Systematic Review and Meta- regression Analysis of Technical Efficiency of Agricultural Production Systems

Abstract

The existing state of over-utilization of input resources affected the efficient production of the agricultural output, which created a challenge for the profitability of the farming community as well as sustainability of different agricultural production systems (APSs). Hence, it is crucial to explore the important input variables, which affect farming efficiency across different APSs. In past studies, data envelopment analysis (DEA) has been used extensively to estimate the mean technical efficiency (MTE) of agricultural farms. In this study, a meta-regression analysis has been performed to examine variables that affect the MTE variation in 100 studies. The selected studies have been classified based on the study period, farm location, journals, product type, sample size and their outcomes. Results revealed that the year of study, location and sample size were not significant, whereas agricultural products such as vegetables, fruits, flowers and livestock significantly affected the performance of MTE across studies. These empirical results establish the importance of related variables in the MTE estimation of different APSs, which will lead and assist better-quality future research in the agricultural efficiency domain.

Keywords

Meta-analysis data envelopment analysis (DEA)technical efficiency agricultural products

Introduction

Urbanization has widely spread in most developing countries. However, poverty is still rampant in rural areas. Poverty minimization strategies are adopted with a great emphasis on rural development. In the rural sector, agriculture is the major income-generating practice to earn their livelihood (Kay, 2009). Although the smallholder agricultural farms play a vital role in generating a major share of the total food supply, they experience resource deterioration and higher dependency on outside feedstock and fertilizing ingredients because of the absence of proper knowledge and suitable technological advancement, which degrades efficient production and profit maximization (Cortez-Arriola et al., 2014). Past studies were helpful to indicate that one of the major reasons for inadequate production with low efficiency worldwide in the agricultural sector is the inability of producers to fully utilize the available resources (Malaga-Toboła, Tabor, & Kocira, 2015). Deshpande and Bhende (2003) revealed that more efficient production by farmers could diminish the gap between demand and food availability across the world.

For sustainable development of agriculture, the efficiency of production is vital which indicates the optimal use of input resources to yield the maximum output under the existing resource constraints (Shanmugam & Venkataramani, 2006). The agricultural sector utilizes a huge amount of resources in the forms of machinery, fossil fuels, electric power, manures, fertilizing chemicals, seeds, water for irrigation purpose and human labour. Efficient utilization of inputs is a vital step towards reducing depletion of natural means of resource availability and obtaining agricultural sustainability. Several methods were adopted to estimate efficiency and reduce resource usage in farming practices.¹

To find the farm-level efficiencies for assessing the performance of any farmer regarding the optimal allocation of resources, data envelopment analysis (DEA) is used. In numerous past studies, DEA has been applied to find production efficiency and necessary optimizations for low input costs and to reduce input consumption. Kuznet (1966) and Thiam, Bravo-Ureta, and Rivas (2001) indicated the significance of agriculture in the economic progress of developing countries. The influence of efficiency on different farming practices has yielded numerous studies on agriculture. Farrell (1957) worked on the frontier methods which were extensively adopted as a technique for efficiency measurement in agricultural production. Frontier analysis has been observed in numerous past studies, and the wide application of DEA makes it the preferred tool for production and efficiency estimation. Several systematic reviews of DEA’s application to estimate technical efficiency (TE) in the agricultural sector were brought out by Battese (1992) and by Bravo-Ureta and Pinheiro (1993). Such attempts made in past studies emphasize the importance of measuring a farmer’s productivity in less developed countries utilizing DEA as an available frontier model.

The DEA is found to be an efficient tool in frontier modelling which is a non-parametric technique, appropriate in performance estimation, than traditional econometric tools like regression analysis. It helps to determine how efficiently a farmer can produce a definite level of yield by utilizing levels of several inputs in comparison to other farmers. The DEA was applied to facilitate studies which were estimated by other approaches in the past (Mardani, Zavadskas, Streimikiene, Jusoh, & Khoshnoudi, 2017). Charnes, Cooper, and Rhodes (1978) explained DEA as a ‘mathematical programming model applied to observational data that provides a new way of obtaining empirical estimates of relations—such as the production functions and/or efficient production possibility surfaces—that are cornerstones of modern economies’.

Meta-analysis is one of the approaches that considers estimates of various indicators from numerous past studies, for example, the mean technical efficiency (MTE) in this investigation, and takes efforts to demonstrate the variation of estimates by analysing dissimilarities over the studies with the help of regression models (Lee, Choe, & Park, 2015). Meta-analysis has been utilized immensely in the educational sector, psychology and health sciences. Several approaches are considered to evaluate TE. Average TE, from the studies, is organized categorically. With the help of the analysis, different issues affecting the variation in TE are discussed. Consecutively, a compilation of data and statistical findings are supplied together with appropriate implications for future research.

The objective of the article is to gather information from vast literature and compilation of data for performing a meta-analysis by examining different variables with the help of logistic regression, accessibility of the data to scholars for efficient DEA modelling and efficient allocation of resources with low input costs and minimal input consumption. We summarized various studies in a quantitative manner to develop a clear understanding of the distribution of MTE in an agricultural production system (APS) by exploring the following questions:

How did MTE vary across different production systems in agriculture?

What is the impact of the production-specific characteristic on MTE?

How does MTE vary as per the various production systems that is livestock, horticulture, floriculture and so on?

Data Envelopment Analysis

The DEA generally involves the use of a linear programming methodology to build an efficient production frontier (Jain, Natarajan, & Ghosh, 2016). In this approach, inefficient decision-making units (DMUs) can be segregated from the efficient ones (Bhatia & Mahendru, 2016; Dutta, 2013). The DMUs which are measured to be efficient reside on the efficient frontier, and the inefficient ones remain below this efficient frontier (Tandon, Tandon, & Malhotra, 2014). Minimizing the input levels by keeping the same levels of output, that is input oriented, and varying the output levels while maintaining the same input levels, that is output oriented, are the two means of converting the inefficient DMU to the efficient levels. The input-oriented model is mostly used in agriculture considering the fact that farmers have little control over the output (Zhou, Ang, & Poh, 2007).

Technical Efficiency

TE can be explained by how a DMU, in this case a farmer, yields maximum output with definite input levels and available technology (Charnes et al., 1978). For multiple input variables and output factors, the TE value can be estimated by the ratio of the weighted sum of outputs to the weighted sum of inputs. Mathematically, it can be expressed as follows (Cooper, Seiford, & Tone, 2006):

T E = \frac{u_{1} y_{1 j} + u_{2} y_{2 j} + \dots + u_{n} y_{n j}}{v_{1} x_{1 j} + v_{2} x_{2 j} + \dots + v_{m} x_{m j}} = \frac{\sum_{r = 1}^{n} u_{r} y_{r j}}{\sum_{s = 1}^{m} v_{s} x_{s j}} ​

(1)

In this case, u_r indicates the output weight n, y_r indicates the output quantity n, v_s indicates the input weight n, x_s indicates the input quantity n, r indicates the number of outputs (r = 1, 2, …, n), s indicates the number of inputs (s = 1, 2, …, m) and j indicates the jth DMU (j = 1, 2, …, k).

\begin{array}{l} Maximize θ = \\ \begin{matrix} {\sum^{​}}_{n}^{r = 1} u_{r} y_{r j} ​ \\ {\sum^{​}}_{n}^{r = 1} u_{r} y_{r j} - {\sum^{​}}_{m}^{s = 1} v_{s} x_{s j} \leq 0 \end{matrix} \\ Subjected to \\ {\sum^{​}}_{m}^{s = 1} v_{s} x_{s j} = 1 ​ \\ u_{r} \geq 0; v ​_{s} \geq 0 a n d (i a n d j = 1, 2, 3 \dots, k) \end{array}

(2)

Pure Technical Efficiency (PTE)

In case of the TE measurement in DEA, constant returns to scale is preassumed considering the Charnes, Cooper and Rhodes (CCR) model which gives an idea of scale efficiency (SE) as well as TE. Unless all the DMUs are performing at an optimal scale, the constant returns to scale assumption are not appropriate, which makes it difficult to follow this assumption (Dutta & Sengupta, 2011). Later, Banker, Charnes, and Cooper (1984) followed a frontier method in DEA, known as the Banker, Charnes and Cooper (BCC) model, for estimating pure technical efficiency (PTE). The later DEA model predicts variable returns to scale (VRS), which depicts a variation in the level of inputs for disproportionate variation in the level of outputs.

\begin{array}{l} Maximize z = u y_{i} - u_{i} \\ Subjected to v x_{i} = 1 \\ - v X + u Y - u_{0} e \leq 0 ​ \\ v \geq 0, u \geq 0 a n d u_{0} f r e e i n s i g n \end{array}

(3)

where u and v indicate the respective weight of output and input matrices and Y and X indicate respective output and input matrices. Here, x_i and y_i indicate the input and output.

Scale Efficiency

SE, however, represents the highly efficient measurement of performance regarding the maximization of average productivity. In case of scale efficient producers, scores for TE and PTE remain the same. SE can be presented as follows (Cooper et al., 2006):

S E = \frac{T E_{C R S}}{P T E_{V R S}} ​

(4)

In Figure 1, the two approaches in DEA are indicated in the efficiency measurement. Based on CCR that is constant returns to scale, the efﬁciency frontier achieved is a straight line which intersects the origin point and the most efficient performer. However, by utilizing the BCC approach the number of efficient units is higher such as P1, P2, P3 and P4. PTE is nothing but TE without the effect of SE.

Figure 1.

Source: Hosseinzadeh-Bandbafha, Safarzadeh, Ahmadi, Nabavi-Pelesaraei, and Hosseinzadeh-Bandbafha (2017).

Figure 2.

Source: The authors.

Methods

This article has adopted a meta-analysis of related variables through a broad literature search and compilation of data to define the methodology. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) concept is applied in the article to illustrate the methodology (Mardani et al., 2017). A systematic approach is there in the review which helps to distinguish and promote a relevant research (Moher et al., 2009). Data which are included in the article are collected from different studies and analysed. This systematic review is associated with meta-analysis which denotes the utilization of statistical methods to support studies which are included in the article. A thorough literature survey has been done to select the appropriate papers from which the collected data are summarized. Depending upon the quality and necessity of the studies the PRISMA concept is applied in this research. We explored online databases to collect every possible article published on the application of DEA in agriculture between 1997 and 2017. The papers are further categorically classified into several divisions depending on the nature of the produce, country of which the paper belongs to, the year of publication and publication houses. In Figure 2, systematic reviews and the meta-analysis are described through a block diagram. The meta-analysis performed in this article can be divided into the following points (Thiam et al., 2001):

Different variables are selected to demonstrate the inconsistency in TEs.

An in-depth literature survey is needed to include past studies in the meta-analysis.

In the third step, coding of all the required variables is to be done in the analysis.

Logistic regression is to be performed as a means of statistical analysis.

Providing results of the meta-analysis and depicting scope for future research.

Classification of Articles Based on Data Envelopment Analysis in Agriculture

After a thorough literature review of the application of DEA in agriculture, we can fairly put papers into few categories. Papers are classified according to the efficiency in different farming practices (horticulture and livestock or animal rearing). In horticulture, we collected articles on vegetables, fruits and flowers. In case of animal farming, we screened papers with efficiencies in poultry production, dairy farming, hog production, fish cultivation and sheep farming. The articles are further distributed and analysed into the year of publication, the country where the research has been performed and the name of the journal (Mardani et al., 2017).

Vegetable Crop Farming

In developing countries, vegetables act a key role in hunger minimization and provide a stable option for poverty dilution with the help of employment and profit maximization. Although vegetable farming is beneficial, income generation faces several limitations. Our findings shown in Table 1 stipulate that 39 previous studies applied DEA in several vegetable crops in different years. Shrestha, Huang, Gautam, and Johnson (2016) examined the economic efficiency of vegetable farms of Nepal using a DEA approach. An input-oriented DEA approach was utilized to calculate the farm efficiency by collecting data from 502 randomly selected farm households. Their investigation showed that vegetable farms in Nepal could improve the vegetable production efficiency by obtaining improved seeds, training and extension services. Khoshnevisan, Rafiee, Omid, and Mousazadeh (2013) implemented DEA to investigate the productivity of wheat farms to discriminate efficient from inefficient farmers and to estimate the uneconomical utilization of resources. They identified that 18 per cent of farmers (47 farmers) were technically effective with high efficiency, while based on the BCC method 154 growers were found to be efficient (59%). Pahlavan, Omid, and Akram (2011) studied the consumption of inputs by different sources in tomato farming in Iran. They implemented DEA for examining the productivity by estimating TE of growers in terms of energy consumption for growing tomato. The investigation revealed that diesel fuel, electric power and fertilizing chemicals consumed more energy in that location. Chauhan, Mohapatra, and Pandey (2006) applied DEA to measure the efficiency in terms of energy consumption for paddy cultivation in the state of West Bengal, India. The investigations suggested that empirical evidence on farming and definite analysis must provide better reliability to future researchers. The authors further concluded that DEA was useful to measure the empirical findings which indicated several vital traits in agricultural production.

Fruit Crop Cultivation

Employment and income generation are crucial in the proper development of countries where the economy is based on agriculture. Fruit crops play an important role to address the issues related to poverty. In many countries, fruit crops such as grapes, citrus, strawberry, kiwifruit, banana, apple and pineapple are vital in developing the economy, as these fruit crops are economically viable. Particularly in developing countries, horticulture is a prime source of income. We have reviewed past studies in Table 1 to indicate the application of DEA in several fruit crops throughout years. Nabavi-Pelesaraei, Abdi, Rafiee, and Mobtaker (2014) studied and analysed the efficiency level of orange production, segregated efficient orange producers from the inefficient farmers and indicated uneconomical consumption of resources by applying data envelopment approach in orange farming at Guilan Province of Iran. Mousavi-Avval, Rafiee, and Mohammadi (2011) had implemented DEA as one of the most preferred non- parametric approaches for estimating the production efficiencies in apple farming at Tehran Province of Iran. They suggested that among all farmers, only 34 per cent were found to be efficient globally, whereas 18 per cent of them were locally efficient farmers. The former state was due to the unfavourable size of scale. According to their study, 48 per cent of the total farmers were inefficient. Khoshroo, Mulwa, Emrouznejad, and Arabi (2013) made the same attempt to improve the productivity in farming grapes. Their paper described a dual approach for estimating efficiency in terms of energy utilization in provinces in Iran. First, a non-parametric input-oriented attempt was made to estimate the TE value of farmers in grape production. Their findings indicated the mean score of TE, as well as efficiency in the BCC model and SE; values were found to be 0.72, 0.88 and 0.79, respectively.

Floriculture

Flowers are commercially cultivated to meet the global utilization on many occasions. Floriculture is both profitable and requires labour-intensive farming. Unlike any other farming, floriculture requires skilled labour; farmers are even willing to adopt modern techniques owing to the greater profit in the market. In Table 1, we have shown the reviewed past studies to understand the use of DEA in the cultivation of flowers which are commercially viable in the global market. Pahlavan, Omid, Rafiee, and Mousavi-Avval (2012) examined the efficiency of farmers for rose production in Iran by conducting DEA. Their investigations showed that overall energy used as input, the level of energy as output and the efficiency of energy consumption were 67.9 GJ per hectare, 11.8 GJ per hectare and 0.17, respectively. They further concluded that electric power and fertilizing chemicals held the major share of energy-consuming inputs. Mousavi Avval, Rafiee, Jafari, and Mohammadi (2011) investigated the sustainable agricultural system of sunflowers by efficient use of energy. Their research involved the application of DEA in 95 sampled farms for estimating the efficiency of growers at Golestan Province, Iran. Their findings in productivity of sunflower also helped in indicating the uneconomical consumption of energy inputs. Results also revealed that 36 per cent of growers were technically efficient. Average efficiency in producing sunflower was 0.87 for constant returns to scale, while it was 0.96 for VRS. Their research estimated 8,448.3 MJ per hectare as optimum energy needed, a possible minimization of 10.8 per cent in overall energy-consuming inputs was found possible by reducing the incapability of resource allocation of producers. By using proper farming techniques, application of several types of machinery and involvement of alternate sources of energy productivity of sunflower farmers could be increased, and along with that environmental impact must also be reduced.

Table 1.

Classification of Papers Based on Different Agricultural Practices

S. No.	Author	Country	Journal	Product	Sample Size	Mean TE
Based on Vegetable Crop Farming
1	Abay, Miran, and Gunden (2004)	Turkey	Journal of Sustainable Agriculture	Tobacco	300	0.456
2	Balcombe, Fraser, Latruffe, Rahman, and Smith (2008)	Bangladesh	Applied Economics	Rice	295	0.59
3	Chauhan et al. (2006)	India	Energy Conversion and Management	Paddy	97	0.772
4	De Koeijer, Wossink, Struik, and Renkema (2002)	Netherland	Journal of Environmental Management	Sugar beet	467	0.63
5	Dhungana, Nuthall, and Nartea (2004)	Nepal	The Australian Journal of Agricultural and Resource Economics	Rice	76	0.76
6	Diep (2013)	Vietnam	Agricultural and Food Economics	Rice	1,000	0.65
7	Ebrahimi and Salehi (2015)	Iran	Journal of Cleaner Production	Button mushroom	22	0.94
8	Elhami et al. (2016)	Iran	Information Processing in Agriculture	Chickpea	110	0.94
9	Farashah, Tabatabaeifar, Rajabipour, and Sefeedpari (2013)	Iran	Open Journal of Energy Efficiency	White button mushroom	26	0.955
10	Hoang Linh (2012)	Vietnam	International Journal of Development Issues	Rice	595	0.704
11	Houshyar, Azadi, Almassi, Sheikh Davoodi, and Witlox (2012)	Iran	Energy	Corn	89	0.55
12	Kamruzzaman, Manos, and Begum (2006)	Bangladesh	Journal of the Asia Pacific Economy	Wheat	150	0.86
13	Karimov (2013)	Uzbekistan	Agriculture	Potato and melon	178	0.64
14	Khan, Baten, Nawawi, and Murat (2016)	Malaysia	American Institute of Physics	Rice	70	0.61
15	Khoshnevisan et al. (2013)	Iran	Energy	Wheat	260	0.82
16	Kwon and Lee (2004)	Korea	The Australian Journal of Agricultural and Resource Economics	Rice	5,130	0.72
17	Mardani and Salarpour (2015)	Iran	Information Processing in Agriculture	Potato	23	0.9
18	Malana and Malano (2006)	Pakistan & India	Irrigation and Drainage	Wheat	25	0.85
19	Masuda (2016)	Japan	Journal of Cleaner Production	Wheat	36	0.554
20	Mobtaker, Akram, Keyhani, and Mohammadi (2012)	Iran	Energy for Sustainable Development	Alfalfa	80	0.84
21	Mohammadi et al. (2013)	Iran	Journal of Cleaner Production	Soybean	94	0.81
22	Mohammadi et al. (2015)	Iran	Journal of Cleaner Production	Paddy	82	0.8
23	Mousavi-Avval, Rafiee, Jafari, and Mohammadi (2011a)	Iran	Energy	Canola	130	0.88
24	Mousavi-Avval, Rafiee, Jafari, and Mohammadi (2011b)	Iran	Applied Energy	Soybean	94	0.853
25	Murthy, Sudha, Hegde, and Dakshinamoorthy (2009)	India	Agricultural Economics Research Review	Tomato	90	0.7768
26	Nabavi-Pelesaraei, Abdi, Rafiee, and Taromi (2014)	Iran	Engineering in Agriculture, Environment and Food	Rice	120	0.79
27	Nabavi-Pelesaraei, Rafiee, Mohtasebi, Hosseinzadeh-Bandbafha, and Chau (2017)	Iran	Journal of Cleaner Production	Paddy	240	0.953
28	Banaeian and Zangeneh (2011)	Iran	Energy	Corn	10	0.812
29	Picazo-Tadeo, Gómez-Limón, and Reig-Martínez (2011)	Spain	Journal of Environmental Management	Wheat	171	0.814
30	Poudel, Yamamoto, and Johnson (2012)	Nepal	International Association of Agricultural Economists	Organic and conventional coffee	240	0.89
31	Raheli, Rezaei, Jadidi, and Mobtaker (2017)	Iran	Information Processing in Agriculture	Tomato	150	0.92
32	Pahlavan et al. (2011)	Iran	Energy	Tomato	31	0.82
33	Pahlavan, Rafiee, and Omid (2012)	Iran	International Journal of Green Energy	Potato	44	0.74
34	Shafiq and Rehman (2000)	Pakistan	Agricultural Economics	Cotton	117	0.77
35	Shang and Mao (2009)	China	Springer Computer and Computing Technologies in Agriculture	Wheat	24	0.9
36	Tadesse and Krishnamoorthy (1997)	India	Agricultural Economics	Paddy	129	0.83
37	Tipi, Yildiz, Nargeleçekenler, and Çetin (2009)	Turkey	New Zealand Journal of Crop and Horticultural Science	Rice	70	0.92
38	Ullah and Perret (2014)	Pakistan	Environmental Management	Cotton	200	0.958
39	Umanath and Rajasekar (2013)	India	Indian Journal of Science and Technology	Vegetable crop	270	0.57
Based on Fruit Cultivation
1	Alemdar and Iúik (2008)	Turkey	Oeconomia	Peanut	75	0.92
2	Banaeian and Namdari (2011)	Iran	International Journal of Renewable Energy Research	Watermelon	85	0.67
3	Banaeian, Omid, and Ahmadi (2012)	Iran	Energy Efficiency	Strawberry	25	0.58
4	Banaeian and Omid (2011)	Iran	Agricultura Tropica Et Subtropica	Strawberry	25	0.73
5	Banaeian, Zangeneh, and Omid (2010)	Iran	Australian Journal of Crop Science	Walnut	37	0.72
6	Beltrán-Esteve and Reig-Martínez (2014)	Spain	Agricultural Systems	Citrus	100	0.71
7	Bolandnazar, Keyhani, and Omid (2014)	Iran	Journal of Cleaner Production	Cucumber	60	0.87
8	Clemente, Lírio, and Gomes (2015)	Brazil	Bio-based and Applied Economics	Citrus	67	0.79
9	Dong, Mitchell, and Colquhoun (2015)	USA	Journal of Environmental Management	Cranberry	95	0.83
10	Houshyar, Mahmoodi-Eshkaftaki, and Azadi (2017)	Iran	Journal of Cleaner Production	Pomegranate	55	0.773
11	Idris, Siwar, and Talib (2013)	Malaysia	American Journal of Applied Sciences	Pineapple	124	0.2927
12	Khoshnevisan et al. (2013)	Iran	Energy	Cucumber	26	0.99
13	Khoshnevisan et al. (2015)	Iran	Journal of Cleaner Production	Watermelon	88	0.80
14	Khoshnevisan, Shariati, Rafiee, and Mousazadeh (2014)	Iran	Renewable and Sustainable Energy Reviews	Strawberry	33	0.85
15	Khoshroo et al. (2013)	Iran	Energy	Grape	41	0.723
16	Kilic, Ceyhan, and Alkan (2009)	Turkey	New Zealand Journal of Crop and Horticultural Science	Hazelnut	151	0.85
17	Heidari, Omid, and Mohammadi (2012)	Iran	Expert Systems with Applications	Cucumber	46	0.8235
18	Mohammadi, Rafiee, Mohtasebi, Mousavi Avval, and Rafiee (2011)	Iran	Renewable Energy	Kiwi	86	0.942
19	Mousavi-Avval, Mohammadi, Rafiee, and Tabatabaeefar (2012)	Iran	Journal of Cleaner Production	Barberry	144	0.66
20	Mousavi-Avval et al. (2011)	Iran	Energy	Apple	56	0.785
21	Nabavi-Pelesaraei et al. (2014)	Iran	Journal of Cleaner Production	Orange	60	0.9
22	Omid, Ghojabeige, Delshad, and Ahmadi (2011)	Iran	Energy Conversion and Management	Cucumber	18	0.879
23	Pahlavan, Omid, and Akram (2012)	Iran	Journal of Agricultural Science and Technology	Cucumber	26	0.83
24	Rebolledo-Leiva, Angulo-Meza, Iriarte, and González-Araya (2017)	Chile	Science of the Total Environment	Blueberry	14	0.87
25	Reig-Martínez and Picazo-Tadeo (2004)	Spain	Agricultural Systems	Citrus	33	0.71
26	Vázquez-Rowe, Villanueva-Rey, Iribarren, Teresa Moreira, and Feijoo (2012)	Spain	Journal of Cleaner Production	Grape	40	0.86
27	Wang, Huo, and Kabir (2013)	China	China Agricultural Economic Review	Apple	255	0.664
Based on Floriculture
1	Aman (2011)	Ethiopia	Journal of Economics and Sustainable Development	Rose cut flower	28	0.61
2	Farani and Bahrami (2013)	Iran	Journal of Life Science and Biomedicine	Bell pepper	30	0.69
3	Mousavi Avval et al. (2011)	Iran	Wiley Library	Sunflower	95	0.96
4	Pahlavan, Omid, et al. (2012)	Iran	Energy for Sustainable Development	Rose	29	0.68
5	Shabani, Rafiee, Mobli, and Khanalipur (2012)	Iran	Energy System	Carnation	20	0.69
6	Usman, Ali, Hassan, and Abid (2015)	Pakistan	Agricultural Sciences	Gladiolus Cut-flower	80	0.72
7	Usman et al. (2015)	Pakistan	The Journal of Animal & Plant Sciences	Cut-flower	100	0.72
Based on Livestock Farming
1	Alam and Murshed-e-Jahan (2008)	Bangladesh	Aquaculture Economics & Management	Prawn-carp	105	0.85
2	Alam (2011)	Bangladesh	Aquaculture Research	Pangas	60	0.86
3	Amid, Mesri Gundoshmian, Shahgoli, and Rafiee (2016)	Iran	Information Processing in Agriculture	Broiler	70	0.88
4	Begum, Alam, Buysse, Frija, and van Huylenbroeck (2012)	Bangladesh	Applied Economics	Poultry	75	0.86
5	Ben-Belhassen and Womack (2000)	United States	American Agricultural Economics Association	Hog (Pig)	28	0.824
6	Bozogˇ lu and Ceyhan (2009)	Turkey	Energy	Trout and sea bass	14	0.999
7	Candemir and Koyubenbe (2006)	Turkey	Journal of Applied Animal Research	Dairy farm	80	0.954
8	Cinemre, Ceyhan, Bozogˇ lu, Demiryürek, and Kiliç (2006)	Turkey	Aquaculture	Trout	73	0.82
9	Fraser and Cordina (1999)	Australia	Agricultural Systems	Dairy farm	50	0.905
10	Galanopoulos, Aggelopoulos, Kamenidou, and Mattas (2006)	Greece	Agricultural Systems	Pig	100	0.83
11	Gaspar, Mesías, Escribano, and Pulido (2009)	Spain	Livestock Science	Livestock	73	0.86
12	Gelan and Muriithi (2012)	Africa	Agrekon	Dairy farm	371	0.8
13	Ghorbani, Mirmahdavi, and Rahimabadi (2009)	Iran	Asian Journal of Animal Sciences	Caspian cattle	70	0.87
14	Günden, _S¸ahin, Miran, and Yildirim (2010)	Turkey	Journal of Applied Animal Research	Dairy farm	87	0.615
15	Heidari, Omid, and Akram (2011)	Iran	Brazilian Journal of Poultry Science	Poultry	44	0.92
16	Henderson et al. (2016)	Africa	Agricultural Systems	Crop-livestock	146	0.72
17	Hosseinzadeh-Bandbafha et al. (2017)	Iran	Energy	Dairy farm	30	0.937
18	Hosseinzadeh-Bandbafha, Safarzadeh, Ahmadi, and Nabavi-Pelesaraei (2016)	Iran	Journal of the Saudi Society of Agricultural Sciences	Dairy farm	110	0.9
19	Iliyasu and Mohamed (2016)	Malaysia	Aquaculture Reports	Fish	100	0.86
20	Iribarren, Hospido, Moreira, and Feijoo (2011)	Spain	Science of the Total Environment	Dairy farm	72	0.43
21	Kaliba and Engle (2006)	USA	Aquaculture Economics & Management	Catfish	44	0.57
22	Kelly, Shalloo, Geary, Kinsella, and Wallace (2012)	Ireland	Irish Journal of Agricultural and Food Research	Dairy farm	190	0.833
23	Payandeh, Kheiralipour, Karimi, and Khoshnevisan (2017)	Iran	Energy	Broiler	90	0.91
24	Shortall and Barnes (2013)	Scotland	Ecological Indicators	Dairy farm	200	0.827
25	Wettemann and Latacz-Lohmann (2017)	Germany	Agricultural Systems	Dairy farm	216	0.83
26	Yusuf and Malomo (2007)	Nigeria	International Journal of Poultry Science	Poultry egg	49	0.873
27	Zongli et al. (2017)	China	Aquaculture Economics & Management	Tilapia	48	0.97

Source: The authors.

Table 2.

Categorical Variable Coding

Variable		Frequency	Coding
Product	Vegetable	40	0
	Livestock	27	1
	Fruit	26	2
	Flower	7	3
Size	1–30	20	0
	31–100	51	1
	Above 100	30	2
Location	Other countries	21	0
	Asian countries	79	1
Iran	Other countries	56	0
	Iran	44	1
Dairy	Others	90	0
	Dairy	10	1
Year of study	1997–2010	29	0
	2011–2017	71	1
Cereals	Others	80	0
	Cereals	20	1
India	Other countries	95	0
	India	5	1

Source: The authors.

Table 3.

Dependent Variable

Original Value	Internal Value
80–100%	1
Below 80%	0

Source: The authors.

Table 4.

Results of the Logistic Regression Analysis

Variables	B	SE	Sig.
Sample size (compared to 1–30)	—	—	0.245
Size (31–100)	1.252	0.852	0.142
Size (above 100)	0.043	0.609	0.944
Location (Asian countries)	−0.060	0.742	0.935
Iran	−1.084	0.700	0.121
India	0.241	1.084	0.824
Year	−0.014	0.627	0.982
Product (compared to vegetable)	—	—	0.004
Product (livestock)	3.051	1.356	0.024
Product (fruit)	5.152	1.503	0.001
Product (flower)	2.529	1.290	0.050
Cereals	0.076	0.704	0.914
Dairy	0.608	1.136	0.592
Constant	−3.278	2.221	0.140

Source: The authors.

Note: −2 Log likelihood = 108.020, Cox & Snell R² = 0.239 and Nagelkerke R² = 0.323.

Table 5.

Hosmer and Lemeshow Test

Chi-square	Sig.
13.814	0.087

Source: The authors.

Table 6.

Findings of the Classification Accuracy Based on Logistic Regression Analysis

Observed		Predicted
		TE		% Correct
		80%–100%	Below 80%	% Correct
TE	80%–100%	26	15	63.4
	Below 80%	11	48	81.4
	Overall %			74.0

Source: The authors.

Figure 3.

Source: The authors.

Figure 4.

Source: The authors.

Livestock Farming

Livestock farming greatly applies to cattle or dairy cows, poultry, goats, hogs, horses and sheep; however, this section of farming is also largely relevant for other livestock such as donkeys, mules, rabbits and flying insects such as honey bees which are reared worldwide as part of animal farming. Developing countries and the gradual increasing human population on planet Earth are demanding more animal protein. Our collected data in Table 1 show that 27 past observations applied DEA in different livestock farming for fish, dairy cows, chickens, pigs and sheep. Fraser and Cordina (1999) made a DEA approach for examining the productivity in dairy farming which was an irrigated farming system in Northern Victoria, Australia. In their research, it was stipulated that DEA provides a useful technique for an efficient benchmarking of the dairy farm industry. Implementation of DEA by the authors led to a systematic approach to find a relationship among all consuming inputs and outputs, simultaneously. Their research indicated that DEA provided a vital and essential estimate of efficiency for dairy farms. They further concluded that DEA provides a relative estimate of efficiency, and the approach indicated the inputs and outputs which were not utilized effectively.

Logistic Analysis and Coding of Variables

Several independent variables were identified in the context of the analysis. A multiple logistic regression technique was adopted for the selected variables. The logit of the logistic regression model can be explained as follows (Hosmer & Lemeshow, 2000):

g (x) = \ln (\frac{π (x)}{1 - π (x)}) = β_{0} + β_{1} x_{1} + β_{2} x_{2} + \cdot \cdot \cdot + β_{p} x_{p}

(5)

In case of multiple logistic regression,

π (x) = \frac{e^{g (x)}}{1 + e^{g (x)}} ​

(6)

Numbers which were generally used to denote different levels of the independent variables of nominal characteristics were simple identifiers and possessed no numeric value. In these situations, the use of design or dummy variables was the best approach. For instance, in this study, the independent variables were coded as 0 or 1 in some cases. Now, the difference in the logit for both conditions is as follows:

g (1) - g (0) = (β_{0} + β_{1} \times 1) - (β_{0} + β_{1} \times 0) = β_{1} ​

(7)

In logistic regression, odds ratio can be explained by proportion of odds from x = 1 to the odds of x = 0, mathematically (Hosmer & Lemeshow, 2000):

O R = \frac{(\frac{π (1)}{1 - π (1)})}{(\frac{π (0)}{1 - π (0)})} ​

(8)

O R = \frac{\frac{\frac{e^{β_{0} + β_{1}}}{1 + e^{β_{0} + β_{1}}}}{\frac{1}{1 + e^{β_{0} + β_{1}}}}}{\frac{\frac{e^{β_{0}}}{1 + e^{β_{0}}}}{\frac{1}{1 + e^{β_{0}}}}} ​

(9)

Therefore, in the case of multiple logistic regression analysis comprising binary explanatory variables coding 0 and 1, the connection between the regression coefficient and odds ratio is:

\begin{matrix} O R = e^{(β_{0} + β_{1}) - β_{0}} \\ O R = e^{β_{1}} ​ \end{matrix}

(10)

Variables were nature of the product, sample size, year of study, location (Asian countries including India and others including Iran), dairy and cereals. To investigate we interpreted the model as TE = f (product, sample size, year of study, location, Iran, India, dairy and cereals)

In Table 2, all the selected variables are given with the code. In this meta-analysis, logistic regression was performed to identify the respective variables responsible for variations in farmers’ efficiency level (Lee et al., 2015).

Results and Discussion

After analysing all the input variables, product variables with coded vegetables, fruits, flowers and livestock were found to be effective with statistical significance. Vegetable crops, flowers, fruits and livestock groups indicated positive significance. Therefore, the variables were statistically effective against TE variation. Table 3 indicates the dependent variable coding with 1 and 0 for high and low efficiencies, respectively. In Table 4, results of the logistic analysis also showed the desired scores, but there was no effective significance. The year of study ( p = 0.982), location ( p = 0.935), study at Iran ( p = 0.121), study at India ( p = 0.824) and sample size of 1–30 ( p = 0.245), 31–100 ( p = 0.142) and above 100 ( p = 0.944) did not contribute to the evaluation. Analyses were found to be moderate and close to significant for sample size ( p = 0.142) and study at Iran ( p = 0.121). However, the study at Iran has negative impacts on TE variation, despite being significant. This result indicates that for a unit increase in study at Iran, the probability of TE variation decreases by 1 per cent. Results also indicated that year of study and country-wise location did not contribute to efficiency measurement. However, TE variations were slightly better for dairy products than cereals. Out of the 100 studies, 79 were in Asia and 21 were from another side of the world, 44 were from Iran, 5 were located each in India, Pakistan and Bangladesh, while Malaysia, China, Japan, Uzbekistan and Vietnam held a smaller share. Out of 100 investigations, 20 products were cereals and 10 belonged to dairy. The mostly observed sample size was 31–100 with 51 investigations, above 100 (30) and 1–30 (20) investigations.

Table 5 shows that statistically there was no significance in the Hosmer–Lemeshow analysis (p = 0.087). However, the goodness of fit was determined for the model by the accuracy of classifications in Table 6; the accuracy was 74.00 per cent. The model predicts 81.40 per cent accuracy for cases in which TE was below 80 per cent. In some cases, the model accuracy was poor, where 80–100 per cent TE was reported. Therefore, a scope of future research is there to identify the variables influencing farms with higher TE. The meta-analysis indicated that higher values for TEs were linked with vegetable crops and fruit cultivation with floriculture and livestock farming . None of the other independent variables were found to be responsible, although there were few with modest significance.

We have taken 100 studies in our analysis, which are classified as 39 papers on vegetable farming, 27 papers each on fruit and livestock farming, and 7 papers on floriculture. Out of the total 100 studies, there were 59 studies having efficiency range from 80 to 100 per cent. Several studies on cereals were identified, comprising rice, wheat and corn. Many of the studies also evaluated eco-efficiency and other environmental impacts. The literature search and compilation will open new doors for future researchers. Considering the applicability of DEA in different sectors of agriculture, numerous papers were published in different journals. Figure 3 represented the distribution of journals on the DEA approach in agriculture, where journals with maximum contribution were taken for estimation. This investigation illustrated that Journal of Cleaner Production and Journal of Energy had 11 publications each on the DEA approach in different farming practices.

Results which are exhibited in this research showed that the DEA is a combination of several inputs and outputs in a linear programming technique utilized for estimating respective efficiency in agricultural production, where a farmer is a DMU (Bravo-Ureta et al., 2007). Each DMU selected necessary weights for input and output related to production. According to the long-established definition of efficiency, DEA is the proportion of the weighted sum of outputs to a weighted sum of inputs.

As per the results based on this study, the broad differences in the technical efficiencies show that there is a need for awareness among farmers to operate the farming technique, rightfully. Technological awareness in operating farms is necessary to optimize a farmer’s income (Zhang, Wang, & Duan, 2016). Definite governmental authorities and private sectors could help in the minimization of input costs to obtain output gains. Results indicate that the farmer’s education level and experience have significant positive effects on efficiency. Further, the wasteful uses of production costs by inefficient farmers were also informed. In recent years, evaluating farmer’s efficiency in an agricultural community has become a vital issue. Numerous researchers have put forward various qualitative and quantitative ideas to optimize income generation in the rural sector. It can be argued that to prevent the impoverishment of farmers, the production efficiency needs to be optimized and reinstatement of government-controlled policy interventions is necessary. In determining performance, TE is the means for developing new technologies and ideas which permit low input costs and low power-consuming inputs in farming. Again, commercial energy utilization in agricultural production is one of the important resources, along with human labour and economy (Karami Dehkordi, Kohestani, Yadavar, Roshandel, & Karbasioun, 2017).

After the discovery of DEA, it was applied in different commercial sections of the society from banking to industries to agriculture. Since this investigation aims to escalate the income maximization of farmers, the application of DEA in agriculture is of primary concern. Figure 4 represented the high contribution of articles in different years. It is evident that DEA was used extensively in measuring the efficiency of farming practices. In this article, 100 past studies were used for the analytical purpose. Furthermore, this study comprises 130 references. Year 2011 has seen the greatest contribution of DEA in the context of this study, with 17 papers published in different reputed journals. 2012 and 2013 also hold a major share with 15 and 12 papers published, respectively.

Conclusion

In this article, a meta-analysis of the application of DEA on different farming practices was done. Papers were reviewed to gather statistical data from 100 published articles. All articles were collected from 60 different journals from 1997 to 2017. The review and the extraction indicated that DEA is an appropriate model to be utilized in evaluating farmers’ efficiency. In most of the developing countries, there is a low availability of good quality food and water resources as the population is increasing exponentially. To meet the required food and water demand efficiently, optimization and allocation of resources are necessary for the agricultural sector. Data which were collected from farmers supplied essential source of production information on different farming systems such as vegetable farming, fruit crops, floriculture and livestock farming. The DEA is an extremely suitable technique to analyse this data and provide relevant information on farmers’ inefficiencies. This technique helps to separate inefficient producers from efficient ones. It has also supported in reviewing the uneconomical consumption of energy by inefficient producers (Elhami, Akram, & Khanali, 2016). Thus, the more the optimal allocation of resources, the more the income generation. Governing bodies in a country can make the farmers aware of new technical achievements, like the proper use of groundwater in conjunction with canal water can maximize the farm income. Thus, the systematic review presented here suggests the effective use of inputs such as human labour, fertilizers, irrigation water and electricity/diesel to adjust the farm-level allocative, cost and technical efficiencies. This systematic review aims to make the research more accessible to researchers all over the world. The authors hope that findings in this study enrich the data and provide farmers’ productivity levels to different strata of academicians and institutional scholars, R&Ds, economists and agriculture-based organizations.

Also, the utilization of cost-effective farming tools which require no fossil fuel to run must be promoted in developing countries. These actions will minimize the human labour requirement at the time of high demands and finish the labour-intensive process in time. If the operations are completed in time with a better standard of cultivation and high quantity, then the financial expenditure of the overall production will be reduced.

This study examined the effects of related variables on efficiency with the help of logistic regression. Agricultural products, sample size, year of study, location, Iran, India, dairy and cereals were taken as independent variables. The TE was taken as a dependent variable, coded 1 and 0. Agricultural products such as vegetables, fruits, flowers and livestock were found significant, and the location variable comprising all countries was insignificant. However, Iran as a variable with 44 observations out of 100 past studies and the sample size, which also has been taken as a variable in this study, ranging from 31–100, was found statistically close to being significant. Among the Asian countries, we observed high variation in efficiency scores, as most of the papers are based on Iran, whereas very few are referring to India, Bangladesh, Pakistan, Vietnam, Malaysia, China and Japan. 2011 witnessed the highest number of publications in the context of our investigation; however, no such significant effect was observed. These empirical results in our study and a large compilation of identified data should assist future research for better quality and coherence in farm-level production efficiency measurement.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Note

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