Efficiency and total factor productivity of crop production at NUTS1 level in Turkey: Malmquist index approach

Armağan, Göksel; Özden, Altuğ; Bekçioğlu, Selim

doi:10.1007/s11135-008-9216-5

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…In the selection of evaluation indicators, some early scholars took traditional production factors such as land, intermediate factors and labor as input factors (Lin, 1992;Wen, 1993), and most scholars took production factors such as land, water resources, labor, pesticides and chemical fertilizers as input factors (Benedetti et al, 2019;Zhang et al, 2021). In the selection of output factor indicators, scholars generally choose the total grain output as the output index (Yao and Xuan, 2019), while some use the total agricultural output value per unit of cultivated land (Armagan et al, 2010;Kumbhakar et al, 2014) or agricultural added value (Gebregziabher et al, 2012).…”

Section: Calculation Of Grain Production Efficiencymentioning

confidence: 99%

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Gao

2021

Front. Environ. Sci.

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Environmental factors have a profound impact on the evaluation of grain production efficiency. Taking environmental factors into account can more accurately measure grain production efficiency and identify the development stage of grain production. Based on the global super-efficiency SBM model, environmental factors are included in the grain production efficiency measurement system, while the temporal and spatial evolution characteristics of grain production environmental efficiency and the sources of inefficiency are analyzed. In addition, the Spatial Durbin Model is used to investigate the influencing factors and spatial spillover effects of China’s grain production environmental efficiency. The results show that: environmental factors have a significant impact on the efficiency of grain production in China; the environmental efficiency of grain production in China fluctuates with a downward trend; the environmental efficiency of grain production in different provinces show obvious spatial differentiation and geographical agglomeration; the main influencing factors of the environmental efficiency loss of grain production in major grain production areas are carbon emissions, non-point source pollution of grain production and labor input; the level of education, the level of technological development, and labor transfer have a significant positive spillover effect on the environmental efficiency of China’s grain production, yet disaster rate has a significantly negative one. Thus, it is important to pay attention to resource conservation and pollution control, strengthen cooperation and exchanges between provinces, and take the path of coordinated development between the environment and grain production.

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Section: Calculation Of Grain Production Efficiencymentioning

confidence: 99%

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Gao

2021

Front. Environ. Sci.

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“…Yapılan hesaplamalar sonucunda bölgelere ait skor ortalamaları her ne kadar birbirine yakın olarak gözükse de bir sıralama yapıldığında ilk dört sırayı Ege, Batı Marmara, Akdeniz ve Doğu Marmara Bölgelerinin izlediği, son dört sırayı ise, Batı Karadeniz, Orta Doğu Anadolu, Güney Doğu Anadolu ve Kuzey Doğu Anadolu bölgelerinin aldığı görülmektedir (Çizelge 5). Bu hesaplamalarda yer alan çalışmalar aynı girdi ve çıktılara göre yapılmamış olsa da sıralama açısından incelendiğinde, sonuçların Armağan et al, (2010) ve Özden ve Armağan (2012) tarafından yapılan çalışmalarla uyumlu olduğu görülmektedir. Bitkisel üretimde teknik etkinlik hesaplamalarının yapıldığı çalışmalardan elde edilen etkinlik ortalamalarının, bölgelere göre tarımsal etkinlik ya da bitkisel üretim etkinliklerinin hesaplandığı çalışmalarla uyumlu olması, bu tarz çalışmaların sonuçlarının geneli yansıtması ve kullanılabilirliği açısından önemlidir.…”

Section: Genel Değerlendi̇rme Ve çIkarimlarunclassified

Türk Tarımında Bitkisel Üretim Etkinliği: 2000 Yılı Sonrası Araştırmaların Genel Değerlendirmesi

Özden

2017

Tarım Ekonomisi Dergisi

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Tarım sektöründe etkinlik çalışmaları dünyada özellikle son 50 yılda sayıları gittikçe artarak literatürdeki yerlerini almışlardır. Türkiye'de de 2000'li yıllarla birlikte sıkça görülmeye başlayan bu çalışmalar, tarımın sürdürülebilirliği ve gelecek kuşaklara dokunulabilir, koklanabilir ve tadılabilir gıdalar bırakılabilmesi açısından önem arz etmektedir. Türkiye'de bitkisel üretim alanında gerçekleştirilen etkinlik araştırmalarının ele alındığı bu çalışmada, yayımlanan tüm araştırmalar kullanılan yöntemlere, modellere, girdi-çıktılara ve gerçekleştirildiği NUTS1 bölgelerine göre sınıflandırılmıştır. Toplamda 38 adet araştırma mercek altına alınmıştır. Veri Zarflama Analizi uygulanan çalışmalar için ÖGSG skorları ortalaması, ÖGDG ortalaması ve ölçek etkinliği skorları ortalaması sırası ile 0.681, 0.817 ve 0.839, Stokastik Sınır Analizi uygulanan çalışmalar için ise teknik etkinlik skorları ortalaması 0.860 olarak hesaplanmıştır. NUTS1 bölgeleri içinde en yüksek etkinliğe sahip bölge TR3 olarak belirlenmiştir. Bitkisel üretimde üretim alanı genişliği, sermaye ve işgücü girdilerinin iyileştirilmesi gerektiği ve bu iyileştirmeler için anahtar kelimenin uzmanlaşma olduğu öngörülmüştür. Abstract Efficiency studies in the agricultural sector have taken place in the literature especially in the last five decades. These studies, which have started to be seen frequently in Turkey with the beginning of 2000, are important in terms of sustainability of agriculture and leaving touchable, smellable and tasteable foods to future generations. In this study, which investigated the efficiency studies carried out in the field of plant production in Turkey, all the published researches were classified according to the methods used, models, inputs-outputs and NUTS1 regions where they were implemented. A total of 38 researches have been focused. At the end of the general evaluation, for the studies which Data Envelopment Analysis was applied, the means of CRS scores, VRS scores and scale efficiency scores were calculated as 0.681, 0.817 and 0.839 respectively. The mean of technical efficiency scores for the studies which stochastic frontier analysis was applied calculated as 0.860. The region with the highest efficiency score within the NUTS1 regions was designated as TR3. It has been determined that in plant production, the inputs like the size of the production area, capital and labor should be improved, and that the key to this improvement is specialization.

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“…Guyomard [17] measured the agricultural production efficiency of French farms by the DEA method; the results showed that technological progress played a key role in improving agricultural production efficiency. Armagan [18] analyzed the TFP of agricultural production in Turkey by using the DEA-Malmquist productivity index and found that the backward technical level was one of the main factors that affected the decline of the growth rate of agricultural TFP. Wouterse [19] found that a significant relationship existed between labor transfer and the technical efficiency of grain production.…”

Section: Introductionmentioning

confidence: 99%

The Evolution and Influencing Factors of Total Factor Productivity of Grain Production Environment: Evidence from Poyang Lake Basin, China

Bao

Jiang

Jin

et al. 2021

Land

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The total factor productivity (hereafter TFP) of grain production is important to achieve balanced development, while environmental factors are an important part of TEP. In order to explore the characteristics and patterns of the temporal and spatial evolution of the environmental total factor productivity (hereafter ETFP), the Malmquist-Luerberger index, and the spatial autoregressive panel (SAR panel) model were adopted to analyze the evolutionary rules and the influencing factors of ETFP. In this study, we took Poyang Lake, one of China’s main grain production areas, as a study area, and carried out empirical research based on grain production statistical data. The results show that: (1) ETFP shows a growth trend with the increase of grain production from 2001 to 2017, and a great potential for improvement exists. Moreover, from the perspective of time sequence evolution and decomposition of ETFP, which belongs to the dual-track driver of environmental technical efficiency and environmental technological progress, relevant technologies play an important role in promoting the improvement of TEFP; (2) Given that the objective conditions of gain production remain unchanged, the fact that the urbanization rate and average annual rainfall have a negative effect on ETFP, the explanatory variables such as the business scale per worker, the proportion of grain growing population, industrial agglomeration, the proportion of grain sown area and the average annual temperature all play a positive role. Among the variables, the business scale per worker and the proportion of grain growing population significantly affect ETFP at the 1% level. The average annual rainfall, industrial agglomeration and the proportion of grain sown area significantly affect the ETFP at the 5% level. The average annual temperature significantly affects the ETFP at the 10% level.

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Efficiency and total factor productivity of crop production at NUTS1 level in Turkey: Malmquist index approach

Abstract: Agriculture, TFP Change, DEA, Turkey,

Cited by 21 publications

References 7 publications

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Türk Tarımında Bitkisel Üretim Etkinliği: 2000 Yılı Sonrası Araştırmaların Genel Değerlendirmesi

The Evolution and Influencing Factors of Total Factor Productivity of Grain Production Environment: Evidence from Poyang Lake Basin, China

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