Effects of digital agriculture solutions on smallscale wage workers and employment

References

• Aakvik, A., Heckman, J. J., & Vytlacil, E. J. (2005). Estimating treatment effects for discrete outcomes when responses to treatment vary: An application to Norwegian vocational rehabilitation programs. Journal of Econometrics, 125(1–2), 1–19. https://doi.org/10.1016/j.jeconom.2004.04.002
   Web of Science ®Google Scholar
• Abegunde, V. O., Sibanda, M., & Obi, A. (2020). Mainstreaming climate-smart agriculture in small-scale farming systems: A holistic nonparametric applicability assessment in South Africa. Agriculture, 10(3), 52. https://doi.org/10.3390/agriculture10030052
   Google Scholar
• Abokyi, M. G. (2013). Exploring the farmer-based organisation (FBO) extension approach. A case study of an NGO in Northern Ghana. MSc Research Project. Van Hall Larenstein University of Applied Sciences.
   Google Scholar
• Abubakari, S. T. (2022). Electronic Agriculture extension and the rural Farmer: the nexus of Technology and literacy [Unpublished Doctoral dissertation]. University for Development Studies.
   Google Scholar
• Acemoglu, D., & Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488–1542. https://doi.org/10.1257/aer.20160696
   Web of Science ®Google Scholar
• Achukwu, G. A., Sennuga, S. O., Bamidele, J., Alabuja, F. O., Bankole, O. L., & Barnabas, T. M. (2023). Factors affecting the rat e of adoption of agricultural technology among small scale rice farmers in Gwagwalada Area Council of FCT. Journal of Agricultural Science and Practice, 8(2), 30–37.
   Google Scholar
• Adaku, A. A., Tsiboe, F., & Clottey, J. (2023). Taking stock of gender gaps in crop production technology adoption and technical efficiency in Ghana. Agrekon, 62(1), 31–48. https://doi.org/10.1080/03031853.2022.2150664
   Web of Science ®Google Scholar
• Agumagu, A., & Nwaogwugwu, O. N. (2008). Extension agents’ commitment to extension work in Abia and Rivers States, Nigeria. Global Approaches to Extension Practice: A Journal of Agricultural Extension, 2(1), 50–59. https://doi.org/10.4314/gaep.v2i1.34950
   Google Scholar
• Ahmed, M. H., & Melesse, K. A. (2018). Impact of off-farm activities on technical efficiency: Evidence from maize producers of eastern Ethiopia. Agricultural and Food Economics, 6(1), 1–15. https://doi.org/10.1186/s40100-018-0098-0
   Google Scholar
• Ahmed, H., & Anang, B. T. (2019). Impact of improved variety adoption on farm income in Tolon district of Ghana. Agricultural Social Economic Journal, 19(2), 105–115. https://doi.org/10.21776/ub.agrise.2019.019.2.5
   Google Scholar
• Akorsu, A. D., & Britwum, A. O. (2022). The architecture of players in Ghana’s digitalising agriculture. WITS University, Southern Centre for Inequality Studies.
   Google Scholar
• Akrong, R., Akorsu, A. D., Jha, P., & Agyenim, J. B. (2022). Assessing the trade and welfare effects of certification schemes: The case of GlobalGAP in Ghana’s mango sector. Scientific African, 18, e01425. https://doi.org/10.1016/j.sciaf.2022.e01425
   Google Scholar
• Akudugu, M. A., Nkegbe, P. K., Wongnaa, C. A., & Millar, K. K. (2023). Technology adoption behaviors of farmers during crises: What are the key factors to consider? Journal of Agriculture and Food Research, 14, 100694. https://doi.org/10.1016/j.jafr.2023.100694
   Google Scholar
• Alston, J. M. (2010). The benefits from agricultural research and development, innovation and productivity growth. OECD Food, Agriculture and Fisheries Working Papers No. 31.
   Google Scholar
• Alston, J. M., Andersen, M. A., James, J. S., & Pardey, P. G. (2010). Persistence pays: U.S. Agricultural productivity growth and the benefits from public R&D spending. Springer.
   Google Scholar
• Alston, J. M., Beddow, J. M., & Pardey, P. G. (2009). Agricultural research, productivity, and food prices in the long run. Science, 325(5945), 1209–1210. https://doi.org/10.1126/science.1170451
   PubMed Web of Science ®Google Scholar
• Anang, B. T., & Apedo, C. K. (2023). The influence of off-farm work on farm income among smallholder farm households in northern Ghana. Cogent Economics & Finance, 11(1), 2196861. https://doi.org/10.1080/23322039.2023.2196861
   Web of Science ®Google Scholar
• Anang, B. T.,Bäckman, S., &Sipiläinen, T. (2020). Adoption and income effects of agricultural extension in northern Ghana. Scientific African, 7, e00219.https://doi.org/10.1016/j.sciaf.2019.e00219
   Google Scholar
• Andrade-Sanchez, P., & Heun, J. T. (2010). Understanding technical terms and acronyms used in precision agriculture, The University of Arizona. Arizona Cooperative Extension Bulletin AZ1534.
   Google Scholar
• Asuming-Brempong, S. (2004). African Association of Agricultural Economists. Shaping the future of African agriculture for development: The role of social scientists [Paper presentation]. Proceedings of the Inaugural Symposium, 6 to 8 December 2004, Grand Regency Hotel, Nairobi, Kenya. The Role of Agriculture in the Development Process: Recent Experiences from Ghana.
   Google Scholar
• Aubron, C., Bainville, S., Philippon, O., & Dorin, B. (2022). Neither corporate, nor family: The Indian “patronal” farm. Frontiers in Sustainable Food Systems, 6, 850545. https://doi.org/10.3389/fsufs.2022.850545
   Google Scholar
• Azumah, S. B., Mahama, A., & Donkoh, S. A. (2020). Modelling the determinants of adoption of multiple climate change coping and adaptation strategies. A micro analysis of smallscale farmers in northern Ghana. Review of Agricultural and Applied Economics, 23(1), 30–37. https://doi.org/10.15414/raae.2020.23.01.30-37
   Google Scholar
• Basok, T. (2002). The farmers’ affliction. Tortillas and Tomatoes: Transmigrant Mexican Harvesters in Canada (pp. 25–41). McGill-Queen’s University Press.
   Google Scholar
• Birner, R., Daum, T., & Pray, C. (2021). Who drives the digital revolution in agriculture? A review of supply-side trends, players and challenges. Applied Economic Perspectives and Policy, 43(4), 1260–1285. https://doi.org/10.1002/aepp.13145
   Web of Science ®Google Scholar
• Bontsa, N. V., Mushunje, A., & Ngarava, S. (2023). Factors influencing the perceptions of smallscale farmers towards adoption of digital technologies in Eastern Cape Province, South Africa. Agriculture, 13(8), 1471. https://doi.org/10.3390/agriculture13081471
   Google Scholar
• Byerlee, D., de Janvry, A., & Sadoulet, E. (2009). Agriculture for development: Toward a new paradigm. Annual Review of Resource Economics, 1(1), 15–31. https://doi.org/10.1146/annurev.resource.050708.144239
   Web of Science ®Google Scholar
• Dash, B., Sharma, P., & Swayamsiddha, S. (2023). Organizational digital transformations and the importance of assessing theoretical frameworks such as TAM, TTF, and UTAUT: A review. International Journal of Advanced Computer Science and Applications, 14(2), 1–6. https://doi.org/10.14569/IJACSA.2023.0140201
   Web of Science ®Google Scholar
• Deininger, K., & Xia, F. (2016). Quantifying spill over effects from large land-based investment: The case of Mozambique. World Development, 87, 227–241. https://doi.org/10.1016/j.worlddev.2016.06.016
   Web of Science ®Google Scholar
• Dey, S., & Singh, P. K. (2023). Estimating farmers’ intention towards institutional credit adoption by using extended theory of planned behavior. Journal of Applied Social Psychology, 53(7), 684–703. https://doi.org/10.1111/jasp.12952
   Web of Science ®Google Scholar
• Diiro, G. M. (2013). Impact of off-farm income on agricultural technology adoption intensity and productivity. Agricultural Economics, 11, 1–15.
   Google Scholar
• Donkor, E. A., Garnevska, E., Siddique, M. I., & Donkor, E. (2021). Determinants of rice farmer participation in the direct marketing channel in Ghana. Sustainability, 13(9), 5047. https://doi.org/10.3390/su13095047
   Web of Science ®Google Scholar
• Dufty, N., Martin, P., & Zhao, S. (2019). Demand for farm workers: ABARES farm survey results 2018. https://www.agriculture.gov.au/abares/research-topics/agricultural-workforce/labour-survey-2018
   Google Scholar
• Eaton, C., & Shepherd, A. W. (2001). Contract farming: Partnership for growth. FAO.
   Google Scholar
• El Intidami, M. E., & Benamar, F. (2021). The role of farmer personal characteristics in the adoption of localized irrigation technology (LIT). International Journal of Accounting, Finance, Auditing, Management and Economics, 2(2), 280–301.
   Google Scholar
• Enu, P., Hagan, E., Ahouandjinou, E., & Attah-Obeng, P. (2014). Relationship between education and wage differentials in Ghana: a case study of Accra-a Suburb of greater Accra Region. International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, 4(1), 277–294.
   Google Scholar
• Engwali, F., Dinga, L., & Chotangui, A. (2019). Comparative analysis of improved maize (Zea mays L.) seeds adoption on smallholder farmers’ performance in Fako division, south west region, Cameroon. Economics, 4(6), 267–274.
   Google Scholar
• Franklin, J., Rostom, M., & Thwaites, G. (2020). The banks that said no: The impact of credit supply on productivity and wages. Journal of Financial Services Research, 57(2), 149–179. https://doi.org/10.1007/s10693-019-00306-8
   Web of Science ®Google Scholar
• Fuglie, K. O. (2012). 16 Productivity growth and technology capital in the global agricultural economy. In Productivity growth in agriculture: An international perspective (p. 335). CABI.
   Google Scholar
• Ghana Statistical Service. (2016). 2015 Labour force report, Ghana Statistical Service. https://www2.statsghana.gov.gh/docfiles/publications/Labour_Force/LFS%20REPORT_fianl_21-3-17.pdf
   Google Scholar
• Ghana Statistical Service. (2018). Ghana Census of Agriculture (GCA). https://statsghana.gov.gh/gsspublications.php?category=OTc2NDgyNTUzLjkzMDU=/webstats/p9r0796n5o
   Google Scholar
• Ghana Statistical Service. (2015). Ghana poverty mapping report. https://www2.statsghana.gov.gh/docfiles/publications/POVERTY%20MAP%20FOR%20GHANA-05102015.pdf
   Google Scholar
• Ghazalian, P. L., & Fakih, A. (2017). R&D and innovation in food processing firms in transition countries. Journal of Agricultural Economics, 68(2), 427–450. https://doi.org/10.1111/1477-9552.12186
   Web of Science ®Google Scholar
• Greene, W. H. (2012). Econometric analysis, 71e. Stern School of Business, New York University.
   Google Scholar
• Gupta, A., Ponticelli, J., & Tesei, A. (2020). Information, technology adoption and productivity: The role of mobile phones in agriculture (No. w27192). National Bureau of Economic Research.
   Google Scholar
• Hammerling, J. H. F. (2022). Technological change in five industries: Threats to jobs, wages, and working conditions. UC Berkeley Labor Center. https://laborcenter.berkeley.edu/ technological-change-in-five-industries/
   Google Scholar
• Harvey, D., Hubbard, C., Gorton, M., & Tocco, B. (2017). How competitive is the EU’s agri-food sector? an introduction to a special feature on EU agri-food competitiveness. Journal of Agricultural Economics, 68(1), 199–205. https://doi.org/10.1111/1477-9552.12215
   Web of Science ®Google Scholar
• Herrmann, R. T. (2017). Large-scale agricultural investments and smallholder welfare: A comparison of wage labor and outgrower channels in Tanzania. World Development, 90, 294–310. https://doi.org/10.1016/j.worlddev.2016.10.007
   Web of Science ®Google Scholar
• Hirpa Tufa, A., Alene, A. D., Cole, S. M., Manda, J., Feleke, S., Abdoulaye, T., Chikoye, D., & Manyong, V. (2022). Gender differences in technology adoption and agricultural productivity: Evidence from Malawi. World Development, 159, 106027. https://doi.org/10.1016/j.worlddev.2022.106027
   Web of Science ®Google Scholar
• International Labour Organization (ILO); International Union of Food, Agricultural, Hotel, Restaurant, Catering, Tobacco and Allied Workers’ Associations. (2007). Agricultural workers and their contribution to sustainable agriculture and rural development, written by Peter Hurst. Collaboration with Paola Termine and Marilee Karl.
   Google Scholar
• Isgin, T., Bilgic, A., Forster, D. L., & Batte, M. T. (2008). Using count data models to determine the factors affecting farmers’ quantity decisions of precision farming technology adoption. Computers and Electronics in Agriculture, 62(2), 231–242. https://doi.org/10.1016/j.compag.2008.01.004
   Web of Science ®Google Scholar
• Ivus, O., & Boland, M. (2015). The employment and wage impact of broadband deployment in Canada. Canadian Journal of Economics/Revue Canadienne D’économique, 48(5), 1803–1830. https://doi.org/10.1111/caje.12180
   Web of Science ®Google Scholar
• Jiménez, A., Palmero-Cámara, C., González-Santos, M. J., González-Bernal, J., & Jiménez-Eguizábal, J. A. (2015). The impact of educational levels on formal and informal entrepreneurship. BRQ Business Research Quarterly, 18(3), 204–212. https://doi.org/10.1016/j.brq.2015.02.002
   Web of Science ®Google Scholar
• Karafillis, C., & Papanagiotou, E. (2011). Innovation and total factor productivity in organic farming. Applied Economics, 43(23), 3075–3087. https://doi.org/10.1080/00036840903427240
   Web of Science ®Google Scholar
• Kashiwagi, K., & Kamiyama, H. (2023). Effect of adoption of organic farming on technical efficiency of olive-growing farms: empirical evidence from West Bank of Palestine. Agricultural and Food Economics, 11(1), 26. https://doi.org/10.1186/s40100-023-00266-7
   Google Scholar
• Kleemann, L., Abdulai, A., & Buss, M. (2014). Certification and access to export markets: Adoption and return on investment of organic-certified pineapple farming in Ghana. World Development, 64, 79–92. https://doi.org/10.1016/j.worlddev.2014.05.005
   Web of Science ®Google Scholar
• Klutse, N. A. B., Owusu, K., & Boafo, Y. A. (2020). Projected temperature increases over northern Ghana. SN Applied Sciences, 2(8), 1–14. https://doi.org/10.1007/s42452-020-3095-3
   Web of Science ®Google Scholar
• Kofarmata, Y. I., & Danlami, A. H. (2019). Determinants of credit rationing among rural farmers in developing areas: Empirical evidence based on micro level data. Agricultural Finance Review, 79(2), 158–173. https://doi.org/10.1108/AFR-03-2018-0023
   Web of Science ®Google Scholar
• Kudi, T. M., Bolaji, M., Akinola, M. O., & Nasa, I. D. H. (2011). Analysis of adoption of improved maize varieties among farmers in Kwara State, Nigeria. International Journal of Peace and Development Studies, 1(3), 8–12.
   Google Scholar
• Kumar, A., Gupta, K., Dixit, S., Mishra, K., & Srivastava, S. (2019). A review on positive and negative impacts of nanotechnology in agriculture. International Journal of Environmental Science and Technology, 16(4), 2175–2184. https://doi.org/10.1007/s13762-018-2119-7
   Web of Science ®Google Scholar
• Läpple, D., & Thorne, F. (2019). The role of innovation in farm economic sustainability: Generalised propensity score evidence from Irish dairy farms. Journal of Agricultural Economics, 70(1), 178–197. https://doi.org/10.1111/1477-9552.12282
   Web of Science ®Google Scholar
• Lai-Solarin, W. I., Adeoye, W. A., & Sennuga, S. O. (2021). Technology adoption capabilities of small farm dairy cattle holders in Gwagwalada, Abuja: Effects of asymmetric information and extension approaches. International Journal of Agricultural Economics, 6(6), 320–328.
   Google Scholar
• Lawal, B. O., Saka, J. O., Oyegbami, A., & Akintayo, I. O. (2004). Adoption and performance assessment of improved maize varieties among smallholder farmers in Southwest Nigeria. Journal of Agricultural & Food Information, 6(1), 35–47. https://doi.org/10.1300/J108v06n01_05
   Google Scholar
• Liu, Y., Shi, R., Peng, Y., Wang, W., & Fu, X. (2022). Impacts of technology training provided by agricultural cooperatives on farmers’ adoption of biopesticides in China. Agriculture, 12(3), 316. https://doi.org/10.3390/agriculture12030316
   Google Scholar
• Lokshin, M., & Sajaia, Z. (2004). Maximum likelihood estimation of endogenous switching regression models. The Stata Journal: Promoting Communications on Statistics and Stata, 4(3), 282–289. https://doi.org/10.1177/1536867X0400400306
   Google Scholar
• Luo, X., Zhu, S., & Song, Z. (2023). Quantifying the income-increasing effect of digital agriculture: Take the new agricultural tools of smartphone as an example. International Journal of Environmental Research and Public Health, 20(4), 3127. https://doi.org/10.3390/ijerph20043127
   PubMedGoogle Scholar
• Mamaqi, X., Miguel, J., & Olave, P. (2011). The relationship between employability and training. International Journal of Economics and Management Engineering, 5(8), 1022–1026.
   Google Scholar
• Materia, V. C., Pascucci, S., & Dries, L. (2017). Are In-house and outsourcing innovation strategies correlated? Evidence from the European agri-food sector. Journal of Agricultural Economics, 68(1), 249–268. https://doi.org/10.1111/1477-9552.12206
   Web of Science ®Google Scholar
• McMichael, P. (2013). Value-chain agriculture and debt relations: Contradictory outcomes. Third World Quarterly, 34(4), 671–690. https://doi.org/10.1080/01436597.2013.786290
   Web of Science ®Google Scholar
• Mhango, S. S. (2023). Comparative profitability among adopters and non-adopters of selected innovations. International Journal of Social Science Research and Review, 6(2), 586–594. https://doi.org/10.47814/ijssrr.v6i2.1071
   Google Scholar
• Miine, L. K., Akorsu, A. D., Boampong, O., & Bukari, S. (2023). Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana. Heliyon, 9(12), e23023. https://doi.org/10.1016/j.heliyon.2023.e23023
   PubMed Web of Science ®Google Scholar
• Ngoma, H. (2018). Does minimum tillage improve the livelihood outcomes of smallscale farmers in Zambia? Food Security, 10(2), 381–396. https://doi.org/10.1007/s12571-018-0777-4
   Web of Science ®Google Scholar
• Okali, C., & Sumberg, J. (2012). Quick money and power: Young people, tomato production and livelihood building in Brong Ahafo, Ghana. IDS Bulletin, 43(6), 44–57. Brighton: Institute of Development Studies (IDS). https://doi.org/10.1111/j.1759-5436.2012.00378.x
   Web of Science ®Google Scholar
• Ostermeier, M. (2020). Key labour market issues and decent work in developing and emerging countries [Doctoral dissertation]. Georg-August-Universität Göttingen.
   Google Scholar
• Pant, L. P., & Odame, H. H. (2017). Broadband for a sustainable digital future of rural communities: A reflexive interactive assessment. Journal of Rural Studies, 54, 435–450. https://doi.org/10.1016/j.jrurstud.2016.09.003
   Web of Science ®Google Scholar
• Pardey, P. G., Alston, J. M., & Ruttan, V. W. (2010). The economics of innovation and technical change in agriculture. In B. H. Hall & N. Rosenberg (Eds.), Handbook of the economics of innovation (Vol. 2, pp. 939–984). North-Holland.
   Google Scholar
• Prause, L., Hackfort, S., & Lindgren, M. (2021). Digitalization and the third food regime. Agriculture and Human Values, 38(3), 641–655. https://doi.org/10.1007/s10460-020-10161-2
   PubMed Web of Science ®Google Scholar
• Quartey, C. L. N. K., Osei Mensah, J., Nimoh, F., Adams, F., & Etuah, S. (2023). Choice of certification schemes by Smallscale pineapple farmers in Ghana: analysis of constraints and determinants of adoption. Journal of Agribusiness in Developing and Emerging Economies, 13(1), 141–155. https://doi.org/10.1108/JADEE-02-2021-0046
   Web of Science ®Google Scholar
• Rahayu, R., & Day, J. (2015). Determinant factors of e-commerce adoption by SMEs in developing country: Evidence from Indonesia. Procedia - Social and Behavioral Sciences, 195, 142–150. https://doi.org/10.1016/j.sbspro.2015.06.423
   Google Scholar
• Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., Reed, M., & Fraser, E. D. G. (2019). The politics of digital agricultural technologies: a preliminary review. Sociologia Ruralis, 59(2), 203–229. https://doi.org/10.1111/soru.12233
   Web of Science ®Google Scholar
• Senou, M. M., & Manda, J. (2022). Access to finance and rural youth entrepreneurship in Benin: Is there a gender gap? African Development Review, 34(1), 29–41. https://doi.org/10.1111/1467-8268.12623
   Web of Science ®Google Scholar
• Shang, S., Yin, Y., Guo, P., & Sun, Q. (2010). Current situation and development trend of mechanization of field experiments. Transactions of the Chinese Society of Agricultural Engineering, 26(1), 5–8.
   Google Scholar
• Shang, L., Heckelei, T., Gerullis, M. K., Börner, J., & Rasch, S. (2021). Adoption and diffusion of digital farming technologies-integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074. https://doi.org/10.1016/j.agsy.2021.103074
   Web of Science ®Google Scholar
• Sulo, T. K., & Chumo, C. (2012). Socioeconomic factors affecting the adoption of improved agricultural technologies among women in marakwet county Kenya. Journal of Emerging Trends in Economics and Management Sciences, 3(4), 312–317.
   Google Scholar
• Sumberg, J., & Okali, C. (2013). Young people, agriculture, and transformation in rural Africa: An "opportunity space" approach. Innovations: Technology, Governance, Globalization, 8(1–2), 259–269. https://doi.org/10.1162/INOV_a_00178
   Google Scholar
• Sundar, K. S. G., Koju, R., Maharjan, B., Marcot, B., Kittur, S., & Gosai, K. R. (2019). First assessment of factors breeding success of two stork species in lowland Nepal using Bayesian Network models. Wildfowl, 69, 45–69.
   Web of Science ®Google Scholar
• Taylor, J. E., Charlton, D., & Yúnez-Naude, A. (2012). The end of farm labor abundance. Applied Economic Perspectives and Policy, 34(4), 587–598. https://doi.org/10.1093/aepp/pps036
   Web of Science ®Google Scholar
• Tenaye, A. (2020). Technical efficiency of smallholder agriculture in developing countries: The case of Ethiopia. Economies, 8(2), 34. https://doi.org/10.3390/economies8020034
   Web of Science ®Google Scholar
• Tsan, M., Totapally, S., Hailu, M., & Addom, B. K. (2019). The digitalisation of African agriculture report 2018–2019. CTA.
   Google Scholar
• Ullah, M. S. (2020). ICTs, power prejudice and empowerment: Digital exclusion of the poor in rural Bangladesh. In Digital inequalities in the global south (pp. 103–133). Springer.
   Google Scholar
• Wang, K. H., Umar, M., Akram, R., & Caglar, E. (2021). Is technological innovation making world "Greener"? An evidence from changing growth story of China. Technological Forecasting and Social Change, 165, 120516. https://doi.org/10.1016/j.techfore.2020.120516
   Web of Science ®Google Scholar
• Weiss, R., Stumbo, P. J., & Divakaran, A. (2010). Automatic food documentation and volume computation using digital imaging and electronic transmission. Journal of the American Dietetic Association, 110(1), 42–44. https://doi.org/10.1016/j.jada.2009.10.011
   PubMedGoogle Scholar
• Wossen, T., Abdoulaye, T., Alene, A., Haile, M. G., Feleke, S., Olanrewaju, A., & Manyong, V. (2017). Impacts of extension access and cooperative membership on technology adoption and household welfare. Journal of Rural Studies, 54, 223–233. https://doi.org/10.1016/j.jrurstud.2017.06.022
   PubMed Web of Science ®Google Scholar
• Xu, Z., Li, J., & Ma, J. (2022). Impacts of extension contact on the adoption of formulated fertilizers and farm performance among large-scale farms in rural China. Land, 11(11), 1974. https://doi.org/10.3390/land11111974
   Web of Science ®Google Scholar
• Zhu, Y., Song, J., & Dong, F. (2011). Applications of wireless sensor network in the agriculture environment monitoring. Procedia Engineering, 16, 608–614. https://doi.org/10.1016/j.proeng.2011.08.1131
   Google Scholar