The public policy of agricultural land allotment to agrarians and its impact on crop productivity in Punjab province of Pakistan. (January 2020)

• Record Type: Journal Article
• Authors: Elahi, Ehsan; Khalid, Zainab; Weijun, Cui; Zhang, Huiming
• Is Part Of: Land use policy. Volume 90(2020)
• Publication Date: 2020-01
• Subjects: Land use policy -- Crop productivity -- Artificial neural network -- Propensity score matching method -- Cobb-Douglas production function -- Punjab province -- Pakistan; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2019.104324
• Languages: English
• ISSNs: 0264-8377

Abstract

Highlights: The public policy of land allotment to agrarians has increased crop yield. Non-agrarians have applied farm inputs more than target quantities. Agrarians have used farm resources at targeted quantities. Non-agricultural education is a reason for inefficient use of farm resources. The public policy of land allotment is positively correlated to increase crop yield. Abstract: The study estimates the impact of land allotment policy to agrarians on crop productivity in Punjab province of Pakistan. The term "agrarian" refers to a respondent who has formal education in agricultural sciences (agronomy, soil science, agricultrual economics, and horticulture etc.), otherwise known as "non-agrarian". In 2009 the Punjab government allotted agricultural plots to agrarians. A multistage sampling technique was used to collect data of 384 crop growers by using well-structured questionnaire from two main cop zones of Punjab province of Pakistan. From July to September 2017, the data of rice and sugarcane were collected from mixed crop zone, and cotton and wheat from cotton-wheat crop zone. Various econometric methods were used to approach study objectives. Results found that compared to non-agrarians, crop productivity of wheat, rice, cotton, and sugarcane was 28.9, 23.4, 19.8, and 8.3%, higher at agrarians' farms, respectively. Agricultural education and recommended use of farm inputs were main factors of getting higher crop yield at agrarians' farms. Artificial neural network (ANN) reported that at agrarian plots, farm inputs, particularly Pure N, Pure P, Pure K, and seed rate were used at recommended level for wheat, cotton, rice, and sugarcane crop. The overuse of Pure N and seed rate and underuse of Pure P, Pure K and farmyard manure (FYM) were found the main reasons for getting lower crop yield at non-agrarians' farms. Moreover, Cobb-Douglas (CD) production function evident that, although the farming experience of non-agrarians significantly increased the crop productivity, excessive use of Pure N and seed rate and non-agricultural education have no significant impact on crop yield. On the other hand, propensity score matching (PSM) method confirmed that public policy of land allotment to agrarians has significanlty increased the yield of wheat, rice, cotton, and sugarcane by 16.5, 14.2, 12.3, and 23.2%, respectively. The study results suggested that besides of land allotment to agrarians, the government should also focus on the provision of formal and informal agricultural education and training to non-agrarians. More specifically, it is necessary to provide knowledge of basic crop production technology to non-agrarians. Furthermore, to exploit maxim crop yield it is highly suggested to apply fertilizers at the recommend level estimated by the ANN method.