R&D Innovation Adoption, Climatic Sensitivity, and Absorptive Ability Contribution for Agriculture TFP Growth in Pakistan
Abstract
:1. Introduction
2. Materials & Methods
2.1. Net Capital Stock
2.2. Empirical Model
2.3. Data and Data Sources
3. Results and Discussion
3.1. Agriculture TFP Growth
3.2. Stationarity Results
3.3. ARDL Estimates
3.4. Endogeneity and Diagnostic Test Results
3.5. Two Stage Least Square Results
4. Conclusions and Recommendations
- The government and research institutions should increase the agriculture R&D innovation expenditures to increase agriculture productivity.
- The research institutions and government should focus on innovative seed development (like hybrid seeds) and its early spillovers for higher agriculture productivity.
- Urbanization has caused a reduction in cultivation land and lowered the agriculture output. So, the government should focus to enhance the under-cultivation of land to avoid food security challenges in Pakistan.
- The government must develop and implement the extension services to educate the farmers about technological innovation and efficient resource utilization.
- The government and technology developing agencies should focus on farmers’ expertise, knowledge-based training, skills-based workshops, capacity building, and community-led experiences to improve the absorptive ability of new technology.
- Farmers should also focus on the adoption of climate smart agriculture, which can be achieved through a proper utilization of rainwater. For this purpose, the government needs to develop small community dams and large-scale dams for the timely use of rainwater.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Abbreviation | Variables | Measurement Units |
---|---|---|
TFPt | Total Factor Productivity | Measured through Cobb Douglas and Translog Production Function |
AIt | Agriculture Investment | Million Rupees |
AL | Agriculture Land | Percentage share in total Land |
ATt | Agriculture Tractor | Total in numbers |
AEMPt | Agriculture Employment | Percent share in total Employment |
HCt | Human Capital index | Index developed by panne world table 10.1 |
RFt | Average Annual Rainfall | Millimeter |
FCt | Fertilizer Consumption in (000) tons | 000 Tonnes |
SD | Innovative Seeds Distribution | Tonnes |
INTt | Interactive term of Human capital with Agriculture Tractors, Seed Distributions, and Fertilizers Consumptions | To Capture the Absorptive Ability |
Variables | Level | First Difference | ||
---|---|---|---|---|
T-Stat | p-Value | T-Stat | p-Value | |
TFPt | 0.0060 | (0.6796) | −6.1420 | (0.0000) * |
ALt | 2.8426 | (0.0236) * | 5.1769 | (0.0000) |
AIt | −1.3215 | (0.1693) | −3.2986 | (0.0016) * |
LAEMPt | −1.1515 | (0.6874) | −8.2817 | (0.0000) * |
LHCt | −1.2886 | (0.6270) | −6.6419 | (0.0000) * |
LRFt | −5.4196 | (0.0003) * | −5.9350 | (0.0001) |
LATt | 0.0791 | (0.9607) | −8.4761 | (0.0000) * |
SDt | (1.3291) | (0.4932) | 6.1674 | (0.0000) * |
LFCTt | −0.1679 | (0.9345) | −5.0333 | (0.0002) * |
Model 1 | Model 2 | Model 3 | ||||
---|---|---|---|---|---|---|
Variables | Coefficient | Prob | Coefficient | Prob | Coefficient | Prob |
C | −5.6559 | 0.0000 *** | 3.9484 | 0.0276 ** | −2.3173 | 0.0000 *** |
LALt | 0.2625 | 0.0188 ** | 0.1286 | 0.0292 ** | 0.0727 | 0.2535 |
LAEMPt | 0.08547 | 0.0921 * | 0.0206 | 0.8421 | 0.1993 | 0.8016 |
LAIt | 0.1914 | 0.0102 ** | 0.0298 | 0.0001 *** | 0.5134 | 0.0001 *** |
LHCt | 0.2876 | 0.4220 | 0.2560 | 0.1934 | 0.0591 | 0.3562 |
LRFt | 0.0073 | 0.0146 ** | 0.0102 | 0.7351 | 0.1726 | 0.0008 *** |
INTt | −0.06431 | 0.0515 * | −0.0689 | 0.0066 ** | −0.5887 | 0.0002 *** |
LATt | 0.2865 | 0.0086 *** | ||||
LSDt | 0.2475 | 0.0282 ** | ||||
LFCt | 0.1769 | 0.0001 *** | ||||
ARDL Bounds Test | F-Statistic | 5.1832 | F-statistic | 5.0530 | F-statistic | 16.7837 |
Endogeneity Test | Prob-Value | |
---|---|---|
J-statistic | 25.436 | (0.1466) |
Instrument Rank | 19 | |
Difference in J-stats | 3.4283 | (0.9047) |
Restricted J-statistic | 31.460 | |
Unrestricted J-statistic | 28.032 | |
Autocorrelation | ||
Prob. Chi-Square | 0.1830 | |
Heteroscedasticity Test | ||
F-Statistic | 0.0524 | (0.4225) |
Normality Test | ||
Jarque–Bera Test | 2.004 | (0.367) |
Model 1 | Model 2 | Model 3 | ||||
---|---|---|---|---|---|---|
Variables | Coefficient | Prob | Coefficient | Prob | Coefficient | Prob |
D(LTFP(−1)) | 0.0953 | 0.5436 | 0.3232 | 0.6231 | 0.5219 | 0.2710 |
D(LAL) | 0.2542 | 0.0494 ** | 0.2524 | 0.0917 * | −0.0702 | 0.2808 |
D(LAL(−1)) | 0.0437 | 0.7240 | 0.2115 | 0.7429 | −0.0226 | 0.6749 |
D(LAEMP) | 0.0409 | 0.1120 | −0.0114 | 0.8399 | 0.0271 | 0.5102 |
D(LAEMP(−1)) | 0.3826 | 0.3411 | −0.0435 | 0.4813 | −0.1038 | 0.0067 *** |
D(LAI) | 0.0182 | 0.8102 | −0.0002 | 0.9475 | 0.1297 | 0.2510 |
D(LAI(−1)) | 0.0767 | 0.4268 | 0.0147 | 0.0255 ** | −0.0861 | 0.4476 |
D(LHC) | 0.7468 | 0.1356 | 0.0142 | 0.0717 * | 0.0106 | 0.3214 |
DLHC(−1)) | 0.7219 | 0.1317 | 0.0185 | 0.0743 * | 0.0491 | 0.0018 *** |
D(LRF) | 0.0045 | 0.0251 ** | 0.0056 | 0.7457 | 0.4705 | 0.1002 |
D(LRF(−1)) | −0.0015 | 0.2216 | 0.0054 | 0.7366 | −0.6895 | 0.0131 ** |
D(INT) | −1.4814 | 0.1305 | −0.0310 | 0.0684 * | −2.4624 | 0.0077 *** |
D(INT(−1)) | −1.4169 | 0.1264 | −0.0450 | 0.0552 * | −0.0815 | 0.9539 |
D(LAT) | 0.9725 | 0.3580 | ||||
D(LAT(−1)) | 0.4962 | 0.0842 *** | ||||
D(LSD) | 0.1592 | 0.0083 *** | ||||
D(LSD(−1)) | 0.1700 | 0.0136 *** | ||||
D(LFCT) | 0.6958 | 0.3146 | ||||
D(LFCT(−1)) | −0.8848 | 0.2822 | ||||
ECM (−1) | −1.2128 | 0.0001 *** | −0.5536 | 0.0019 *** | −1.1577 | 0.0000 *** |
Autocorrelation (Breusch–Godfrey) | 0.0862 (0.9176) | 0.5222 (0.5983) | 2.2928 (0.1376) | |||
Heteroskedasticity (Breusch–Pagan–Godfrey) | 1.2323 (0.3035) | 1.3594 (0.2341) | 0.3984 (0.9840) | |||
Normality (Jarque–Bera) | 2.6202 (0.2697) | 4.3351 (0.1035) | 1.8697 (0.3925) |
Variable | Coefficient | p-Values |
---|---|---|
C | 1.8428 | 0.0138 ** |
LAIt | 0.0161 | 0.0005 *** |
LAEMPt | −0.0883 | 0.4002 |
LALt | 0.2151 | 0.0173 *** |
LHCt | 0.6950 | 0.3063 |
LRFt | 0.0976 | 0.0000 *** |
INTt | −0.0453 | 0.0038 *** |
LATt | 0.0731 | 0.0637 * |
LSDt | 0.1209 | 0.0392 ** |
LFCTt | 0.0718 | 0.0189 ** |
F-statistic | 133.126 | (0.000) *** |
R-squared | 0.93 |
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Usman, M.; Hameed, G.; Saboor, A.; Almas, L.K.; Hanif, M. R&D Innovation Adoption, Climatic Sensitivity, and Absorptive Ability Contribution for Agriculture TFP Growth in Pakistan. Agriculture 2021, 11, 1206. https://doi.org/10.3390/agriculture11121206
Usman M, Hameed G, Saboor A, Almas LK, Hanif M. R&D Innovation Adoption, Climatic Sensitivity, and Absorptive Ability Contribution for Agriculture TFP Growth in Pakistan. Agriculture. 2021; 11(12):1206. https://doi.org/10.3390/agriculture11121206
Chicago/Turabian StyleUsman, Muhammad, Gulnaz Hameed, Abdul Saboor, Lal K. Almas, and Muhammad Hanif. 2021. "R&D Innovation Adoption, Climatic Sensitivity, and Absorptive Ability Contribution for Agriculture TFP Growth in Pakistan" Agriculture 11, no. 12: 1206. https://doi.org/10.3390/agriculture11121206