Nerlovian area response as an error correction model: An application to Western Canadian agriculture

Clark, Jim; Klein, K. K.

doi:10.1007/bf01180698

Cited by 5 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with the previous studies, the first major contribution is that this study further extends the classical area response model by investigating nonlinear and dynamic interactions between rice area and climate variables. The literature on estimating supply response to price and climate factors have a long history in agricultural economics (Nerlove, 1956; Chavas et al , 1983; Clark and Klein, 1996; Weersink et al , 2010; Miao et al , 2016; Von and Weersink, 1993). However, these studies adopted regression model and assumed a linear relationship between expected price and area.…”

Section: Discussion and Policy Implicationmentioning

confidence: 99%

“…The literature on estimating supply response function have a long history in agricultural economics. Much of the early literature focused attention on the price factors and price expectations in the specification of area response (Nerlove, 1956; Chavas et al , 1983; Clark and Klein, 1996). The explanation from Miao et al (2016) is that an increase in the output price of the crop can be expected to increase the acreage devoted to the crop by creating incentives to switch land from other uses to this crop.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climate change and its impact on rice acreage in high-latitude regions of China: an estimation by machine learning

Tian

Yan

2022

IJCCSM

View full text Add to dashboard Cite

Purpose Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China. Design/methodology/approach This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation. Findings The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation. Originality/value To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.

show abstract

Section: Discussion and Policy Implicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate change and its impact on rice acreage in high-latitude regions of China: an estimation by machine learning

Tian

Yan

2022

IJCCSM

View full text Add to dashboard Cite

show abstract

“…The ECM has been used to explain the decline in the agriculture share of GDP in terms of factor endowments, prices, technological change, and agricultural policies (Martin and Warr 1993;McKay, Morrissey, and Vaillant 1999;Sun, Fulginiti, and Peterson 2007). Other applications of ECM analyses in the agricultural sector include the estimation of machinery demand (Vasavada and Cook 1996), fertilizer demand (Mergos and Stoforos 1997), price volatility and interdependencies (Rezitis and Stavropoulos 2011;Algieri 2014;Abdelradi and Serra 2015;Serra 2015;Würriehausen, Ihle, and Lakner 2015), crop supply (Abdulai and Rieder 1995), and crops' area response to output prices (Clark and Klein 1996). Additionally, the ECM framework has been used to investigate agricultural commodity price linkages between developed and developing countries (Yavapolkul, Gopinath, and Gulati 2006) and to reexamine causality relationships such as the farmland price-rent relationship (Shigeto, Hubbard, and Dawson 2008) and the agricultural sector-economic growth causality direction (Awokuse and Xie 2015).…”

mentioning

confidence: 99%

Lessons from long-run (1975–2017) structural change in Colombia's coffee production

Saenz

Álvarez

Brock

2021

Agric. Resour. Econom. Rev.

View full text Add to dashboard Cite

Although coffee is still an important agricultural commodity in Colombia, the coffee share of GDP has significantly declined over the past 40 years. Controlling for changes in relative prices, factor endowments, and technological change, we analyze the coffee share decline in conjunction with other agricultural output by applying a Vector Error Correction model. The results indicate that while market liberalization policies contributed to the declining coffee share of GDP, the decline was partly offset by the end of coffee export quotas. Our results support policies in favor of assistance for farmers to compete in international markets.

show abstract

Policy implications of trends in Turkey's meat sector with respect to 2023 vision

et al. 2013

View full text Add to dashboard Cite

Nerlovian area response as an error correction model: An application to Western Canadian agriculture

Cited by 5 publications

References 20 publications

Climate change and its impact on rice acreage in high-latitude regions of China: an estimation by machine learning

Climate change and its impact on rice acreage in high-latitude regions of China: an estimation by machine learning

Lessons from long-run (1975–2017) structural change in Colombia's coffee production

Policy implications of trends in Turkey's meat sector with respect to 2023 vision

Contact Info

Product

Resources

About