metadata: authors: 0: author: Ali, Mubarik
1: author: Byerlee, Derek
count: Pakistan
docna: 0: docna: Productivity growth and resource degradation
            in Pakistan's Punjab - a decomposition analysis
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Livestock and Animal Husbandry,Hydrology,Food Security,Armed Conflict
teratopic: Agriculture
abstracts: cdata!: The introduction of green revolution
            technologies in wheat, and rice production in Asia, in the
            mid 1960s reversed the food crisis, and stimulated rapid
            agricultural, and economic growth. But the sustainability of
            this intensification strategy is being questioned, in light
            of the heavy use of external inputs, and growing evidence of
            a slowdown in productivity growth, and degradation of the
            resource base. The authors address the critical issue of
            long-term productivity, and the sustainability of
            Pakistan's irrigated agriculture. To estimate changes
            in total factor productivity in four production systems of
            Punjab province, they assemble district-level data on 33
            crops, 8 livestock products, and 17 input categories. They
            find that average annual growth in total factor productivity
            was moderately high (1.26 percent) for both crops, and
            livestock for the period 1966-94, but observe wide variation
            in productivity growth by cropping system. A second,
            disaggregated data set on soil, and water quality reveals
            significant resource degradation. The authors use the two
            data sets to decompose the effects of technical change, and
            resource degradation through application of a cost function.
            They find that continuous, and widespread resource
            degradation (as measured by soil and water quality
            variables) has had a significant negative effect on
            productivity, especially in the wheat-rice system, where
            resource degradation has more than offset the productivity
            effects of technological change. Degradation of the health
            of the agro-ecosystem was related in part, to modern
            technologies, mono-cropping, and mismanagement of water
            resources. The results call for urgent analysis of
            technology, and options to arrest the degradation of resources.
geo_reg_key: 517198,517191,517212
repnme: repnme: Productivity growth and resource
            degradation in Pakistan's Punjab - a decomposition analysis
display_title: Productivity growth and resource
            degradation in Pakistan's Punjab - a decomposition analysis
listing_relative_url: /research/2000/11/717424/productivity-growth-resource-degradation-pakistans-punjab-decomposition-analysis
topicv3: Agriculture and Food Security m1326205 435,Water m1326522 137,Crop Production m1326627 174,Revolutionary Violence m1327774 2465,Livestock m1504490 4024
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Agriculture
origu: Agriculture & Environmental Serv (AES)
no_of_pages: 38
model_extractions: | POLICY RESEARCH WORKING PAPER 2480

# Summary findings

The introduction of green revolution technologies in wheat and rice production in Asia in the mid-1960s reversed the food crisis and stimulated rapid agricultural and economic growth. But the sustainability of this intensification strategy is being questioned in liglit of the heavy use of external inputs and growing evidence of a slowdown in productivity growth and degradation of the resource base.

Ali and Byerlee address the critical issue of long-term productivity and the sustainability of Pakistan's irrigated agriculture. To estimate changes in total factor productivity in four production systems of Punjab province, they assemble district-level data on 33 crops, 8 livestock products, and 17 input categories.

They find that average annual growth in total factor productivity was moderately high (1.26 percent) for both crops and livestock for the period 1966-94, but observe wide variation in productivity growth by cropping system.

A second, disaggregated data set on soil and water quality reveals significant resource degradation. The authors use the two data sets to decompose the effects of technical change and resource degradation through application of a cost function.

They find that continuous and widespread resource degradation (as measured by soil and water quality variables) has had a significant negative effect on productivity, especially in the wheat-rice system, where resource degradation has more than offset the productivity effects of technological change.

Degradation of the health of the agro-ecosystem was related in part to modern technologies, monocropping, and mismanagement of water resources.

The results call for urgent analysis of technology and policy options to arrest the degradation of resources.

This paper-a joint product of the Rural Development Department and the Asian Vegetable Research and Development Center-is part of a larger effort to support the development of sustainable intensification of irrigated agricultural systems. The study was funded by the Bank's Research Support Budget under the research project "Total Factor Productivity Growth in Post-Green Revolution Agriculture of Pakistart and Northwest India." Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Derek Byerlee, room MCS-759, telephone 202458-7287, fax 202-614-0065, email address dbycrleeCaworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.orgiresearch/worki ngpapers. Mubarik Al may be contacted at mubarik@netra.avrdc.org.tw. November 2000. (30 pages) _The Policy Research Working Paper Series disseminites the findings of work in progress to encourage the exchange of ideas ahout development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fiilly polished. The papers carry the names of the authors and should be -ited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countPtes they represent._ Produced by tvhe Plolicy Research Dissemination Center
# **Productivity Growth and Resource Degradation in**<sup>**Pakistan's**</sup> **Punjab: A Decomposition Analysis**

## Mubarik Ali

Asian Vegetable Research and Development Center,<sup>Taiwan, China</sup> and

## Derek Byerlee

Rural Development Department, World Bank
# **Contents**

|Introduction .............................................|1|
|---|---|
|Analytical Approach .............................................|2|
|Data Sources .............................................|7|
|Major Trends in Punjab's Agriculture .............................................|8|
|_Changes in the crop subsector ................................................_|_9_|
|_Changes in the livestock subsector .................................................._||
|_Trends in resource quality ................................................_|_11_|
|_Trends in productivity ..............................................._|_14_|
|Decomposition of Productivity Growth .............................................|16|
|Conclusions ..............................................|22|
|Appendix<br>.......................||
|References .............................................|27|

### **List of Tables**

|Table 1. Physical and human resource base, size of holding, and land ownership type in the irrigated<br>Pakistan's Punjab .............................................<br>8|
|---|
|Table 2. Input use in the crop and livestock sectors in Pakistan .................<br>............................ 10|
|Table3. Average values of selected water and soil quality parameters in Punjab .................. ................. 12|
|Table 4. Growth rate of partial factor productivity, input, output, and TFP indices .....................................<br>15|
|Table 5. Decomposition of the total change in the crop sector productivity in Punjab during 1971-94 ...... 21|
|Table 6. Effect of modern technology inputs on productivity through depletion of the resource stock ...... 22|

## **List of Boxes**

Box 1. Variable definitions used in the translog cost function for Punjab agriculture, 1971-94 ......... ........ 19

### **List of** Figures

|Figure 1 a. Indices of trends in soil quality .........................................................................<br>13|
|---|
|Figure lb. Indices of tends in tubewell water quality ......................................................................... 13|
|Figure 2. Trends in TFP by production system .........................................................................<br>16|

i
# **Introduction**

The introduction of Green Revolution technologies in wheat<sup>and rice in Asia in the mid-1960s</sup> produced impressive results in reversing the food crisis and<sup>stimulating agricultural and</sup> economic growth. However, questions are now being asked about<sup>the sustainability of this</sup> intensification strategy, in light of high use of external inputs and<sup>growing evidence of a slow</sup> down in productivity growth and degradation of the resource base<sup>(Byerlee, 1992; Pingali and</sup> Heisey, 1996; Huang and Rozelle, 1995; Pagiola, 1995).

These concerns were initially based on declining yields observed in<sup>long-term experiments</sup> (Flinn and De Datta, 1994). Scientists later observed stagnating yields<sup>in farmers' fields despite</sup> growing input use, especially where intensive cereal mono-cropping<sup>had been continuously</sup> practiced (Cassman and Pingali, 1995; Ali, 1996; Byerlee and Siddiq,<sup>1994). These problems</sup> were considered especially important in the wheat-rice belt, the breadbasket<sup>of northern India</sup> and Pakistan which covers over 12 million ha and provides food security<sup>for some 500 million</sup> people (Hobbs and Morris, 1996).

Nonetheless, to date, there is little quantitative evidence of the lack of sustainability<sup>of current</sup> intensification strategies. Part of the problem has been the difficulty of agreeing<sup>on useful</sup> measures of sustainability. Lynam and Herdt (1989) proposed a non-positive<sup>trend in total factor</sup> productivity (TFP) as a good indicator of lack of sustainability of a production<sup>system. This</sup> measure raises several practical issues, such as the level of aggregation<sup>(e.g. crop or system) at</sup> which TFP should be measured, whether inputs should include changes<sup>in resource quality, and</sup> if so, how are those changes to be measured and valued? Recent evidence<sup>of changes in TFP in</sup> Pakistan, for example, provides inconsistent results from negative trends<sup>(Ali and Valesco, 1993)</sup> to significantly positive (Khan, 1994). Moreover, it is not necessary (and<sup>perhaps not possible) to</sup> maintain resource quality for all components in a system in all locations<sup>(Lynam and Herdt,</sup> 1989). In particular, farmers may avoid or mitigate resource<sup>degradation by exploiting</sup> substitution possibilities among inputs and crops.

In addition, TFP alone is a relatively blunt instrument for tracking<sup>sustainability over the long-</sup> term. A positive trend in TFP does not necessarily imply a sustainable<sup>system since rapid</sup> productivity gains from new technologies may mask the effects<sup>of serious resource degradation</sup> caused by technology-led intensification, at least in the short to<sup>medium-term. In order to</sup> overcome this problem as well as to interpret trends in TFP, it is necessary<sup>to track indicators of</sup> agro-ecosystem health, especially measures of soil and water quality (Herdt<sup>and Lynam, 1989).</sup> Few studies have attempted to relate productivity trends to such indicators<sup>(Lindert, 1996; Huang</sup> and Rozelle, 1995).1

> Some studies have estimated the effect of environmental variables, such<sup>as pollution, on productivity (Ball</sup><sup>_et al.,_</sup> _1994_ and Pittman, 1981, ) A recent study by Gollop and Swinand (1998) develops an approach to account for externalities in productivity measures but the theoretical basis is questionable<sup>(Smith, 1998; Weaver, 1998). Others</sup> have related agriculture production to externalities caused by the use of modem<sup>technologies (Antle and Pingali,</sup> 1995). However, measurement of pollution was indirect
The main objectives of this study are first, to estimate total factor productivity at the production system level in the period since the advent of the Green Revolution in Pakistan's Punjab Province, and second, to relate productivity trends to changes in resource quality. Earlier studies have estimated resource productivity at the national or state level (e.g., Rosegrant and Evenson, 1992), or have been restricted to one crop in a region (e.g., Cassman and Pingali, 1995). Since sustainability is likely to relate to underlying agronomic and socioeconomic characteristics of the farming or production system, such as crop rotation, an important contribution of this study is to quantify productivity trends at the production system level defined in terms of the dominant cropping pattern. Considerable resources were invested in collecting the large amount of data on individual crop and livestock products, inputs, and prices that are required for the system-level analysis.

Further, a major contribution of the study is to quantify trends in resource quality and then to econometrically relate trends in productivity to indicators of resource quality. To do this, we establish a second comprehensive data set on soil and water quality variables based on records of tests in farmers' fields. This enables us to at least partially decompose productivity trends into the effects of technological change, improvements in human resources and infrastructure, and natural resource degradation.<sup>2</sup> This study focuses on the measurement of agricultural productivity change in the irrigated agriculture of Pakistan's Punjab province which is the agriculturally dominant province in the country, with a farming population of over 60 million people, and often described as Pakistan's bread basket. Over 80% of the cropped area of the province is irrigated. The Punjab was one of the earliest beneficiaries of the Green Revolution with the introduction of modern varieties of wheat and rice in the 1960s. However, recent studies have questioned the sustainability of current intensification strategies (Byerlee and Siddiq, 1994). In light of recent record imports of wheat, the staple food, the issue is clearly critical to agricultural development strategies and food security in Pakistan, as well as in neighboring areas of India.

The paper is developed as follows. The next two sections outline the analytical approach and the major data sources. This is followed by a description of major trends in the agricultural sector, especially those related to technical change and resource quality. Estimates of TFP growth are then presented and productivity growth decomposed into the effects of technology, resource degradation, human resources, and infrastructure through estimation of a cost function. The last section summarizes the main policy implications.

# **Analytical Approach**

Following Lynam and Herdt, a non-positive trend in TFP has been widely accepted, at least by economists, as an indicator of unsustainable resource management (e.g., Cassman and Pingali, 1995; Ehui and Spencer, 1993). However, many practical problems are encountered in choosing an acceptable measure of TFP apart from the fact that positive TFP growth by itself does not

> 2 Huang and Rozelle (1995) estimated the effects of technological change and environmental degradation using secondary statistics, such as area of land eroded. Preliminary work by Lindert (1996) has used soil testing data although he does not decompose the effects of technological change and resource degradation.

2
necessarily indicate sustainable resource management. For example, Squires included conventional inputs only, while the resource stock, such<sup>as soil nutrient content, was considered</sup> as a technical constraint that influences trends in TFP.<sup>In contrast, Herdt and Lynam and Alston,</sup> Anderson, and Pardey have proposed a measure of<sup>Total Social Factor Productivity (TSFP) that</sup> incorporates changes in resource quality as well as externalities.<sup>3</sup> We prefer Squires's approach in the TFP estimation on<sup>both conceptual and empirical grounds.</sup> Conceptually estimates of TFP are based on the<sup>assumption of profit maximization, which is</sup> violated by attempts to account for market failure in<sup>TSFP measures.4In practice, it is difficult</sup> to value changes in resource quality and externalities,<sup>even where these changes can be</sup> physically quantified. Also, in the short to medium<sup>term covered by this paper, farmers may not</sup> be able to observe resource degradation and therefore<sup>it is exogenous to decision making rather</sup> than endogenous. The direct estimates of TFP using<sup>only conventional inputs and outputs is then</sup> the sum of i) technological progress, ii) changes in<sup>resource quality, and iii) improvement in</sup> technical and allocative efficiency due to investments<sup>in human capital and infrastructure. We</sup> then use a cost-function to decompose productivity<sup>growth into each of these effects. This</sup> provides a production system-specific estimate of the effects<sup>of changes in resource quality on</sup> productivity.

It should be noted that this measure overestimates TFP to<sup>the extent that resource degradation is</sup> internally induced-for example, depletion of soil nutrients to maintain current<sup>production</sup> levels. Also as a measure of sustainability, it ignores externalities<sup>such as effects on human and</sup> environmental health, which may be the major sustainability<sup>issues in some systems. However,</sup> in addition to practical difficulties, the theoretical case<sup>for including such externalities in</sup> agricultural productivity measures is questionable (Smith, 1998;<sup>Weaver, 1998).</sup> The chain-linked Tornqvist-Theil indexing procedure is commonly<sup>used to measure TFP because</sup> it is exact for a flexible linear homogeneous translog aggregate<sup>production function (Diewert,</sup> 1976, and Hulten, 1986). The index is estimated as follows.<sup>5</sup>

- 3 For empirical efforts to apply this approach see Ehui and<sup>Spencer (1993), Antle and McGuckin (1993), Oskam</sup> (1991), Archibald (1988) and Barnett, Payne and Steiner.<sup>(1995)</sup>

- 4 See Murgai (1999) and the recent exchange in American<sup>Journal of Agricultural Economics (Gollop and Swinand</sup> (1998); Weaver (1998) ; Smith (1998).

> 5 The measures of productivity growth will be biased if firms<sup>are in a temporary equilibrium rather than the long-run</sup> equilibrium, implicitly assumed by this estimation method<sup>(Berndt and Fuss (1986) and Hulten (1973)). This</sup> situation may arise, for example, with underutilized capital<sup>and labor capacity. We have no reason to expect this is</sup> the case in this study. Moreover, even if there is under<sup>utilization we have estimated values of the quasi-fixed</sup> inputs using the method specified in Bemdt and Fuss (1986).<sup>That is, we computed the</sup><sup>_value_of services from</sup> stocks of quasi-fixed inputs, rather than using the _quantity_<sup>of these stocks. For example, we took area planted</sup> (i.e., total capacity), not harvested area (utilized capacity)<sup>multiplied by the market rental rate of land. In the case</sup> of tractors and labor, we employed an annual utilization<sup>rate</sup>

3
m ln(X, _/ X,, 1_<sup>_) = 1/_</sup> _2X_<sup>_(sz, + sz,)_ln(xz, /</sup> _xt_.),_ and z=1 (lb) where qit is the quantity of the ith output (i=1,2,...,m number of product types) in the tth period _(t=1,2,...,T_ number of years); xz, is the quantity of the zth input (z=1,2,...,n number of inputs) in the tth period; In(Q, / _Qt-,)_ and ln(Xt / _Xt-l)_ are the weighted rate of change in all outputs and inputs, respectively; and _sz_ and _si,_ are the respectively share of the zth input in total costs and of the ith output in total value of production. This study estimated TFP for crops, livestock, and all agriculture. The partial factor productivities (PFPs) for important inputs (labor, water, and land) were also estimated, in order to help understand underlying trends in TFP.<sup>6</sup> The decomposition of productivity changes can be econometrically estimated through a production function, profit function, or a cost function. We selected the cost function which has a number of advantages over the primal approach. The cost function in translog form<sup>7</sup> can be expressed as:

> 6To estimate PFPs the summation sign in (lb) is removed to estimate the input index, and the input share is set to one. The output index remains unchanged.

> 7The translog form is preferred because specific features of technology (like returns to scale or homotheticity) may be tested by examining the estimated model parameters. Specifically, if the technology is homothetic, the dual cost function is multiplicatively separable in output quantities and input prices. However, the function does not allow the possibility of weak input-output separability. The approximation potential of the translog and other locally flexible forms has been called into questions in a number of studies (Chalfant, 1984; Thompson, 1988).

4
where C is total cost, _qi_ is output, w, is the price of the zth input,<sup>_I,_are variables related to</sup> technological innovations (such as proportion of modem<sup>variety area),</sup><sup>_So_are resource quality-</sup> related variables (such as organic matter content in soils),<sup>_Hf_are human and infrastructure-</sup> related variables (such as literacy rate, and distance from road), _Tr_ is<sup>an annual index of time for</sup> production system r, and _Dd_ is a dummy variable for<sup>district d. The effect of technological</sup> change on cost is controlled through the coefficient _u_ (expected<sup>to be negative), and the effect of</sup> changes in resource quality are measured through 0 plus the<sup>coefficients of the interaction terms</sup> between resource quality and other variables evaluated at the<sup>mean value of these variables (the</sup> sign will be positive or negative depending on a negative<sup>or positive definition of resource</sup> quality). The effect of improvements in human resource<sup>and infrastructure variables on</sup> productivity is estimated through y (expected to be negative).<sup>The system-specific trends in</sup> technology, resource quality or efficiency, not captured by the above<sup>variables are estimated</sup> through **X.** In addition to the traditional interaction terms of the translog function<sup>between input prices and</sup> outputs, selected other interaction terms were also included<sup>to keep the number of estimated</sup> coefficients manageable. The effects of variable inputs, output level, and<sup>technological change</sup> on production may depend upon the resource stock variables.<sup>To capture these effects, the</sup> interaction between each of input prices, output, and technology<sup>variables with resource stock</sup> variables were introduced. Moreover, as the study covers<sup>a relatively long period of time,</sup> expected overtime changes in input and output elasticities are captured<sup>by including the</sup> interaction between input prices and outputs with the trend variable.<sup>8</sup> For the cost function in (2) to be homogenous of degree 1 in input prices<sup>requires:</sup> From the translog cost function, we derive input share equations using<sup>the Shephard's lemma</sup> and assuming the profit maximization behavior as follows:

> 8 Technological change is assumed to be Hick's neutral, therefore<sup>interactions of technological change variables</sup> with input prices and output levels are not included. The same is true for the<sup>infrastructure variables. Likewise,</sup> resource stock and infrastructure variables can safely be assumed to<sup>independently affect productivity, and their</sup> interactions are excluded. The resource stock, infrastructure, and technology<sup>variables are assumed to affect</sup> productivity linearly, thus the square terns for these variables<sup>are not included. The exclusion of these variables</sup> keeps the number of estimated parameters manageable, and reduces<sup>multicollinearity</sup>

_5_
where Sz are input share in the total cost as defined before. The condition that all factor shares add up to one requires to impose the same restrictions on the paraineters of the factor share equation as in (3).

Additionally assuming marginal cost pricing for the outputs, we obtain the "revenue share" equations, as follows: Estimating the full dual system (i.e., cost and share equations together) results in much higher efficiency. Therefore, in this analysis equations (2), (4), and 5) were estimated simultaneously, along with the restriction specified in equation (3). Including the revenue share equation and equating its coefficients with the respective coefficients in the cost function, has the advantage of relaxing the assumption of fixed output(s), usually associated with application of the cost function.

One of the main objectives of this study is to decompose productivity growth. This can be done using the cost function coefficients and respective growth rates of the variables causing growth. The growth rates due to technological change (GI), human and physical infrastructure development (GH), and changes in resource stock (Gs) can be estimated as: where _alnSJaT,_ alna/aT, and alnHIaT are the percentage growth rates during the study period in resource stock, technology, and infrastructure variables, respectively. Apart from these, there may be some unidentified sources of productivity and degradation sources due to, for example, change in pest complex, or deterioration in the genetic potential of varieties.. These are captured through regional trend coefficient A . Total productivity growth due to all these factors (parallel to the TFP estimated with the index number approach) will be . _GH + G1 +GS +A_ 6
# Data Sources

We used district-level data for all 16 irrigated<sup>districts of Punjab, defined as those having at least</sup> 50% irrigated area. Together they account<sup>for over 90% of agricultural production in the Punjab.</sup> The district level data were then aggregated<sup>to the production system level for purposes of</sup> computing TFP. Production systems were<sup>defined based on: i) cropping intensity, and ii) the</sup> major summer (or kharif) crop during<sup>1980-81. Wheat is the dominant winter (or rabi) crop</sup> everywhere, accounting for over 80% of cropped<sup>area. Thus the districts of the province were</sup> divided into four commonly recognized systems<sup>based on the dominant summer (kharif) crop: 1)</sup> wheat-mixed summer crops (often maize<sup>or sugarcane), 2) wheat-cotton, 3) wheat-rice, and 4)</sup> wheat-mungbean (or wheat-fallow).<sup>9</sup> These systems represent quite different agronomic<sup>types</sup> from a cereal-legume or fallow system (more likely<sup>to be sustainable) to a continuous cereal</sup> cropping system (likely to be less sustainable-Hobbs and Morris, 1996). Livestock are important in all systems, especially cows and buffaloes<sup>for milk and meat.</sup> District level annual data were collected from<sup>secondary sources and the Statistical Division of</sup> the Federal Ministry of Food and Agriculture,<sup>at a high level of detail for the period 1966-94.</sup> The study includes 33 crops, 8 livestock products,<sup>six crop by-products, and 17 input categories.</sup> Inputs and outputs were valued at farmgate harvest<sup>prices, also collected or estimated at the</sup> district level.

Input categories for the crop sector included land,<sup>labor, water, machinery (separately for tractor,</sup> thresher, and harvester), draught animals, fertilizer<sup>(separately for nitrogen, phosphorus, and</sup> potash), and pesticide costs (separately for aerial<sup>and ground spray), and for the livestock sector,</sup> labor, fodder and feed, and interest and maintenance<sup>costs (shed, veterinary supplies, and other</sup> cost).<sup>10</sup> The annual use of these inputs for 19 districts<sup>over a period of 1966-94 was estimated.</sup> All stock values were converted to flow values.<sup>Land was evaluated at its rental value, and</sup> family labor at its opportunity cost of unskilled<sup>labor. Labor stocks were multiplied by a year-</sup> and gender-specific participation rate (number<sup>of days labor used in agriculture in a year), based</sup> on household survey data collected annually by<sup>the Punjab Economic Research Institute (PERI).</sup> Similarly, machinery service price include interest<sup>and depreciation on capital, and operation</sup> cost which includes labor, fuel, shed and maintenance<sup>costs based on annual utilization rates.</sup> Changes in the quality of conventional inputs are<sup>often confounded with changes in TFP (Alston,</sup> Norton, and Pardey, 1995). To avoid this problem<sup>i) labor was disaggregated into skilled and</sup> unskilled labor based on the rural literacy rate<sup>in each district; ii) land was divided into irrigated</sup> and unirrigated land; and iii) water into canal and<sup>tubewell water. The price of each input-quality</sup> type were separately estimated based on annual<sup>surveys by PERI. This high level of</sup> disaggregation resulted in a much richer data set<sup>than previously employed for analysis of</sup> productivity trends in the Punjab.

> 9 District boundaries define these systems reasonably<sup>well. In three of the systems there is one dominant crop</sup> rotation. Only in the wheat-mixed system is there a<sup>much variation in crop rotation. In the wheat-mungbean</sup> system the majority of the land is fallowed in the summer<sup>season due to water scarcity. See Byerlee and Hussain</sup> (1992) for details.

> 10 Depreciation cost was not included in livestock<sup>as appreciation of the younger stock was assumed to cancel the</sup> depreciation of older stock.

7
Finally, district-level data for the variables on resource quality based on soil and water testing were collected from the Punjab Soil Fertility Department. These variables represent values, averaged by district and year, from thousands of soil tests (organic matter, phosphorus content, pH, and soluble salts) conducted by the department for scientists and farners. While not strictly a random sample, we have no reason to believe there will be systematic biases by district or over time. There has also been considerable concern about secondary salinity and sodicity caused by use of low quality tubewell water (Siddiq, 1994; Byerlee and Siddiq, 1994). This was captured by a similar data set on tubewell water test values (residual carbonate and electroconductivity) by district and year.

Growth in TFP was analyzed for three periods corresponding to different phases of Green Revolution technical change (Byerlee, 1992): the Green Revolution period, 1966-74, when modem varieties were widely adopted with associated inputs, the input-intensification period, 1975-84, when input use increased rapidly, and a post-Green Revolution period, 1985-94, when input use leveled off. However, the cost function analysis was restricted to the whole period, 1971-94, because of the non-availability of resource quality data prior to 1971.

# **Major Trends in Punjab's Agriculture**

The major characteristics of Punjab agriculture are described in table 1. Farm size which now averages 3.9 ha has continuously declined over the past three decades, with a decreasing share of that land farmed by the tenant. At the same time, human resource investments and infrastructure have steadily improved over this period; however, rural literacy remains very low.

**Table 1. Physical and human resource base, size of holding, and land ownership type in the irrigated Pakistan's Punjab**

_By region and period (1966-94)_

||**_Period_**<br>|**_Wheat-_**<br>**_mixed_**<br>|**_Wheat-rice_**|**_Wheat-_**<br>**_cotton_**|**_Wheat-_**<br>**_mungbean_**|**_All_**<br>**_Punjab_**|
|---|---|---|---|---|---|---|
||Green Revolution<br>|4.9|4.4|_5.3_|7.0|5.3|
|Farm size (ha)|Intensification<br>|4.5<br>|4.0<br>|4.9|6.5|4.8|
||Post Green Revolution<br>|3.7|3.2|4.0|_5.2_|3.9|
||Green Revolution<br>|45.6|43.5|49.5|43.7|44.4|
|Rented land (%)|Intensification<br>|36.8<br>|38.3|42.2|37.4|37.3|
||Post Green Revolution|28.9|28.9|33.5|29.8|29.1|
||Green Revolution<br>|21.9|21.3|15.8|16.1|18.8|
|Literacy (%)|Intensification<br>|28.9<br>|28.6<br>|22.1<br>|22.1<br>|25.4|
||Post Green Revolution<br>|33.9|32.5|25.1|22.2|28.7|
|Distance from|Green Revolution<br>Intensification|6.0<br>40|6.5<br>44|7.6<br>48|9.2<br>95|7.2<br>54|
|<br>paved road (krn)|<br>Post Green Revolution|.<br>2.1|.<br>2.5|.<br>2.2|.<br>3.8|.<br>2.5|

Green Revolution =1966-74; Intensification = 1975-84; Post Green Revolution, =1985-94.

_Source:_ Pakistan Agriculture Census reports data on these parameters for the period 1971, 1981, and 1991. The values for other ye extrapolated using a constant growth rate in between the two periods. The values reported here are the average for the study period.

8
# _Changes in the crop subsector_

Crop growth in the Punjab has largely been through intensification. Land area has increased at only 0.7% annually since 1966. Technological change in Pakistani Punjab's agriculture was triggered by the introduction of modem varieties (MVs) of wheat in 1967 which covered almost all wheat area by 1983. MVs of rice were also adopted but were limited by farmers' specialization in high-valued aromatic Basmati rice varieties produced largely for the export market. However, an improved high value rice variety (Basmati 385) was released in 1985 and rapidly adopted (Sharif et al., 1992). Similarly new high-yielding cotton varieties were widely adopted in the post-Green Revolution period.

Use of modem varieties stimulated rapid input intensification (table 2). Fertilizer use jumped from an average of 14 kg of nutrient per ha of cropped area in the Green Revolution period to an average of 86 kg per ha in the post-Green Revolution period. Pesticide use also increased rapidly, especially for cotton in the post-Green Revolution period. Finally, total supply of water and its timely availability have been greatly improved through investment (largely private) in tubewells, especially during the Green Revolution and intensification periods. The wheat-rice system is most dependent on tubewell water.

The Green Revolution technology increased the demand for labor so that average labor use for crops increased from 85 person-days per ha in the Green Revolution period to 99 days per ha in the intensification period (a 15% increase) (see table 2). However, the additional labor demand together with off-farm demands, notably from the Middle East, pushed the wage rate up and induced the adoption of mechanical technology. This decreased labor use for crops to 71 days/ha in the post-Green Revolution period. Meanwhile, mechanical power (tractor, harvester, and thresher) increased from 1.5 hr per ha in the first period to 14.8 hr per ha in the most recent period replacing draft power use which decreased dramatically from 79.6 days to 8.5 days per ha over the same period. Considerable across region variation in input intensification can be seen from table 2.

9
# **Table 2. Input use in the crop and livestock sectors in Pakistan**

|_By cropping region an_|_d period, 1_|_966-94_||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Sector_**|**_Wh_**|**_eat-mixed_**||**_Wh_**|**_eat-rice_**||**_Crop S_**<br>**_W_**|**_ystem by P_**<br>**_heat-cott_**|**_eriof_**<br>**_on_**|**_Whea_**|**_t-Mungbe_**|**_an_**|**_Al_**|**_l Punjab_**||
||**_1_**|_2_|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|
|Crop**(hal)**||||||||||||||||
|Fertilizer (kg)|14.1|46|76|12.3|44.8|64.7|18.5|62.3|120.3|6.1|26|49.1|14.1|48.3|86.1|
|Plant<br>|18.2|24.6|23.9|18.9|28.3|13.4|21.3|35.6|70.2|11.1|16.6|25.3|18.3|27.9|40.1|
|protection (%)||||||||||||||||
|Labor (days)|89.5|108.4|84.6|73.6|88.7|69.4|87.2|100.7|65.8|82.4|86.4|62.6|85|98.7|71.1|
|Bullock (days)|74.4|45.6|10.8|72.9|38.1|6.5|85.4|49.6|7.7|83.8|45.8|8|79.6|45.8|8.5|
|Machine (hr)|1.4|6|14.7|1.6|7.1|15.7|1.7|6.4|17.3|0.8|2.6|9.1|1.5|5.7|14.8|
|Water (acre ft)||||||||||||||||
|Tubewell|1.1|1.9|2.5|2.8|5|6|1.5|2.4|2.6|0.6|1.4|2|1.4|2.5|3|
|Canal|4.9|4.6|4.2|2.6|2.5|2.1|5.9|5.4|4.8|3.5|3.2|2.9|4.7|4.3|3.9|
|Irrigated area(%)|81|85.2|84.7|74|85.2|88.6|97.2|98.1|98.4|59.7|61.5|67|81.9|85.1|86.3|
|Crop intensity (%)|113|130|132|132|139|147|121|128|147|104|109|118|117|126|136|
|**Livestock (SAU.l)b**||||||||||||||||
|Labor (days)|35.8|41.5|42.5|32.6|37.8|40.3|33.6|35.6|34.5|30.2|35.2|30.5|33.8|37.9|37.4|
|Feed &fodder(t)|3.19|3.87|4.11|3.5|3.8|4.13|4.07|3.91|3.66|2.07|2.42|2.51|3.4|3.7|3.7|
|Others (Rs)|50.9|60.4|61.9|52.5|62.4|63.3|49.6|58.6|61.9|45.9|54.3|59.5|_50_|59.1|61.7|

1 = Green Revolution 1966-74,2= Intensification 1975-84, 3 = Post Green Revolution, 1985-94

## bStandard Animal Unit

Source: Authors' own calculation 10
The yield of all crops in the province increased<sup>at an average rate of 1.8% per annum led by</sup> wheat and cotton. The highest yield gain occurred<sup>in the Green Revolution period. The</sup> introduction of short-duration varieties of major<sup>crops, supported by increased water availability</sup> triggered double-crop cultivation on the same<sup>land. Overall, the cropping intensity rose about</sup> 30% during 1966-94.

The production of all crops in the province increased<sup>at the rate of 3.3% per annum during the</sup> study period, slightly higher than the rate of population<sup>growth. The rate of growth was highest</sup> in the Green Revolution period at 3.8% per annum,<sup>and then declined to about 3% as yield</sup> growth in wheat slowed sharply. Production<sup>growth rates were maintained in the post-Green</sup> Revolution period due to rapid increases in<sup>cotton yields and release of new early maturing</sup> mungbean varieties. There were also significant<sup>differences in performance by system.</sup> Production growth in the wheat-cotton and<sup>wheat-mungbean systems was double that of the</sup> wheat-rice system.

# _Changes in the livestock subsector_

Livestock production increased at about the same rate<sup>as for crops (3.4%), although causes of</sup> this increase are less apparent than in the crop sector. The<sup>growth rate in all livestock outputs</sup> jumped sharply in the post-Green Revolution period.<sup>The major jump occurred in milk</sup> production which is due in part to the substitution<sup>of bullocks by milking cows and buffaloes.</sup> The high growth in meat production during the<sup>later periods was mainly because of the</sup> slaughtering of bullocks due to tractorization. Changes in<sup>the livestock sector across region were</sup> not uniform, although they varied less than in<sup>the crop sector.</sup>

# _Trends in resource quality_

There are strong indications that soil and water<sup>quality in the province has deteriorated (table 3</sup> and figures la and lb). For example, average soil<sup>organic matter was already lower than 1%</sup> during the early 1970s, and this has further deteriorated<sup>in all production systems at an average</sup> annual rate of 2.3%, or a decline of over 33% over the<sup>study period. The rate of decrease was</sup> highest in the wheat-rice system. Available phosphorus has<sup>also decreased in all systems at about</sup> the same rate. Total soluble salts increased significantly<sup>in all the systems, while an increase in</sup> pH occurred in two of the four systems.

Similarly, the data confirm the deterioration in tubewell<sup>water quality, reflected in a significant</sup> increase in residual carbonate and electroconductivity<sup>of tubewell water in all production</sup> systems. Residual carbonate almost doubled during<sup>the study period, reflecting the common</sup> observation that farmers are increasingly tapping poorer quality<sup>groundwater.</sup> 11
Table 3. Average values of selected water and soil quality parameters in Punjab _By period and region, 1971-94_

||||_Soil_|||_Wat_|_er_|
|---|---|---|---|---|---|---|---|
|_System_|_Period'_|_Organic_<br>_matter_<br>_(°te)_|_Available_<br>_phosphorus_<br>_(ppm)_|_Soil pH_|_Soluble_<br>_salts_<br>_(No)_|_Residual_<br>_carbonate_<br>_(me/J)_|_Electrocon_<br>_-ductivity_|
||1|0.85|5.94|8.01|0.28|1.35|848|
|Wheat-mixed|2|0.68|5.00|8.33|0.34|1.83|943|
||3|0.57|5.01|8.39|0.45|2.66|1172|
||1|1.02|7.42|7.74|0.12|1.20|743|
|Wheat-rice|2|0.72|6.23|8.26|0.33|1.72|800|
||3|0.59|4.60|8.50|0.42|2.78|874|
||1|0.86|5.94|8.30|0.20|2.16|1010|
|Wheat-cotton|2|0.62|5.00|8.63|0.44|2.41|1099|
||3|0.54|5.01|8.41|0.41|3.03|1214|
||1|0.90|7.53|8.08|0.27|1.18|939|
|Wheat-mungbean|2|0.62|5.17|8.61|0.42|1.37|1006|
||3|0.53|4.83|8.50|0.38|2.00|1141|
||1|0.89|7.04|8.06|0.22|1.54|896|
|Punjab|2|0.66|5.46|8.46|0.39|1.90|993|
||3|0.56|4.83|8.44|0.42|2.67|1123|

= I = Green Revolution 1966-74 (In this table, however, the mean is only for 1971-74 as data for earlier years are not available) , 2 Intensification 1975-84, 3 = Post Green Revolution, 1985-94 _Source:_ Authors' own calculation 12
Figure la.

Figure 1a: Indlces of trends In soil quality <!-- Start of picture text --> 300<br>250 -4 Avall.P<br>-4Soil pH1<br>g200 A - -- Organic matter<br>-<br>\, Soluble salt<br>XC100 *s -_<br>0 1<br>Al A3 <k A% q %lb %lb bA %q 5 9¢b<br>Year<br><!-- End of picture text --> _Source: Authors' own calculation_ Figure lb.

Figure lb: Indices of trends in tubewell water quality <!-- Start of picture text --> 250<br>150<br>-100<br>x<br>xl 50<br>O-~~~ . ... .. .. .. .. .. .. ...<br>+ + < 4 <9~~t 0N otb b %A0 9 f<br>Year<br><!-- End of picture text --> _Source: Authors' own calculation_ 13
# _Trends in productivity_

Table 4 summarizes various measures of productivity. Both land and labor productivity have increased at about 2.5% annually. Land productivity has risen more slowly since the boost during the Green Revolution period, while labor productivity has generally increased in successive periods due to rapid mechanization. An important result is the overall decline in water productivity, which reflects inefficient use of irrigation water in part due to subsidies on canal water prices and fixed rates on electricity used for tubewells (Faruqee, 1995).

The overall growth in TFP for the crop sector was 1.26% per annum. Contrary to most views, TFP increased little in the Green Revolution period due to high investment costs, especially in tubewells, and was most rapid in the post-Green Revolution period." There have also been sharp differences by system with the highest rate of growth in TFP in the wheat-cotton and wheat-mungbean systems (Figure 2). Both systems experienced rapid growth in the post-Green Revolution period due to successful technological innovations (especially new cotton and mungbean varieties).

> Similar results have been observed in a recent study of the Indian Punjab (Murgai, 1999). The reason for this decline may be high investment on tubewells which enters TFP as a capital flow cost. As most of tubewells are not utilized to their full capacity, the cost does not fully reflect their contribution in productivity. In particular, installation of tubewells is probably a risk reducing strategy, due to uncertain water supply from the canal system.

14
**Table 4. Growth rate of partial factor**<sup>**productivity, input, output, and TFP indices**</sup>

|_By production system, percent per year_|
|---|

|_System_|_Period'_|_Part_|_nid productiv_|_ity_||_Crop_|||_Livestock_||**_Overal_**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||||||_TFP_|**_TFP_**|
|||_water_|_lond_|_labor_|_output_|_input_|_TFP_|_output_|_input_|<br>||
||AU|-1.90|2.09||2.29|1.42|0.87|2.79|2.01<br>|0.78<br>|1.00<br>|
||1|-3.08|2.54|-5.09|3.35<br>|4.46<br>|**-1.12ns**<br>|2.77<br>|3.57<br>|-0.80"S<br>|-0.90"<br> <br>|
|Wheat-**mlixed**|2|-1.50|3.51|1.67|2.25|0.75|1.50<br>|1.47<br>|1.46<br>|**O.01ns**<br>|<br>1.10<br>|
||3|-0.90|0.02|2.66|1.87|1.40|0.46**ns**|4.80|2.66<br>|2.14<br>|1.28<br>|
||All|-3.03|0.89|1.03|1.79|2.30|_-0.50_|2.70<br>|2.00<br>|0.70<br>|0.11<br>|
||1|-7.31|0.76|-2.11|3.44|5.88|-2.43|2.86|3.59<br>|-0.72m<br>|-1.77<br>|
|**Wheat-rnce**|2|-3.65|0.88|0.80|1.24|1.84|-0.60|1.19<br>|1.07<br>|0.12M<br>|**-0.37ns**<br>|
||3|0.14|1.85|3.01|2.04|1.17|0.88|5.17|3.04|2.13<br>|1.87<br>|
||AlH|-0.25|2.98|3.44|3.65|2.08|1.57|3.75|1.92|1.82<br>|1.94<br>|
||1|-4.99|3.18|-1.81<br>|3.66<br>|3.96<br>|-0.30<br>|3.10<br>|1.72<br>|1.38<br>|0.09w<br>|
|**Wheat-cotton**|2|0.10|2.32|3.48|3.55|1.77|1.79|3.06|2.20<br>|0.87<br>|1.87<br>|
||3|1.96|2.90|4.30|2.70|0.92|1.77|5.36<br>|2.55<br>|2.81<br>|2.32<br>|
||All|-3.49|1.89|3.08|3.68|2.36|1.32|4.40|2.46|1.93<br>|1.98<br>|
|Wheat-|1|-7.85|4.36|0.95|6.79|4.55<br>|2.24<br>|1.50<br>|3.08<br>|-1.59<br>|1.61<br>|
|mungbean|2|-2.81|-1.26|0.70|1.31ns|2.02|-0.70|4.73|3.08<br>|1.64<br>|0.46m<br>|
||3|-0.48|3.68|7.70|4.80|1.56|3.24|5.46|1.66<br>|3.80<br>|4.03<br>|
||All|-1.41|2.43|2.51|3.23|1.97|1.26|3.30|2.05|1.25|1.51<br>|
||1|-5.14|2.75|-2.85|4.00|4.49|-0.49Is<br>|2.75<br>|2.82<br>|**_0.07ns**<br>|<br>**-0.17n"**<br>|
|**All Punjab**|2|-1.29|2.22|2.33|2.77|1.50|1.27<br>|2.39<br>|1.92<br>|0.47ns<br>|1.21<br>|
||3|0.61|1.96|4.14|2.85|1.25|1.60|5.15|2.53|2.62|2.25|

Note: All **the** coefficients, except those<sup>bearing" sign, are statistically significant at least at the</sup><sup>**10%**level</sup>

**I =** Green Revolution 1966-74.2 **= Intensification** 1975-84, **3** = **Post-Green Revolution,** 1985-94;<sup>**ALL =**1966-94.</sup>

15
**Figure** _2._

**Figure 2: Trends In TFP by Production System**

<!-- Start of picture text --> 200 - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>-4-wheat-mix<br>-- 4--wheat-cotton<br>-O*-wheat-mungbean<br>150 _--- -_------------ ------ --- ----- ------ wet-e<br>bAfq' 1Xtq 100 i <-<br>Year<br><!-- End of picture text --> _Source: Authors' own calculation_ By contrast, the wheat-rice system experienced a significant negative growth in TFP over the study period. These results confirm widespread concerns that continuous double cropping of cereals, especially rice and wheat which require very different soil and water management practices, is an unsustainable cropping pattern (Hobbs and Morris, 1996). Deterioration in soil and water quality, discussed earlier, seems to be especially serious in this system.

Growth in TFP for livestock was similar to the crop sector (1.25% per annum) but almost all of this productivity growth occurred in the post-Green Revolution period. There were no significant technological innovations in livestock but improved fodder supply, substitution of milk animals for draft animals, and the one-time slaughter of draft animals, explain this jump in TFP. However, these sources of livestock productivity may not be available in the future. Combined crop and livestock TFP grew at 1.51% per annum, higher than for the crop or livestock sector alone.

# **Decomposition of Productivity Growth**

As concern related to resource productivity is usually directed to the crop sector, which is directly affected by soil and water quality, we estimated the cost function for the crop sector only. One problem with multi-output, multi-input cost functions is that, even for a few outputs 16
and inputs, the number of parameters to be estimated is large (Ray, 1982). This<sup>problem is</sup> greater when many soil and water quality parameters and their interaction with input price, output level, and technology variables are also included in the function. However,<sup>this is less a</sup> problem in our case because of the large number of data points available from combining<sup>time-</sup> series and cross-sectional data and by estimating the cost function, and factor- and<sup>revenue-share</sup> equations simultaneously. One output (Divisia Index of all 33 crop outputs)<sup>and six inputs</sup> (chemicals, water, labor, land, machinery, and animal/bullock cost) were included<sup>in the function</sup> for the crop sector. In the cost function, as in any other dual function, input prices<sup>rather than</sup> physical quantities were used.

Aggregation bias can occur with group (district-level) data as used in this<sup>study. However, use of</sup> district and regional trends, infrastructure, and resource quality variables can<sup>account for</sup> differences in the cost structure across districts. Tests for heteroskedasticity<sup>and first order</sup> autocorrelation were conducted using Langrange multiplier tests (Jarque and Bera, 1980),<sup>and</sup> the presence of these effects was rejected at the 10% significance level.

The definition of the variables in the cost function are given in box 1. The effect of technological change was imperfectly proxied by two variables (i) the proportion of area sown<sup>to modem</sup> wheat varieties, and (ii) cropping intensity. Cropping intensity is a proxy for adoption<sup>of MVs in</sup> summer crops (for which varietal adoption data were not available), since one<sup>of the major</sup> impacts of MVs of cotton and rice was to shorten the growing period, and thus to resolve<sup>the</sup> conflict between harvesting of the summer crop and planting of the following wheat<sup>crop</sup> (Byerlee et al., 1987.; Sharif et al., 1992; Ali et al., 1997). The effect of resource degradation<sup>was</sup> estimated through the soil and water quality variables described above. The adult<sup>literacy rate</sup> was used to capture the effect of changes in labor quality. The inverse of the distance of a village from the nearest metal road was used to quantify the effect of improvement in<sup>physical</sup> infrastructure. Region-specific time-trend dummies were included to capture the<sup>remaining</sup> unspecified effects of technological change, resource degradation, or change<sup>in resource</sup> productivity not included in the function.

The cost function in equation (2) along with the factor- and revenue-shares equations in<sup>(4) and</sup> _(5)_ and cross equation restrictions in (3) were simultaneously estimated using the seemingly unrelated regression (SUR) procedure. In view of the adding-up requirement<sup>of the input share,</sup> one equation (for draft animal) was deleted. The interactions of one input price<sup>with all other</sup> prices and resource stock variables, as a group, were tested. Similarly, interactions of output<sup>with</sup> all input prices and resource stock variables as a group were tested. The trend<sup>variables for input</sup> and output prices were also validated. All these interactions as a group, except<sup>those of water</sup> with resource quality variables which were subsequently excluded, were significant at least<sup>at the</sup> 10% level. The results of the cost function estimates are reported in the Appendix.

The production elasticities, estimated at the mean level of prices and output and resource<sup>stock in</sup> the middle of the study period, are positive for all inputs, except for machinery.<sup>The respective</sup> elasticities for fertilizer, water, labor, land, machinery, and draft power are 0.03,<sup>0.17, 0.61, 0.58,</sup> -0.15, and 0.07. The negative elasticity for machinery can be explained by the lumpy<sup>nature of</sup> 17
investment in tractors, introduced at a subsidized rate in the country. The elasticities of all inputs except labor are increasing as indicated by positive trend coefficients. The return to scale (the inverse of the output elasticity) is 1.10 and increasing.

The elasticities of soil quality parameters were estimated at the regional mean values of input prices, output level, and technology variables. They are as expected. For example, organic matter and phosphorus have negative elasticities in all the systems, while total soluble salts, an indication of salinity, has a positive sign in the wheat-rice and wheat-mixed systems, confirming results of a recent farm-level study, that soil salinity is an important constraint on crop yields in northern Punjab (Siddiq, 1994). Of the two water-quality variables only electroconductivity produced a consistent and significant coefficient, indicating that soil salinity is in part due to the application of poor quality tubewell water.'<sup>2</sup>

> 12 The residual sodium carbonate of tubewell water was found to be highly correlated with electroconductivity and was deleted from the final equation.

18
# **Box 1. Variable definitions used in the translog cost function**<sup>**for Punjab agriculture, 1971-94**</sup>

# **_Independent variables_**

- _q=_ output = Crop output measured in divisia index number of<sup>production [1965=100]</sup>

- WI fertilizer =Divisia index of N, P, and K prices of nutrient<sup>and spray prices</sup>

- **w2** = water = Index of tubewell water price

- _w 3_ =labor = Index of wage rate of labor

- _w 4_ =land = Divisia index of annual rent of irrigated<sup>and unirrigated land</sup>

- **_w5_** = machine = Index of tractor prices

- w6 = animal = Index of feed, fodder, interest, and miscellaneous<sup>animal costs per standard animal unit</sup>

- S 1 = soil organic matter = Index of organic matter content in the<sup>soils (average of all the observations in a</sup> district)

- _S2_ = soil phosphorus = Index of available phosphorus in<sup>the soils (average of all the observations in a district)</sup> **_S3_** = salts in soil = Index of total soluble salts in the soils<sup>(average of all the observations in a district)</sup>

- S4 _=_ electroconductivity = Index of electroconductivity of water<sup>(average of all the observations in a district)</sup>

- II= modem variety = Index of proportion of the<sup>total wheat area under modem wheat varieties</sup>

- **I2** = cropping intensity = Index of cropping intensity defined<sup>as total cropped area divided by net sown area</sup> = education = Index of percentage of farmers who can read and write

- = road = Index of the inverse of the average distance of a village<sup>from a road.</sup>

- _T_ = trend at the cropping system level = Regional time<sup>trend variable. Its value for a specific region was equal</sup> to trend (0,1,2,...for 1965,1966,1967....), and zero otherwise

- D =district dummy = Set of district dummy variables<sup>having a value of one for a district, and zero otherwise.</sup>

# **_Dependent variables_**

**Si** = fertilizer factor share = is (total cost of all types of<sup>fertilizer nutrients and spray cost)+(total farm</sup> production expenditure)

- **s2** = water factor share = (total cost of tubewell operating<sup>cost and canal water cost)+(total farm production</sup> expenditure)

- **S 3** = labor factor share = (total cost of labor including family<sup>labor evaluated at the unskill wage rate)+(total</sup> farm production expenditure)

**_S4_** = land factor share = (rent of irrigated and unirrigated land)+(total<sup>farm production expenditure)</sup>

- _S5_ = machine factor share = (total operating cost of tractor,<sup>harvester, threshing, and bullock)+(total farm</sup> production expenditure)

**S 6** = animal factor share = (interest and miscellaneous animal<sup>cost)+(total farm production expenditure)</sup>

- _y,_ = revenue share = (marketing revenue from all crops)+(total<sup>farm production expenditure)</sup>

- C = total cost = Index of farm production expenses (1965=100),<sup>which includes total chemicals, water, labor,</sup> land, machinery, and draft power.

19
The technological variables had the expected effects of lowering costs with the greatest effect for cropping intensity, reflecting success in developing short-duration modem varieties . Finally, the quality of human and physical resources plays a critical role in improving agriculture productivity, especially in a dynamic agriculture, like that of the irrigated Punjab. A 1% increase in the human and physical infrastructure variables is estimated to increase crop sector productivity by 0.01% and 0.07%, respectively, confirming other evidence of high returns to these investments in post-Green Revolution agriculture (e.g., Hussain and Byerlee, 1995; Ali and Flinn, 1989).

Using coefficients of the cost function and formulation in (6), productivity growth was decomposed into three effects: i) technological change, ii) degradation of soil and water quality, and iii) improvement in human and physical infrastructure (table 5), all evaluated at the systemspecific mean rate for each variable included in the cost function.'<sup>3</sup> Technological changes and improvements in human and physical infrastructure combined produced a growth of 0.94% per annum with each accounting for about half the total. Resource degradation in aggregate lowered growth by 0.53% per annum. The combined effect of technological change, resource degradation, and improvement in human and physical infrastructure was negative in the wheatrice system (overall increase in unit cost). The contribution of technological change was highest in the wheat-cotton and wheat-mungbean systems.

Soil and water degradation reduced productivity in all regions, highlighting the role of natural resource variables on productivity. In the wheat-rice system, resource degradation more than cancelled the effects of technological change, and improvements in education and infrastructure. The unspecified "other factors" captured by coefficients on the regional time-trend variable also reduced productivity quite strongly in all but the wheat-cotton system. These environmental and management-related factors may include development of insect-pest complex (e.g., increased intensity of _Phalaris minor_ in the rice-wheat system) due to inappropriate use of pesticides and monocropping of cereals, depletion of water aquifers, and development of a hard plow pan due to inappropriate mechanization (Byerlee, 1992; Hobbs and Morris, 1996). More research is needed to identify the management practices causing such a decline. As massive public investment to control water logging and salinity is not included in the cost function which relates to private costs and returns only, the effect of these other factors is probably under estimated.

> 3 Total annual productivity growth estimated through the econometric analysis is 0.41% for 1971-94, lower than the 1.30% estimated through the index number approach for the corresponding period , but with the same ranking by production system. The difference in productivity growth obtained using index number or econometric approaches may be due to many reasons; (i) the TFP growth rate (primal) is computed with input levels held constant, whereas the cost function rate (dual) is computed with input level adjusted optimally to technological change (Antle and McGuckin, 1995, p. 182), (ii) the productivity measure obtained from the cost function is net of factor substitution, while the index number approach includes the substitution effect (Ray, 1982, p. 496), (iii) TFP growth in the index number includes changes in resource quality and infrastructure, whereas the econometric approach has controlled for these variables, and (iv) not all the variables related to technological change could be included in the cost function which might have under estimated technological progress

20
**Table 5. Decomposition of the total change in the**<sup>**crop sector productivity in Punjab during 1971-94**</sup>

_percent per year_

||||_System_<br>|||
|---|---|---|---|---|---|
||**_Wheat-_**<br>**_mixed_**|**_Wheat-_**<br>**_cotton_**|**_Wheat-_**<br>**_rice_**|**_Wheat-_**<br>**_mungbean_**|**_OveraU_**<br>**_Punjab_**|
|_Soil and water quality deterioration_||||||
|Water electroconductivity|-0.0241|0.0033|-0.0109|0.0013|-0.0073|
|Soil phosphorous|-0.0268|-0.0642|-0.0868|-0.0043|-0.0487|
|Soil organic matter|-0.1547|-0.109|-0.2123|-0.0655|-0.1374|
|Total soil soluble salts|-0.0567|0.0009|-0.0583|0.0001|-0.0265|
|Other factorsa|-0.3060|0.4670|-0.9250|-0.5570|-0.3140|
|Total|-0.5682|0.2982|-1.2929|-0.6257|-0.5343|
|_Technological Change_||||||
|CI &MVb|0.3499|0.7268|0.3394|0.5271|0.4970|
|_Public investment_||||||
|Roads and literacy|0.4176|0.4984|0.3714|0.4643|0.4434|
|Net effects|0.1992|1.5234|-0.5821|0.3658|0.4061|

Note: The rate of productivity change in each production<sup>system was evaluated by multiplying the negative of the coefficient with the</sup> system-level rate of per annum change (in percentage)<sup>in each factor.</sup> Based on the system-specific trend coefficients evaluated<sup>at regional mean values of the other variables used in the cost function .</sup>

**bCI** = cropping intensity, MV = percent area in modem<sup>varieties of wheat.</sup>

_Source: Authors' own calculation_ The deterioration in agro-ecosystem health,<sup>depicted by the declining trend in the resource stock</sup> variables discussed earlier, is in itself a cause for concern.<sup>This is more so when at least some of</sup> this deterioration seems to relate to intensification,<sup>especially increased use of fertilizer and</sup> water (table 6).14 We also estimated the long-term<sup>negative effect of modern inputs on</sup> productivity through their effect on degradation of the<sup>resource stock. On average, a 1% increase</sup> in tubewell water and fertilizer reduced productivity<sup>over the period by 0.011 and 0.021%</sup> through their effects on the quality of the resource<sup>stock, offsetting some of their positive</sup> production effects.

> 14 Some of these effects may be indirect. For example,<sup>use of fertilizer and water may deplete soil phosphorous and</sup> organic matter by encouraging higher cropping intensity.

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**Table 6. Effect of modern technology inputs on productivity through depletion of the resource stock**

|**_Resource qualitypararneter_**<br>**_Resouce_**<br>**_qalityparaeterelasti_**|**_Production_**<br>**_city_**<br>**_(I%)'_**|_Effect of 1%_<br>**_technologyinpu_**<br>**_stc_**|**_increase in_**<br>**_tsonresource_**<br>**_%2()3_**|_Effect of_**_1% increase in_**<br>**_technologyinputsonproductivity_**|
|---|---|---|---|---|
|||**_Water_**|**_Fertilizer_**|**_Water_**<br>**_Fertilizer_**|
|Available<br>soil<br>organic<br>matter|<br>0.060|-0.053|-0.265|-0.003<br>-0.016|
|Available soil phosphorus|0.024|-0.159|-0.136|-0.004<br>-0.003|
|Total soluble salts in soils<br>|-0.015|0.224|0.103|-0.003<br>-0.002|
|Electroconductivity<br>|-0.006|0.077|0.095|-0.000<br>-0.001|
|**Total**||||**-0.011**<br>**-0.021**|

t The production elasticities of resource stock variables _(alnPlalnS,_ where P represent productivity, and S a stock variable) were obtained from the cost function coefficients by evaluating them at the mean values of all other variables involved, and then inverting their signs, i.e. _alnPIalnS=-alnC1lnS_ 2The percentage effect of a technology variables on a resource stock variable (alnSIalnl, where I represents a technology variable) was obtained by regressing the logarithm of each resource stock variables on the logarithm of technology inputs (i.e., fertilizer and water).

> 3The effect of technology variables on productivity _(dlnPd61nl)_ was estimated by multiplying the production elasticity of a resource stock variable with the effect of technology variable on the resource stock, i.e. _(6InPlo/nf) = (alnPlalnS)* (alnSIalnl)._

_Source: Authors' own calculation_

# **Conclusions**

Although results achieved in Pakistan with Green Revolution technologies are impressive, the results of this study raise important questions about the sustainability of those gains, especially in light of growing evidence of degradation of land and water resources. In this study, the assembly and analysis of a very comprehensive data set on both crop and livestock production, as well as a number of soil and water-quality variables, provided the opportunity to understand some of the underlying trends in major production systems of Pakistan's irrigated Punjab.

On the surface, overall output growth in the sector of over 3% annually for nearly three decades, and TFP growth of 1.26% per year, suggest a fairly dynamic sector backed by significant technical change. However, growth in land productivity has slowed since the Green Revolution while labor productivity has jumped with acceleration of tractorization so that much of the recent growth in TFP is due to adoption of labor-saving technologies. A closer examination also shows considerable variation in productivity growth by production system. Crop sector TFP growth was relatively high in two production systems (wheat-cotton and wheat mungbean), modest in one system (wheat-mixed), and negative in the wheat-rice system.

TFP is a composite measure of the effects of changes in technology, resource quality, and improvements in human and physical infrastructure. As such a positive trend in TFP as observed 22
in most production systems in this paper,<sup>is not a good measure of long-term sustainability. The</sup> one system (wheat-rice) with negative<sup>TFP growth almost certainly suggests problems of</sup> sustainability. The data assembled<sup>for this paper suggests considerable evidence of degradation</sup> in soil and water quality throughout<sup>the province and there is some evidence that part of this</sup> depletion is related to the use of<sup>inputs considered to be important ingredients of growth in</sup> modern agriculture. The estimation<sup>of a cost function across districts and over time including</sup> several variables for resource quality<sup>and variables for technical change suggests that resource</sup> degradation had important negative<sup>effects on productivity in all systems, and especially in the</sup> wheat-rice system. On average, this<sup>deterioration in resource quality lowered annual productivity</sup> growth by 0.53% in the province. Other<sup>unmeasured factors, such as development of insect-pest</sup> complexes, were also responsible for<sup>a further reduction in resource productivity. Externalities,</sup> such as the effects of pesticide<sup>use on human health in the wheat-cotton system could not be</sup> analyzed in this study but may also<sup>be important. Thus TFP growth would have been much</sup> higher in the absence of resource<sup>degradation.</sup> These results combined with the stagnation<sup>of output in recent years, reflected in large-scale</sup> imports of wheat, underline growing<sup>concerns about degradation in Pakistan's most valuable</sup> asset--its irrigated land base. Resource degradation<sup>in itself is not a reason for policy intervention</sup> if it is internalized into producer decision<sup>making. However, in this case, there are several</sup> reasons to believe that this is not the<sup>case. First, some of the problem arises from distorted</sup> policies that lead to divergence in private<sup>and social costs. In particular, several of the modern</sup> inputs analyzed in this study have been<sup>subsidized for much of the period of analysis. Even now</sup> electricity for tubewell operation is<sup>priced at a fixed annual rate leading to overuse of poor</sup> quality tubewell water which is a<sup>major contributor to soil salinity (Siddiq, 1994). Second, the</sup> information base on which farmers<sup>make decisions is inadequate with respect to internalizing</sup> rapid changes in soil and water quality<sup>variables by moving to more sustainable practices such as</sup> integrated nutrient and pest management,<sup>more diversified crop rotations, and incorporation of</sup> legumes into the system. Third,<sup>public sector research has undoubtedly been biased toward</sup> development of technologies based on<sup>packages of modern inputs, and has neglected research on</sup> public goods such as integrated crop<sup>management and crops such as legumes that enhance</sup> diversification and sustainability of<sup>production systems. Indeed, until recently, very little</sup> research addressed efficient use of<sup>inputs, and the balancing of external input use and internal</sup> sources of nutrients. Thus from a policy<sup>perspective, there is a case for public and private</sup> initiative on several fronts--increased<sup>investment in resource management research and</sup> extension, research to develop diversified<sup>and more sustainable cropping patterns and rotations,</sup> removal of price distortions on key<sup>inputs, especially water, and special incentives to invest in</sup> inputs such as gypsum that can counteract<sup>the problem of poor quality tubewell water. Although,</sup> this paper contends that such policy interventions<sup>may be rewarding if they can reverse the trend</sup> in resource degradation, costs of such<sup>interventions have to be considered against potential</sup> benefits, before making definite policy prescriptions.

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