Policy Incentives for Reducing Nitrate Leaching in Agricultural Lands: A Case Study of Irrigation and Drainage Dorudzan

Agricultural activities increasingly use water, fertilizers and pesticides, which may generate negative impacts on environment. Nowadays, nitrogen leaching from agricultural lands is a widespread global problem. Therefore, alternative land management practices such as nutrient management (rate, method and time of application), tillage operations (conservation and no-tillage), and irrigation management are routinely used to reduce non-point source pollution and improve water quality. In fact, a number of studies have illustrated the positive effects of best management practices (BMPs) on water and nutrient losses. The objective of this paper is to develop a bio-economic model and introducing the policy instrument for reducing nitrate from irrigation and drainage Dorudzan. We aim to identify ‘‘win–win’’ opportunities for improving farm profitability and reducing nitrate leaching.
An integrated biophysical-economic model was developed with five components. The first component is based on a process-based biophysical model (SWAT) that simulates the key processes of crop growth within the water and nitrogen cycles. The second component is a meta-model, which is used to link the biophysical model with the economic model. The meta-model employs regression techniques to replace the original simulation model and allow the application of simulation model results in an economic model of farm production. Crop yield is the key link between the two models. A quadratic function (Eq. 1), which allows estimation of the effect of increasing input levels and diminishing marginal returns, particularly when a wide range of inputs need to be incorporated, was used in this study.
(1)
Where, Y, W and N are crop yield, water and nitrogen fertilizer application, respectively.
The third component is a simple farm economic model, which is used to predict farmers’ response behavior under different policy scenarios, and to determine when and how much to irrigate and fertilize, and the farm gross margin that results (Eq. 2).
(2)
Where, GM, , , and C are the farm gross margin (rial/ha), water price (rial/m3), nitrogen fertilizer price (rial/kg), crop price (rial/kg) and other costs, respectively.
The fourth component is a nitrate leaching function which is a function of water and nitrogen fertilizer application (Eq. 3).
(3)
The fifth component is assessment of policy incentives. Regulators have at their disposal a portfolio of policy instruments (such as water pricing or tax on nitrogen fertilizer application) that can be used to influence nitrate leaching.
For running SWAT model, the data for estimating the wheat, barley, corn and rice yield production functions were generated by running the biophysical model hundreds of times with a different range of amount of water and nitrogen applications. The total number of scenarios of combinations of amounts of irrigation and nitrogen applications was about 96, 72, 88 and 84 for wheat, barley, corn and rice, respectively. Then, the yield production function was estimated with the multiple non-linear regression technique in the Eviews software. For model simulation, the economic model was formulated based on the crop production function obtained from the multiple non-linear regression analysis in the SWAT running stage and generate the farm gross margins. The effects of increasing water prices on nitrate leaching were simulated with the biophysical model.
The values of the coefficients from the multiple nonlinear regressions are shown in Table 1.
Table 1-Results from multiple non-linear regressions
Rice corn Barely Wheat Parameters
-6.011*** -11.335** -2.64*** 0.978***
(-6.252) (-2.075) (-4.073) (5.125)
0.0221** 0.047*** 0.0288*** 0.00098***
(8.17) (2.68) (7.00) (11.342)
-1.13*10-5*** -3.21*10-5*** -4.31*10-5*** -1.38*10-5***
(-7.726) (-2.478) (-6.657) (-12.985)
0.000158*** - 0.008*** 0.01***
(6.272) - (5.835) (31.407)
-3.23*10-6*** -4.88*10-6*** -3.24*10-5*** -1.22*10-5***
(-6.378) (-6.815) (-10.422) (-42.378)
- 9.15*10-6*** 2.19*10-5*** 5.89*10-6***
- (7.766) (5.563) (11.27)
0.99 0.98 0.955 0.994 R2
***,** significant in 1% and 5% , Numbers in parentheses ret-statistics
The results for water and nitrogen inputs, crop yield, farm gross margin and nitrate leaching under agronomic optimum, economic optimum and bio-economic optimum, also are shown in Table 2.
Table 2- Comparison between agronomic optimum, economic optimum and bio-economic optimum
Gross margin Nitrate leaching crop yield nitrogen fertilizer Irrigation* Scenario crop
(rial/ha) (kg/ha) (kg/ha) (kg/ha) (m3/ha)
26743460 56.81 5913.2 523 11721.5 Agronomic optimum wheat
26995120 49.83 5882.9 486.4 10727 economic optimum
26354880 37.99 5723.4 408.59 9824.5 bio-economic optimum
16314000 15.08 4203.5 262 10020 Agronomic optimum barely
16416560 14.71 4105.8 245.8 9637.75 economic optimum
16416560 14.71 4105.8 245.8 9637.75 bio-economic optimum
44180040 101.31 9009 750 20000 Agronomic optimum corn
44364700 89.57 8988 684 20000 economic optimum
40805580 37.9 8390 394 20000 bio-economic optimum
66876590 51.98 5020 244.58 24450 Agronomic optimum rice
67111950 42.43 5000 209.91 23918.14 economic optimum
67098590 38 4900 197.2 23918.14 bio-economic optimum
*irrigation efficiency 40%
The results show that there are win-win opportunities for improving farm profitability and reducing nitrate leaching by moving from current status to economic optimum. But from this point onwards, reduction in nitrate leaching is not achievable without profit penalties. In other word, to move from economic optimum to bio-economic optimum point, there is a trade-off relationship between farm profitability and groundwater quality protection. Also results of policy assessment showed that for wheat-water pricing and for corn and rice, because of high elasticity of yields of these crops to water application, tax on nitrogen application are cost effective policies.