The Impact of Climatic Change Adaptation on Agricultural Productivity in Central Chile: A Stochastic Production Frontier Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Practices Considered for Climate Change Adaptation
- Binary decision: a dichotomous variable indicating that at least one practice was adopted (A1). In this case, the aim is to analyze the impact of being able to carry out a basic strategy.
- Intensity: measured as the number of practices or technologies adopted on the farm (A2). Compared to A1, this measure analyzes the impact of passing the first hurdle, i.e., the decision to adapt.
- Quality: an index calculated as the sum of adaptation practices weighted by the experts’ score (A3). The objective here is to estimate the impact of adopted practices that are more effective to face climate change. The weights were estimated by normalizing the average scores (0–3) given by the panel of experts to each practice, to generate a scale. The quality adaptation index (A3) was constructed considering the sum of all the practices on a given farm multiplied by the weight assigned by experts (Wij), divided by the sum of all weights (Wi). The formula used is as follows: , where i are the farms (from 1–265) and j are the practices (from 1–14).) The value of A3 ranges from 0–100% where 100% implies that the practice presents the highest valuation assigned by the experts.
2.3. Analytical Framework and Empirical Model
3. Results and Discussion
3.1. Production Frontiers
3.2. Technical Efficiency
3.3. Efficiency and Climate Change Adaptation
4. Concluding Remarks
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A
Variables | Climate Change Adaptation Measurement | ||
---|---|---|---|
Decision | Intensity | Quality | |
Constant (β0) | 4.0218 (0.9857) *** | 4.6682 (0.9741) *** | 4.6090 (0.9891) *** |
Land (β1) | 0.2314 (0.0887) *** | 0.2654 (0.0869) *** | 0.2602 (0.0857) *** |
Capital (β2) | 0.6828 (0.0764) *** | 0.6206 (0.0754) *** | 0.6255 (0.0758) *** |
Labor (β3) | 0.1043 (0.0283) *** | 0.1112 (0.0283) *** | 0.1110 (0.0270) *** |
Dryland (β4) | −0.4204 (0.1334) *** | −0.3578 (0.1270) *** | −0.3614 (0.1314) *** |
Diversification (β5) | 0.5990 (0.1381) *** | 0.5957 (0.1357) *** | 0.6054 (0.1349) *** |
Climate change adaptation (β6) | 0.0331 (0.0735) | 0.0035 (0.0017) *** | 0.0046 (0.0024) ** |
Inefficiency Model | |||
Constant (δ0) | 0.2035 (0.6194) | 0.2166 (0.5937) | 0.1591 (0.7713) |
Age (δ1) | 0.0189 (0.0072) *** | 0.0177 (0.0075) *** | 0.0185 (0.0096) ** |
Schooling (δ2) | 0.0097 (0.0250) | 0.0129 (0.0259) | 0.0130 (0.0262) |
Dependence (δ3) | −0.4878 (0.1697) *** | −0.7657 (0.1776) *** | −0.7480 (0.2117) *** |
Specialization (δ4) | −0.0099 (0.0031) *** | −0.0098 (0.0034) *** | −0.0099 (0.0032) *** |
Use of meteorological information (δ5) | −0.7010 (0.2326) *** | −0.7406 (0.2423) *** | −0.7420 (0.2981) *** |
Membership (δ6) | 0.0877 (0.1701) | 0.2591 (0.1773) * | 0.2663 (0.1970) * |
Farm size (δ7) | −0.0040 (0.0026) * | −0.0035 (0.0010) *** | −0.0034 (0.0009) *** |
Distance to market (δ8) | 0.0056 (0.0029) ** | 0.0053 (0.0029) ** | 0.0051 (0.0030) ** |
Returns to scale | 1.0185 | 0.9972 | 0.9967 |
MLF | −218.13 | −211.14 | −211.51 |
Sigma2 | 0.4588 (0.0611) *** | 0.4516 (0.0652) *** | 0.4493 (0.0725) *** |
Gamma | 0.5632 (0.0996) *** | 0.5222 (0.1044) *** | 0.5178 (0.1144) *** |
TE | 67.52 | 71.34 | 71.50 |
Endogeneity (F value) | 4.868 *** | 14.266 *** | 13.012 *** |
Variable name | Description | Coefficient |
---|---|---|
A1 | Dependent Variable | |
ExpAgIndep | Years of independent experience in agriculture. | −0.0153 * (0.0087) |
SanClemente | Dummy variable = 1 if the farm is located in San Clemente and 0 otherwise | −0.9189 *** (0.2886) |
TTPropia | Dummy variable = 1 if the farmer is owner and 0 otherwise | 0.3590 (0.2821) |
Internet | Dummy variable = 1 if the farmer has access to meteorological information principally form the Internet and 0 otherwise | 0.9667 *** (0.3290) |
Constant | 0.5849 ** (0.3012) | |
Log-likelihood | −170.73 | |
N | 265 | |
Pseudo R2 | 5.86 | |
Correctly classified values by Logit (%) | 62.2 |
Variable name | Description | Coefficient |
---|---|---|
A2 | Dependent Variable | |
ExpAgIndep | Years of independent experience in agriculture. | −0.0121 *** (0.0034) |
RXP | Dummy variable = 1 if the farmer has adopted any irrigation improvement and the location is in Pencahue municipality and 0 otherwise | 0.7731 *** (0.1324) |
SupProd | Surface designated to production in hectares | 0.0003 (0.0003) |
Internet | Dummy variable = 1 if the farmer has access to meteorological information principally form the Internet and 0 otherwise | 0.2233 * (0.1329) |
Constant | 1.0172 *** (0.1362) | |
Log-likelihood | −411.76 | |
N | 265 | |
Correlation of predicted values (A1’) with A1 (%) | 53.51 |
Variable name | Description | Coefficient |
---|---|---|
A3 | Dependent Variable | |
ExpAgIndep | Years of independent experience in agriculture. | −0.2518 *** (0.0893) |
RXP | Dummy variable = 1 if the farmer has adopted any irrigation improvement and the farm location is Pencahue and 0 otherwise | 18.445 *** (4.2773) |
SupProd | Surface designated to production in hectares | 0.0173 * (0.0105) |
Internet | Dummy variable = 1 if the farmer has access to meteorological information principally form the Internet and 0 otherwise | 3.4477 (3.1870) |
Constant | 20.1456 *** (2.8415) | |
Log-Likelihood | −574.27 | |
N | 265 | |
Correlation of predicted values (A2’) with A2 (%) | 51.35 |
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Municipality | Area | Rainfall (mm/Year) | Farms | Farms Interviewed | Main Crop System (%) | ||||
---|---|---|---|---|---|---|---|---|---|
Wheat and Oat | Spring Crops a | Spring Vegetable b | Rice | Others Crops c | |||||
Pencahue | Irrigated dryland | 709 | 1129 | 40 | 12.5 | 35.0 | 52.5 | 0.0 | 0.0 |
Cauquenes | Non-irrigated dryland | 670 | 3026 | 81 | 97.5 | 2.5 | 0.0 | 0.0 | 0.0 |
San Clemente | Irrigated Andean foothill | 920 | 2990 | 89 | 40.4 | 42.6 | 12.4 | 0.0 | 4.6 |
Parral | Irrigated central valley | 900 | 1813 | 89 | 54.5 | 7.3 | 1.8 | 36.4 | 0.0 |
Total | 8958 | 265 | 56.6 | 77.4 | 12.5 | 7.5 | 1.5 |
Variable | Name | Unit | Definition | Mean | SD | |
---|---|---|---|---|---|---|
Production Function Variables | ||||||
y | Agricultural production | MM$ | Crop production value in Chilean pesos a | 31.2 | 14.0 | |
L | Cultivated land | Ha | Hectares with crops | 17.1 | 53.3 | |
C | Capital | MM$ | Value of seeds, fertilizers, pesticides and machinery contracted in Chilean pesos | 11.4 | 51.7 | |
W | Labor | MM$ | Value of family and hired labor | 2.2 | 6.8 | |
D | Dryland | % | Dummy variable = 1 if the farm is located in a dryland area and 0 otherwise | 30.6 | 46.2 | |
H | Diversification | % | Crop diversification index | 23.7 | 27.5 | |
A1 | Climate change adaptation | Decision | % | Dummy variable = 1 if there are at least one practice adopted and 0 otherwise | 56.6 | 49.7 |
A2 | Intensity | Number | Number of climate change adaptation practices adopted in the farm | 1.8 | 2.2 | |
A3 | Quality | % | Index of adaptation based on experts’ opinion | 12.6 | 15.4 | |
Inefficiency Model Variables | ||||||
z1 | Age | Years | Age of the head of the farm in years | 55.5 | 14.1 | |
z2 | Schooling | Years | Years of schooling of the head of the farm | 7.2 | 4.1 | |
z3 | Dependence | % | Dummy variable = 1 if agriculture is the main source of income for the household and 0 otherwise | 82.6 | 37.9 | |
z4 | Specialization | % | Percent of total income that corresponds to income from crops | 62.1 | 32.0 | |
z5 | Use of meteorological information | % | Dummy variable = 1 if the farmer is a user of meteorological information and 0 otherwise | 93.2 | 25.2 | |
z5 | Membership | % | Dummy variable = 1 if the farmer is a member of an association and 0 otherwise | 52.4 | 50.0 | |
z7 | Farm size | Ha | Total farm size in hectares | 56.4 | 122.3 | |
z8 | Distance to market | Km | Distance to the regional capital city in kilometers | 77.4 | 43.8 |
Practice | Type a | Weight % | Farmers (n = 265) | |
---|---|---|---|---|
No. of Respondents | % of Total | |||
Incorporation of crop varieties resistant to droughts | Cr | 85.7 | 2 | 0.7 |
Use of drip and sprinkler | I | 83.3 | 31 | 11.7 |
Incorporation of crops resistant to high temperatures | Cr | 80.9 | 2 | 0.7 |
Changes in planting and harvesting dates | Cr | 78.6 | 110 | 41.5 |
Afforestation | WSC | 76.2 | 5 | 1.9 |
Zero tillage | WSC | 69.0 | 3 | 1.1 |
Use of water accumulation systems | I | 66.7 | 38 | 14.3 |
Use of green manure | WSC | 66.0 | 33 | 12.4 |
Use of mulching | WSC | 61.9 | 24 | 9.0 |
Use of cover crops | WSC | 61.9 | 16 | 6.0 |
Other WSC practices | WSC | 61.9 | 16 | 6.0 |
Use of hoses and pumps for irrigation | I | 59.5 | 52 | 19.6 |
Implementation of infiltration trenches | WSC | 57.1 | 19 | 7.1 |
Cleaning of canals | WSC | 54.8 | 60 | 22.6 |
Variables | Climate Change Adaptation Measurement | ||
---|---|---|---|
Decision | Intensity | Quality | |
Constant (β0) | 4.1356 (0.9463) *** | 4.7996 (0.9253) *** | 4.7690 (0.9894) *** |
Land (β1) | 0.2284 (0.0849) *** | 0.2876 (0.0850) *** | 0.2726 (0.0877) *** |
Capital (β2) | 0.6184 (0.0739) *** | 0.5950 (0.0710) *** | 0.6041 (0.0779) *** |
Labor (β3) | 0.1224 (0.0278) *** | 0.1044 (0.0276) *** | 0.1140 (0.0275) *** |
Dryland (β4) | −0.3485 (0.1303) *** | −0.4280 (0.1222) *** | −0.3882 (0.1350) *** |
Diversification (β5) | 0.5670 (0.1312) *** | 0.5933 (0.1373) *** | 0.6074 (0.1361) *** |
Climate change adaptation (β6) | 0.1092 (0.3012) *** | 0.1656 (0.0546) *** | 0.0075 (0.0052) * |
Inefficiency Model | |||
Constant (δ0) | 0.2005 (0.6762) | 0.3462 (0.6594) | −0.3082 (0.6554) *** |
Age (δ1) | 0.0124 (0.0083) * | 0.0171 (0.0080) ** | 0.0212 (0.0084) *** |
Schooling (δ2) | 0.0200 (0.0175) | 0.0147 (0.0270) | 0.0107 (0.0296) |
Dependence (δ3) | −0.7099 (0.1738) *** | −0.8436 (0.1797) *** | −0.7310 (0.1800) *** |
Specialization (δ4) | −0.0085 (0.0034) *** | −0.0099 (0.0034) *** | −0.0112 (0.0031) *** |
Use of meteorological information (δ5) | −0.6258 (0.2770) ** | −0.8279 (0.2463) *** | −0.7480 (0.2556) *** |
Membership (δ6) | 0.2027 (0.1698) | 0.2533 (0.1742) * | 0.1915 (0.1884) |
Farm size (δ7) | −0.0036 (0.0008) *** | −0.0028 (0.0029) | −0.0035 (0.0026) * |
Distance to market (δ8) | 0.0085 (0.0033) *** | 0.0038 (0.0031) * | 0.0057 (0.0031) ** |
Returns to scale | 0.9692 | 0.9870 | 0.9907 |
Maximum Likelihood Function | −209.18 | −209.60 | −212.76 |
Sigma2 | 0.4209 (0.0731) *** | 0.4203 (0.0693) *** | 0.4828 (0.0747) *** |
Gamma | 0.5363 (0.1043) *** | 0.4247 (0.1111) *** | 0.5411 (0.0989) *** |
TE | 67.8 | 76.4 | 72.3 |
TE difference with models without correcting endogeneity | ns | *** | *** |
Interval TE | Farms in Interval (%) | ||||||
---|---|---|---|---|---|---|---|
Not-Correcting Endogeneity | Correcting Endogeneity | ||||||
Decision | Intensity | Quality | Decision | Intensity | Quality | ||
0–29 | 2.6 | 3.0 | 3.0 | 6.4 | 2.6 | 3.4 | |
30–39 | 9.1 | 5.3 | 5.3 | 7.9 | 3.0 | 4.5 | |
40–49 | 7.2 | 6.8 | 6.4 | 6.4 | 4.9 | 6.0 | |
50–59 | 10.6 | 6.4 | 6.4 | 9.1 | 6.0 | 6.4 | |
60–69 | 16.6 | 13.3 | 13.7 | 10.9 | 9.4 | 12.1 | |
70–79 | 25.6 | 23.0 | 23.0 | 22.7 | 16.7 | 23.4 | |
80–89 | 23.8 | 35.8 | 34.7 | 30.6 | 45.7 | 35.9 | |
>90 | 4.5 | 6.4 | 7.5 | 6.0 | 11.7 | 8.3 | |
Average TE | 67.5 | 71.3 | 71.5 | 67.8 | 76.4 | 72.3 | |
Correlation Matrix for TE Values | |||||||
Not-correcting for endogeneity | Decision | 1 | - | - | - | - | - |
Intensity | 0.9872 | 1 | - | - | - | - | |
Quality | 0.9876 | 0.9999 | 1 | - | - | - | |
Correcting for endogeneity | Decision | 0.9666 | 0.9779 | 0.9766 | 1 | - | - |
Intensity | 0.9532 | 0.9842 | 0.9841 | 0.9569 | 1 | - | |
Quality | 0.9874 | 0.9967 | 0.9969 | 0.9741 | 0.9839 | 1 |
Average TE | Model | Grouping Criteria | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Adoption of at Least One Irrigation Improvement | Changes in Planting and Harvesting Schedules | Adoption of at Least Two Adaptation Practices | Value of Adaptation Index ≥ 25% | ||||||||||
Yes | No | Sig | Yes | No | Sig | Yes | No | Sig | Yes | No | Sig | ||
Complete sample | Decision | 73.5 | 65.3 | *** | 64.9 | 70.0 | ** | 81.4 | 65.1 | *** | 86.5 | 65.4 | *** |
Intensity | 80.9 | 74.4 | *** | 75.1 | 77.3 | ns | 86.3 | 74.5 | *** | 88.8 | 74.8 | *** | |
Quality | 77.5 | 69.9 | *** | 70.9 | 73.3 | ns | 84.4 | 69.9 | *** | 87.3 | 70.3 | *** | |
% | 54.7 | 42.6 | 16.2 | 11.3 | |||||||||
Pencahue | Decision | 86.1 | 85.0 | ns | 85.7 | 85.7 | ns | 85.1 | 86.0 | ns | 86.0 | 85.2 | ns |
Intensity | 88.2 | 88.5 | ns | 87.8 | 88.7 | ns | 88.2 | 88.5 | ns | 88.1 | 88.5 | ns | |
Quality | 86.7 | 86.7 | ns | 86.3 | 87.0 | ns | 86.8 | 86.6 | ns | 86.7 | 86.7 | ns | |
% | 62.5 | 37.5 | 65.0 | 60.0 | |||||||||
Cauquenes | Decision | 45.2 | 50.2 | ns | 47.2 | 50.9 | ns | 41.7 | 49.4 | ns | - | - | |
Intensity | 62.0 | 62.8 | ns | 62.0 | 63.3 | ns | 63.6 | 62.6 | ns | - | - | ||
Quality | 55.7 | 58.2 | ns | 56.3 | 59.0 | ns | 59.6 | 57.6 | ns | - | - | ||
% | 21.0 | 48.1 | 3.7 | 0.0 | |||||||||
San Clemente | Decision | 82.2 | 77.0 | *** | 83.3 | 77.1 | *** | 81.6 | 78.2 | ns | 86.3 | 78.3 | ** |
Intensity | 86.3 | 81.7 | *** | 87.2 | 81.8 | *** | 87.6 | 82.5 | ** | 90.0 | 82.8 | ** | |
Quality | 83.2 | 77.4 | *** | 84.4 | 77.6 | *** | 85.0 | 78.4 | ** | 87.8 | 78.9 | ** | |
% | 31.5 | 24.7 | 13.5 | 4.5 | |||||||||
Parral | Decision | 66.5 | 64.1 | ns | 64.0 | 65.8 | ns | 79.5 | 64.0 | * | 93.1 | 63.5 | *** |
Intensity | 80.0 | 76.3 | ns | 76.7 | 77.6 | ns | 89.0 | 76.6 | * | 94.5 | 76.4 | ** | |
Quality | 75.7 | 70.9 | ns | 71.2 | 71.6 | ns | 86.1 | 71.2 | * | 92.9 | 71.0 | *** | |
% | 20.0 | 67.3 | 3.6 | 3.6 |
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Roco, L.; Bravo-Ureta, B.; Engler, A.; Jara-Rojas, R. The Impact of Climatic Change Adaptation on Agricultural Productivity in Central Chile: A Stochastic Production Frontier Approach. Sustainability 2017, 9, 1648. https://doi.org/10.3390/su9091648
Roco L, Bravo-Ureta B, Engler A, Jara-Rojas R. The Impact of Climatic Change Adaptation on Agricultural Productivity in Central Chile: A Stochastic Production Frontier Approach. Sustainability. 2017; 9(9):1648. https://doi.org/10.3390/su9091648
Chicago/Turabian StyleRoco, Lisandro, Boris Bravo-Ureta, Alejandra Engler, and Roberto Jara-Rojas. 2017. "The Impact of Climatic Change Adaptation on Agricultural Productivity in Central Chile: A Stochastic Production Frontier Approach" Sustainability 9, no. 9: 1648. https://doi.org/10.3390/su9091648