Popularization of plant protection UAVs and farmers’ income increases: a quasinatural experiment

References

• Abd Kharim MN, Wayayok A, Shariff ARM, Abdullah AF, Husin EM. 2019. Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation. Comput Electron Agr. 167:105045. doi: 10.1016/j.compag.2019.105045.
   Web of Science ®Google Scholar
• Adamopoulos T, Brandt L, Leight J, Restuccia D. 2022. Misallocation, selection, and productivity: a quantitative analysis with panel data from China. Econometrica. 90(3):1261–1282. doi: 10.3982/ECTA16598.
   Web of Science ®Google Scholar
• Ahmed S, Qiu B, Kong C, Xin H, Ahmad F, Lin J. 2022. A data-driven dynamic obstacle avoidance method for liquid-carrying plant protection UAVs. Agronomy. 12(4):873. doi: 10.3390/agronomy12040873.
   Google Scholar
• Amare M, Shiferaw B, Takeshima H, Mavrotas G. 2021. Variability in agricultural productivity and rural household consumption inequality: evidence from Nigeria and Uganda. Agr Econ-Blackwell. 52(1):19–36. doi: 10.1111/agec.12604.
   Web of Science ®Google Scholar
• Ao G, Liu Q, Qin L, Chen M, Liu S, Wu W, Xue B. 2021. Organization model, vertical integration, and farmers’ income growth: empirical evidence from large-scale farmers in Lin’ an, China. PLOS ONE. 16(6):e0252482. doi: 10.1371/journal.pone.0252482.
   PubMed Web of Science ®Google Scholar
• Bhoi SK, Jena KK, Panda SK, Long H, Kumar R, Subbulakshmi P, Jebreen H. 2021. An internet of things assisted unmanned aerial vehicle based artificial intelligence model for rice pest detection. Microprocess Microsy. 80:103607. doi: 10.1016/j.micpro.2020.103607.
   Web of Science ®Google Scholar
• Bhuiyan MA, Davit M, Zeng X, Zhang Z. 2022. The impact of agricultural insurance on farmers’ income: Guangdong province (China) as an example. PLOS ONE. 17(10):e0274047. doi: 10.1371/journal.pone.0274047.
   PubMed Web of Science ®Google Scholar
• Biglia A, Grella M, Bloise N, Comba L, Mozzanini E, Sopegno A, Pittarello M, Dicembrini E, Alcatrão LE, Guglieri G, et al. 2022. UAV-spray application in vineyards: flight modes and spray system adjustment effects on canopy deposit, coverage, and off-target losses. Sci Total Environ. 845:1–17. doi: 10.1016/j.scitotenv.2022.157292.
   Web of Science ®Google Scholar
• Chen H, Lan Y, Fritz BK, Hoffmann WC, Liu S. 2021. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). Int J Agr Biol Eng. 14(1):38–49. doi: 10.25165/j.ijabe.20211401.5714.
   Web of Science ®Google Scholar
• Chen S, Lan Y, Zhou Z, Ouyang F, Wang G, Huang X, Deng X, Cheng S. 2020. Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy-Basel. 10(2):195. doi: 10.3390/agronomy10020195.
   Web of Science ®Google Scholar
• Clay N, Zimmerer KS. 2020. Who is resilient in Africa’s green revolution? sustainable intensification and climate smart agriculture in Rwanda. Land Use Policy. 97:104558. doi: 10.1016/j.landusepol.2020.104558.
   PubMed Web of Science ®Google Scholar
• de Camargo T, Schirrmann M, Landwehr N, Dammer KH, Pflanz M. 2021. Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops. Remote Sens-Basel. 13(9):1704. doi: 10.3390/rs13091704.
   Web of Science ®Google Scholar
• Deng X, Zhang M, Wan C. 2022. The impact of rural land right on farmers’ income in underdeveloped areas: evidence from micro-survey data in Yunnan province, China. Land. 11(10):1780. doi: 10.3390/land11101780.
   Web of Science ®Google Scholar
• Du J, Liu H, Yuan Y. 2023a. On the impacts of agricultural subsidies on agricultural carbon emissions in China: empirical evidence from microdata of rice production. Environ Sci Pollut R. 30(45):101151–101167. doi: 10.1007/s11356-023-29096-2.
   PubMed Web of Science ®Google Scholar
• Du J, Zhang Y, Liu B, Dong R. 2023b. Impact of digital village construction on agricultural green total factor productivity and its mechanisms. China Popul, Res Environ. 33(2):165–175.
   Google Scholar
• Enos JK, Ward MP, Hauber ME. 2021. A review of the scientific evidence on the impact of biologically salient frightening devices to protect crops from avian pests. Crop Prot. 148:105734. doi: 10.1016/j.cropro.2021.105734.
   Web of Science ®Google Scholar
• Han J. 2010. Analysis on the influence of my country’s agricultural machinery purchase subsidy policy on farmers’ income. Product Res. 3:52–53+81.
   Google Scholar
• Hu P, Zhang R, Yang J, Chen L. 2022. Development status and key technologies of plant protection UAVs in china: a review. Drones. 6(11):354. doi: 10.3390/drones6110354.
   Google Scholar
• Iost FH, Pazini JD, Alves TM, Koch RL, Yamamoto PT. 2022. How does the digital transformation of agriculture affect the implementation of integrated pest management? Front Sustain Food S. 6:972213. doi: 10.3389/fsufs.2022.972213.
   Google Scholar
• Ivezić A, Trudić B, Stamenković Z, Kuzmanović B, Perić S, Ivošević B, Buđen M, Petrović K. 2023. Drone-related agrotechnologies for precise plant protection in western Balkans: applications, possibilities, and legal framework limitations. Agronomy. 13(10):2615. doi: 10.3390/agronomy13102615.
   Google Scholar
• Jiang T. 2022. Mediating effects and moderating effects in causal inference. China Ind Econ. 5:100–120.
   Google Scholar
• Jiang X, Zhong S, Huang C, Guo X, Zhao J. 2022. Blessing or curse? the impacts of non-agricultural part-time work of the large farmer households on agricultural labor productivity. Technol Econ Dev Eco. 28(1):26–48. doi: 10.3846/tede.2021.15134.
   Web of Science ®Google Scholar
• Jin S, Jiang A, Bao B. 2023. Can China’s transfer payment in key ecological function areas reduce the carbon intensity? -quasi-natural experimental evidence from Jiangxi, China. Ecol Indic. 154:110537. doi: 10.1016/j.ecolind.2023.110537.
   Web of Science ®Google Scholar
• Kuang F, Li J, Jin J, Qiu X. 2023. Do green production technologies improve household income? evidence from rice farmers in China. Land. 12(10):1848. doi: 10.3390/land12101848.
   Web of Science ®Google Scholar
• Labelle ER, Hansson L, Högbom L, Jourgholami M, Laschi A. 2022. Strategies to mitigate the effects of soil physical disturbances caused by forest machinery: a comprehensive review. Curr For Rep. 8(1):20–37. doi: 10.1007/s40725-021-00155-6.
   Web of Science ®Google Scholar
• Li H, Chen L, Zhang Z. 2023. A study on the utilization rate and influencing factors of small agricultural machinery: evidence from 10 hilly and mountainous provinces in China. Agriculture. 13(1):51. doi: 10.3390/agriculture13010051.
   Google Scholar
• Li Y, Fan Z, Jiang G, Quan Z. 2021. Addressing the differences in farmers’ willingness and behavior regarding developing green agriculture-a case study in Xichuan county, China. Land. 10(3):316. doi: 10.3390/land10030316.
   Web of Science ®Google Scholar
• Li H, Peng S, Huang D. 2022. Policy effect and optimization strategy for plant protection UAV subsidy system in southern China. J China Agric Univ. 27(1):287–296.
   Google Scholar
• Liu Y, Ou Y, Cai Y. 2021. Evaluation of China’s agricultural green TFP and its spatiotemporal evolution characteristics. J Quantit Technol Econ. 38(5):39–56.
   Google Scholar
• Liu Y, Qiao J, Xiao J, Han D, Pan T. 2022. Evaluation of the effectiveness of rural revitalization and an improvement path: a typical old revolutionary cultural area as an example. Int J Env Res Pub He. 19(20):13494. doi: 10.3390/ijerph192013494.
   PubMed Web of Science ®Google Scholar
• Li R, Yu Y. 2022. Impacts of green production behaviors on the income effect of rice farmers from the perspective of outsourcing services: evidence from the rice region in Northwest China. Agriculture. 12(10):1682. doi: 10.3390/agriculture12101682.
   Google Scholar
• Luo L, Qiao D, Zhang R, Luo C, Fu X, Liu Y. 2022. Research on the influence of education of farmers’ cooperatives on the adoption of green prevention and control technologies by members: evidence from rural China. Int J Env Res Pub He. 19(10):6255. doi: 10.3390/ijerph19106255.
   PubMed Web of Science ®Google Scholar
• Meng Y, Song J, Lan Y, Mei G, Liang Z, Han Y. 2019. Harvest aids efficacy applied by unmanned aerial vehicles on cotton crop. Ind Crops Prod. 140:111645. doi: 10.1016/j.indcrop.2019.111645.
   Web of Science ®Google Scholar
• Mueller V, Masias I, Vallury S. 2019. Labor-saving technologies and structural transformation in northern Ghana. Agr Econ-Blackwell. 50(5):581–594. doi: 10.1111/agec.12511.
   Web of Science ®Google Scholar
• Nguyen LD, Raabe K, Grote U. 2015. Rural-urban migration, household vulnerability, and welfare in Vietnam. World Dev. 71:79–93. doi: 10.1016/j.worlddev.2013.11.002.
   Web of Science ®Google Scholar
• Pan Y, Shi K, Zhao Z, Li Y, Wu J. 2024. The effects of China’s poverty eradication program on sustainability and inequality. Hum Soc Sci Commun. 11(1):119. doi: 10.1057/s41599-024-02631-3.
   Google Scholar
• Pawłowska A, Grochowska R. 2021. “Green” transformation of the common agricultural policy and its impact on farm income disparities. Energies. 14(24):8242. doi: 10.3390/en14248242.
   Web of Science ®Google Scholar
• Peng J, Zhao Z, Liu D. 2022. Impact of agricultural mechanization on agricultural production, income, and mechanism: evidence from Hubei province, China. Front Env Sci-Switz. 10:838686. doi: 10.3389/fenvs.2022.838686.
   Web of Science ®Google Scholar
• Quan X, Doluschitz R. 2021. Factors influencing the adoption of agricultural machinery by Chinese maize farmers. Agriculture. 11(11):1090. doi: 10.3390/agriculture11111090.
   Google Scholar
• Rajkhowa P, Qaim M. 2022. Mobile phones, off-farm employment and household income in rural India. J Agr Econ. 73(3):789–805. doi: 10.1111/1477-9552.12480.
   Web of Science ®Google Scholar
• Rong J, Hong J, Guo Q, Fang Z, Chen S. 2023. Path mechanism and spatial spillover effect of green technology innovation on agricultural CO2 emission intensity: a case study in Jiangsu province, China. Ecol Indic. 157(15):111147. doi: 10.1016/j.ecolind.2023.111147.
   Google Scholar
• Sarri D, Martelloni L, Rimediotti M, Lisci R, Lombardo S, Vieri M. 2019. Testing a multi-rotor unmanned aerial vehicle for spray application in high slope terraced vineyard. J Agric Eng-Italy. 50(1):38–47. doi: 10.4081/jae.2019.853.
   Web of Science ®Google Scholar
• Sunak Y, Madlener R. 2016. The impact of wind farm visibility on property values: a spatial difference-in-differences analysis. Energ Econ. 55:79–91. doi: 10.1016/j.eneco.2015.12.025.
   Web of Science ®Google Scholar
• Tan C, Tao J, Yi L, He J, Huang Q. 2022. Dynamic relationship between agricultural technology progress, agricultural insurance and farmers’ income. Agriculture. 12(9):1331. doi: 10.3390/agriculture12091331.
   Google Scholar
• Wang J, Dong X, Qiao H, Dong K. 2020. Impact assessment of agriculture, energy and water on CO2 emissions in China: untangling the differences between major and non-major grain-producing areas. Appl Econ. 52(60):6482–6497. doi: 10.1080/00036846.2020.1796920.
   Web of Science ®Google Scholar
• Wang L, Huang X, Li W, Yan K, Han Y, Zhang Y, Pawlowski L, Lan Y. 2022. Progress in agricultural Unmanned Aerial Vehicles (UAVs) applied in China and prospects for Poland. Agriculture. 12(3):397. doi: 10.3390/agriculture12030397.
   Google Scholar
• Wang Y, Lu H, Chen Y, Yang P, Cheng X, Xie F. 2023. The impact of farmland management rights mortgage loan on the agri-food industrial agglomeration: case of Hubei province. Land. 12(7):1389. doi: 10.3390/land12071389.
   Web of Science ®Google Scholar
• Wang Y, Weng F, Huo X. 2023. Can digital finance promote professional farmers’ income growth in China?-an examination based on the perspective of income structure. Agriculture. 13(5):1103. doi: 10.3390/agriculture13051103.
   Google Scholar
• Wang J, Xiao H. 2007. Analysis on the effect of my country’s fine seed subsidies, agricultural machinery subsidies and agricultural tax reduction and exemption policies. Iss Agric Econ. 2:24–28.
   Google Scholar
• Wang X, Xu Z, Li G, Zhuo Y, Zou W. 2023. Farmland transfer and income distribution effect of heterogeneous farmers with livelihood capital: evidence from CFPS. Land. 12(7):1398. doi: 10.3390/land12071398.
   Web of Science ®Google Scholar
• Wang W, Zhang Y. 2020. Agricultural productivity, factor endowment and rural household income: theoretical and empirical research based on CHIP data. Fin Trade Res. 31(7):61–74.
   Google Scholar
• Wei S, Lu Y. 2024. Adoption mode of agricultural machinery and food productivity: evidence from China. Front Sustain Food S. 7:1257918. doi: 10.3389/fsufs.2023.1257918.
   Google Scholar
• Wu F. 2022. Adoption and income effects of new agricultural technology on family farms in China. PLOS ONE. 17(4):e0267101. doi: 10.1371/journal.pone.0267101.
   PubMed Web of Science ®Google Scholar
• Wu L, Liu R. 2021. Awakening sleeping capital: income leverage effect of rural land mortgage loans. J Fin Econ. 9:108–122.
   Google Scholar
• Yang J. 2022. Information-theoretic model of induced technical change: theory and empirics. Metroeconomica. 74(1):2–39. doi: 10.1111/meca.12399.
   Web of Science ®Google Scholar
• Yang W, Xu C, Kong F. 2022. Does non-food cultivation of cropland increase farmers’ income? Int J Env Res Pub He. 19(12):7329. doi: 10.3390/ijerph19127329.
   PubMed Web of Science ®Google Scholar
• Yang D, Zhang H, Liu Z, Zeng Q. 2021. Do cooperatives participation and technology adoption improve farmers’ welfare in China? a joint analysis accounting for selection bias. J Integr Agr. 20(6):1716–1726. doi: 10.1016/S2095-3119(20)63325-1.
   Web of Science ®Google Scholar
• Yan Y, Lan Y, Wang G, Hussain M, Wang H, Yu X, Shan C, Wang B, Song C. 2023. Evaluation of the deposition and distribution of spray droplets in citrus orchards by plant protection drones. Front Plant Sci. 14:1303669. doi: 10.3389/fpls.2023.1303669.
   PubMed Web of Science ®Google Scholar
• Yu S, Zhu J, Zhou J, Cheng J, Bian X, Shen J, Wang P. 2022. Key technology progress of plant-protection UAVs applied to mountain orchards: a review. Agronomy. 12(11):2828. doi: 10.3390/agronomy12112828.
   Google Scholar
• Zhang S, Wei C, Shao J, Wu Z. 2016. Technical efficiency and its determinants of the various cropping systems in the purple-soiled, hilly region of southwestern China. J Mt Sci-Eng. 13(12):2205–2223. doi: 10.1007/s11629-015-3679-3.
   Web of Science ®Google Scholar
• Zhao Y, Luo E, Li J. 2019. The impacts of the investment in the integrated agricultural development projects on rural household income: an analysis based on China’s provincial panel data. Chin Rural Econ. 5:22–37.
   Google Scholar
• Zheng S, Wang Z, Wachenheim CJ. 2019. Technology adoption among farmers in Jilin province, China: the case of aerial pesticide application. China Agr Econ Rev. 11(1):206–216. doi: 10.1108/CAER-11-2017-0216.
   Web of Science ®Google Scholar
• Zheng S, Yu L, Fu H. 2023. Has rural e-commerce increased potato farmers’ income? evidence from the potato home of China. Potato Res. 67(1):15–35. doi: 10.1007/s11540-023-09614-y.
   Web of Science ®Google Scholar
• Zhou L, Luo J. 2023. Dilemmas and theoretical exploration of China’s rural land property rights system reform. Front Sustain Food S. 7:1238241. doi: 10.3389/fsufs.2023.1238241.
   Google Scholar
• Zhou Z, Zhang C, Peng C, Kong X. 2016. Agricultural mechanization and farmers’ income: evidence from the subsidy policy for the purchase of agricultural machinery. Chin Rural Econ. 2:68–82.
   Google Scholar
• Zou Y, Wang Q. 2022. Impacts of farmer cooperative membership on household income and inequality: evidence from a household survey in China. Agr Food Econ. 10(1):17. doi: 10.1186/s40100-022-00222-x.
   Google Scholar