.5 kg cps |
Crop renewal | USD 0.04 | USD 0.05 | USD 0.23 | USD 0.18 |
Weed management | USD 0.10 | USD 0.05 | USD 0.56 | USD 0.01 |
Fertilization | USD 0.20 | USD 0.09 | USD 0.82 | USD 0.05 |
Pest control | USD 0.03 | USD 0.03 | USD 0.22 | USD 0.00 |
Harvest | USD 0.69 | USD 0.10 | USD 1.27 | USD 0.47 |
Postharvest | USD 0.08 | USD 0.04 | USD 0.58 | USD 0.00 |
Administrative expenses | USD 0.16 | USD 0.12 | USD 0.66 | USD 0.01 |
True Random-Effects Model (Half-Normal) Group Variable Farmer Code | Number of Obs = 952 Number of Groups = 136 | |||||
---|---|---|---|---|---|---|
Time variable: Year | Obs per group: | |||||
Min = 7 | ||||||
Avg = 7 | ||||||
Max = 7 | ||||||
Log-simulated likelihood = 1211.9535 | Prob > chi2 = 0.0000 | |||||
Wald chi2(10) = 7295.21 | ||||||
Number of Pseudo-Random Draws = 250 | ||||||
LUnit cost | Coef. | Std. Err | z | P > |z| | [95% Conf. Interval] | |
Frontier | ||||||
LProductivity | −0.022411 | 0.0069812 | −3.21 | 0.001 | −0.0360939 | −0.0087282 |
LCost harvest | 0.5777314 | 0.0169494 | 34.09 | 0.000 | 0.5445112 | 0.6109517 |
LCost postharvest | 0.0271037 | 0.0038555 | 7.03 | 0.000 | 0.019547 | 0.0346604 |
LCost fertilization | 0.1625467 | 0.0067318 | 24.15 | 0.000 | 0.1493526 | 0.1757409 |
LCost pest control | 0.0016984 | 0.0004773 | 3.56 | 0.001 | 0.0007629 | 0.0026339 |
LCost crop renewal | 0.0086642 | 0.0006207 | 13.96 | 0.000 | 0.0074477 | 0.0098808 |
LCost weed management | 0.0816932 | 0.0052962 | 15.42 | 0.000 | 0.0713128 | 0.0920735 |
LAdministrative expenses | 0.1159822 | 0.0047837 | 24.25 | 0.000 | 0.1066064 | 0.125358 |
Aptitude | −0.0002715 | 0.0079998 | −0.03 | 0.973 | −0.0159508 | 0.0154078 |
Scale | 0.0196376 | 0.0041571 | 4.72 | 0.000 | −0.0408214 | 0.0234594 |
_cons | 1.636146 | 0.1867383 | 8.76 | 0.000 | 1.741275 | 2.726517 |
Usigma _cons | −4.397366 | 0.0678828 | −64.78 | 0.000 | −4.530414 | −4.264318 |
Vsigma _cons | −7.87959 | 0.3001687 | −26.25 | 0.000 | −8.46791 | −7.29127 |
Theta _cons | 0.236709 | 0.002594 | 9.13 | 0.000 | 0.0185868 | 0.0287551 |
Sigma_u | 0.1109492 | 0.0037658 | 29.46 | 0.000 | 0.1038086 | 0.118581 |
Sigma_v | 0.0194522 | 0.0029195 | 6.66 | 0.000 | 0.014495 | 0.0261048 |
lambda | 5.703683 | 0.0058315 | 978.09 | 0.000 | 5.692253 | 5.715112 |
True Random-Effects Model (Exponential) Group Variable Farmer Code | Number of Obs = 952 Number of Groups = 136 | |||||
---|---|---|---|---|---|---|
Time variable: Year | Obs per group: | |||||
Min = 7 | ||||||
Avg = 7 | ||||||
Max = 7 | ||||||
Log-simulated likelihood = 1248.6408 | Prob > chi2 = 0.0000 | |||||
Wald chi2(10) = 7982.28 | ||||||
Number of Pseudo-Random Draws = 250 | ||||||
LUnit cost | Coef. | Std. Err | z | P > |z| | [95% Conf. Interval] | |
Frontier | ||||||
LProductivity | −0.0240747 | 0.0063699 | −3.78 | 0.000 | −0.0365594 | −0.0115899 |
LCost harvest | 0.5760682 | 0.0157167 | 36.65 | 0.000 | 0.5452641 | 0.6068724 |
LCost postharvest | 0.055658 | 0.0052729 | 10.56 | 0.000 | 0.0453233 | 0.0659926 |
LCost fertilization | 0.1639617 | 0.0062411 | 26.27 | 0.000 | 0.1517294 | 0.176194 |
LCost pest control | 0.0018981 | 0.0004255 | 4.46 | 0.000 | 0.0010641 | 0.0027321 |
LCost crop renewal | 0.0078804 | 0.0004956 | 15.90 | 0.000 | 0.006909 | 0.0088517 |
LCost weed management | 0.0772404 | 0.0050419 | 15.32 | 0.000 | 0.0673585 | 0.0871223 |
LAdministrative expenses | 0.1102238 | 0.0045316 | 24.32 | 0.000 | 0.101342 | 0.1191056 |
Aptitude | −0.0006008 | 0.0074631 | −0.08 | 0.936 | −0.0152283 | 0.0140266 |
Scale | 0.0168867 | 0.0037464 | 4.51 | 0.000 | 0.0095438 | 0.0242295 |
_cons | 1.532464 | 0.1735522 | 8.83 | 0.000 | 1.192308 | 1.872621 |
Usigma _cons | −5.36782 | 0.09581 | −56.03 | 0.000 | −5.555604 | −5.180036 |
Vsigma _cons | −7.354975 | 0.1740003 | −42.27 | 0.000 | −7.696009 | −7.013941 |
Theta _cons | −0.0222747 | 0.0023655 | −9.42 | 0.000 | −0.0269111 | −0.0176383 |
Sigma_u | 0.0682956 | 0.0032717 | 20.87 | 0.000 | 0.062175 | 0.0750187 |
Sigma_v | 0.0252864 | 0.0021999 | 11.49 | 0.000 | 0.0213222 | 0.0299876 |
lambda | 2.700879 | 0.0047524 | 568.32 | 0.000 | 2.691565 | 2.710194 |
Classes | Frequency | % | % Accumulated | |
---|---|---|---|---|
0.75–0.80 | 3 | 2% | 2% | |
0.81–0.85 | 5 | 4% | 6% | |
0.85–0.90 | 48 | 35% | 41% | |
0.91–1.00 | 80 | 59% | 100% | |
Average | Median | Standard deviation | Minimum | Maximum |
0.8969 | 0.9052 | 0.3036 | 0.7724 | 0.9464 |
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Salazar Echeverry, H.M.; Duque Orrego, H.; Granobles-Torres, J.C. The Economic Efficiency of Coffee Growers in the Department of Caldas, Colombia. Economies 2023, 11, 255. https://doi.org/10.3390/economies11100255
Salazar Echeverry HM, Duque Orrego H, Granobles-Torres JC. The Economic Efficiency of Coffee Growers in the Department of Caldas, Colombia. Economies. 2023; 11(10):255. https://doi.org/10.3390/economies11100255
Chicago/Turabian StyleSalazar Echeverry, Hugo Mauricio, Hernando Duque Orrego, and Juan Carlos Granobles-Torres. 2023. "The Economic Efficiency of Coffee Growers in the Department of Caldas, Colombia" Economies 11, no. 10: 255. https://doi.org/10.3390/economies11100255