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Technical efficiency and agricultural sustainability–technology gap of maize producers in Fars province of Iran

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Abstract

Fars has a special place in Iran in terms of natural resources and diverse climate aiming this province to increase production of major crops such as corns. In light of increasing domestic production and preventing yield loss of maize, many farmers utilize high quantities of pesticides, chemical fertilizers and over-extract groundwater without considering immediate and long-term consequences of such operations on environment. The main purpose of this study is to investigate technical efficiency and sustainability–technology gap ratio (STGR) of maize producers by agricultural sustainability in Fars province in Iran. Technical efficiency is considered as a key element among the triple elements of sustainable development (economic, social and ecological). Applying model of agricultural sustainability and compromise programming method, regions were classified into three groups (sustainable, relatively sustainable and unsustainable), and data were collected interviewing a total of 300 farmers in 2008–2009 from Kazerun, Firouzabad and Marvdasht chosen randomly and systematically as representatives of these three groups. Technical efficiencies and STGRs were calculated for the regions applying stochastic production frontier, regional stochastic frontier functions and the metafrontier. The results indicate that assuming the same technology between the fields (traditional methods) leads to overestimation of technical efficiency. Mean STGRs in Marvdasht, Firouzabad and Kazerun were found to be 59.3, 71.1 and 68.9, respectively. This suggests that technical efficiency and STGR of relatively sustainable regions are higher than those of the unsustainable regions. Thus, farmers in these areas can reduce gap between technology and agricultural sustainability levels via achieving metatechnology that is compatible with sustainable agriculture.

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Notes

  1. In this study, four weight groups were used for each indicator.

  2. The Divisia index is calculated as: \(Xi4(k) = \prod\nolimits_{j = 1}^{6} {p_{ij(k)}^{{\alpha_{ij} }} }\) where i = 1, 2,…, N k ; α ij(k) denotes share of jth input in variable cost for the ith farm in the kth group; P i1(k) denotes the cost of labor (in toman); P i2(k) denotes the cost of irrigation (toman); P i3(k) denotes the cost of pesticides (toman); P i4(k) denotes the cost of animal fertilizer (toman); P i5(k) denotes the total cost of machinery(toman); P i6(k) denotes other cost (toman) for the ith farm in the kth group.

  3. First group is recognized as sustainable region and includes Abadeh, Kazerun, Lamerd and Mamasani. Second group includes Arsanjan, Darab, Eqlid, Fasa, Firouzabad and Lar and is categorized as relatively sustainable, and the third group includes Estahban, Jahrom, Marvdasht and Shiraz and is realized as unsustainable.

  4. The LR statistic is given by \(\lambda = - 2\left[ {\ln (L(H_{0} )/L(H_{1} ))} \right] = - 2\left[ {\ln (L(H_{0} )) - \ln (L(H_{1} ))} \right],\) where ln[L(H 0) is value of likelihood function under H 0. The results of each model are detailed in Table 5.

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  1. Department of Agricultural Economics, Shiraz University, Shiraz, Iran

    Farnaz Pourzand & Mohammad Bakhshoodeh

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  1. Farnaz Pourzand
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Correspondence to Farnaz Pourzand.

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Pourzand, F., Bakhshoodeh, M. Technical efficiency and agricultural sustainability–technology gap of maize producers in Fars province of Iran. Environ Dev Sustain 16, 671–688 (2014). https://doi.org/10.1007/s10668-013-9501-x

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