The impact of the supply of farmland, level of agricultural mechanisation, and supply of rural labour on grain yields in China

doi:10.7896/j.2081

Cited by 5 publications

(6 citation statements)

References 37 publications

(46 reference statements)

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“…These findings obtained from the research are supported by current studies showing that agricultural mechanization [34] and energy use in agriculture [58,59] positively affect grain production and grain yield in the long run. Similarly, the fertilizer used in grain production also positively affects the cultivated area.…”

Section: Discussionsupporting

confidence: 80%

“…However, research in the literature needs to examine the direction and coefficient of this relationship, including the researchers mentioned earlier. On the other hand, there are many studies on the effect of grain production areas on grain production [34,35,[38][39][40][41]. Chandio et al [38] found the effect of land under grain production (hectares) on grain yield (kg per hectare) to be negative.…”

Section: Discussionmentioning

confidence: 99%

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Grain Production in Turkey and Its Environmental Drivers Using ARDL in the Age of Climate Change

Gurbuz,

Kadioglu

2023

Sustainability

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This study aims to evaluate the long-run and causality relationships between the annual grain production (kg per hectare) in Turkey, fertilizer used in agriculture, the number of tractors, agricultural greenhouse gas emissions, and grain production area from 1988 to 2018. The study’s data for the years 1988–2018 were taken from the World Bank and Turkish Statistical Institute (Turkstat) databases. The autoregressive distributed lag bounds (ARDL) test was applied to estimate the cointegration between the variables. The cointegration test results confirmed a long-run relationship between the variables. The short-run estimation revealed that the error correction coefficient was negative and statistically significant. The result obtained for the error correction term estimated that the deviations from the short-run equilibrium would be corrected, and the system would converge to the long-run equilibrium within 1.05 years. Further, the long-run estimation showed that all variables included in the model had a statistically significant effect on the dependent variable. While this relationship was negative for grain production amount and carbon emission, it was positive for fertilizer use and the number of tractors. The grain areas estimated as the dependent variable in the ARDL model were in a feedback relationship with the current production and number of tractors variables, while the fertilizer and carbon emission variables were in a unidirectional causality relationship towards the grain production area. There is a negative relationship between grain production (kg per hectare) and grain production areas (hectares). A 1% increase in grain production leads to a decrease of approximately 0.30% in grain production areas. Agricultural greenhouse gas emissions, another variable that stands out with its negative impact in ARDL long-run estimation results, indicate that product groups produced as an alternative to grain have a higher emission-generating power. The other long-run estimation results reveal that the tractor variable positively affects grain production areas.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Grain Production in Turkey and Its Environmental Drivers Using ARDL in the Age of Climate Change

Gurbuz,

Kadioglu

2023

Sustainability

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“…Several socioeconomic and demographic factors can be the basis for improving productivity in both the economic and agricultural sector. However, how labor is allocated in the agricultural sector can affect productivity (Abate & Kuang, 2021; Benjamin, 1992). The productivity difference is the result of a reallocation of labor from a less productive sector to a more productive one (Wu et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Also, Abate and Kuang (2021) used the ARDL model with data from 1978 to 2017 to analyze the short‐term and long‐term impact of agricultural land supply, rural labor, and agricultural mechanization development on cereal crop yields in China. They found that the impact of labor on yields was insignificant but land supply and mechanization had a positive effect and increased cereal yields in the short term and long term.…”

Section: Literature Reviewmentioning

confidence: 99%

Labor differentiation and cotton productivity in Burkina Faso

Zong‐naba,

N'gbo,

Combary

2024

African Development Review

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Agriculture is a very important sector in Africa's economic development, particularly in Burkina Faso, as it employs a large proportion of the population. Given the importance of labor in this sector, a good allocation of the different types of labor could help increase agricultural productivity in Burkina Faso. This research contributes to the literature by determining the specific contributions of each type of labor in enhancing cotton productivity. The sample of this research is 477 cotton farms, and a semiparametric stochastic frontier model has been used in the analysis. The results show that the proportion of wage labor has a nonlinear effect and contributes to improving cotton productivity when the number of educated people in the household increases. But family labor decreases cotton productivity when the number of educated people in the household increase. The comparison between the findings of the semiparametric and parametric frontier shows that technical efficiency is 72.44% when education is used as the channel through which production factors affect cotton productivity. However, this technical efficiency is 54.96% when production factors directly affect cotton productivity in the parametric frontier model. Promoting education in rural areas will help to increase the number of people educated and consequently improve cotton productivity.

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“…These models can also incorporate complex approaches like neural networks, multiagent systems, and multinomial logistic regression for better simulation performance (Basse et al, 2014;Mustafa et al, 2018). Furthermore, numerous driving factors, such as urbanization, agricultural machinery advancement, and population economic growth, can also be incorporated into maize yields, making the model framework more realistic (Yu et al, 2020;Abate et al, 2021). However, previous studies did not consider the combined response of maize yields to climate change and future changes in maize acreage (Basso et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Estimation of maize yield incorporating the synergistic effect of climatic and land use change in Jilin, China

et al. 2023

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Yield forecasting can give early warning of food risks and provide solid support for food security planning. Climate change and land use change have direct influence on regional yield and planting area of maize, but few studies have examined their synergistic impact on maize production. In this study, we propose an analysis framework based on the integration of system dynamic (SD), future land use simulation (FLUS) and a statistical crop model to prefuture maize yield variation in response to climate change and land use change in a region of central Jilin province, China. The results show that the cultivated land is likely to reduce by 862.84 km 2 from 2030 to 2050. Nevertheless, the total maize yield is expected to increase under all four RCP scenarios due to the promotion of per hectare maize yield. the scenarios, RCP4.5 is the most beneficial to maize production, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, and taking adaptation and mitigation measures to coordinate climate change and production.

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The impact of the supply of farmland, level of agricultural mechanisation, and supply of rural labour on grain yields in China

Cited by 5 publications

References 37 publications

Grain Production in Turkey and Its Environmental Drivers Using ARDL in the Age of Climate Change

Grain Production in Turkey and Its Environmental Drivers Using ARDL in the Age of Climate Change

Labor differentiation and cotton productivity in Burkina Faso

Estimation of maize yield incorporating the synergistic effect of climatic and land use change in Jilin, China

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