An Econometric Analysis of Agricultural Production, Focusing on the Shadow Price of Groundwater: Policies Towards Socio-Economic Sustainability

Abstract

The focus of this chapter is on the agricultural sector in the Asopos catchment as it has a significant impact on the status of water in the area. In particular, the aim of the chapter is to estimate the farmers’ valuation of groundwater’s shadow price for the region of Asopos. In order to achieve that, an agricultural micro-economic data-set from the catchment has been collected through the use of a detailed agricultural questionnaire. As it will be explained in the chapter, the questionnaire focuses on collecting information regarding cultivations, production structures and use of groundwater for irrigation. The objective of the micro-econometric analysis is to uncover patterns of groundwater use and farm efficiency. The chapter presents the derived estimates that make possible the analysis of the impact of different economic policies, – which will be used for the implementation of an optimal, sustainable and integrated water policy – on farmers’ profits and social welfare. The chapter finishes with policy recommendations based on the principle of socio-economic sustainability that assures both economic efficiency of farms and concludes with the estimation of groundwater for irrigation shadow price and how this can be used in the design of pumping taxes to reduce pollution and to increase farms efficiency.

Appendix

Type of crops grown in the Asopos region
Crop	Number of farms	Total area (acres)	Share of total area (%)	Total irrigated area (acres)	Share of total irrigated area (%)
Temporary crops
Barley	6	230	1.7	0	0.0
Beetroot	2	4	0.0	4	0.1
Carrot	1	15	0.1	15	0.3
Corn	1	20	0.1	0	0.0
Cotton	7	265	2.0	265	5.3
Crop	1	20	0.1	0	0.0
Cabbage	6	30	0.2	30	0.6
Oats	1	100	0.7	0	0.0
Okra	2	7	0.1	7	0.1
Onions	21	506	3.7	486	9.8
Potatoes	55	2,643	19.5	2,638	53.0
Tomatoes	1	200	1.5	200	4.0
Watermelon	1	6	0.0	6	0.1
Wheat	72	4,595	33.9	114	2.3
Beans	1	80	0.6	80	1.6
Cauliflower	1	5	0.0	5	0.1
Melon	1	70	0.5	70	1.4
Peas	1	10	0.1	10	0.2
Spinach	1	3	0.0	3	0.1
Permanent crops
Grapes	2	50	0.4	50	1.0
Olives	125	3,712	27.4	202	4.1
Organic olives	2	19	0.1	0	0.0
Pistachios	4	160	1.2	160	3.2
Vineyard	25	822	6.1	632	12.7
Total		13,572	100 %	4,977	100 %

Wheat, olives, and potatoes are the three major crops grown in the region. They represent respectively 34 %, 27 %, and 20 % of the total cultivated area. About half of the total irrigated area is planted with potatoes (53 %). Vineyard and onions represent 13 % and 10 % of the total irrigated area in the sample, respectively.

Use of irrigation for each type of crop
Crop	Number of farms	Total area (acres)	Total irrigated area (acres)	Share of the area which is irrigated (%)
Temporary crops
Barley	6	230	0	0
Beetroot	2	4	4	100
Carrot	1	15	15	100
Corn	1	20	0	0
Cotton	7	265	265	100
Cabbage	6	30	30	100
Oats	1	100	0	0
Okra	2	7	7	100
Onions	21	506	486	96
Potatoes	55	2,643	2,638	100
Tomatoes	1	200	200	100
Watermelon	1	6	6	100
Wheat	72	4,595	114	2
Beans	1	80	80	100
Cauliflower	1	5	5	100
Melon	1	70	70	100
Peas	1	10	10	100
Spinach	1	3	3	100
Permanent crops
Grapes	2	50	50	100
Olives	125	3,712	202	5
Organic olives	2	19	0	0
Pistachios	4	160	160	100
Vineyard	25	822	632	77
Total	340	13,572	4,977	37 %

Cereals (barley, corn, oats, wheat) are not irrigated in general. Only 5% of the area planted with olive trees is irrigated. Fields planted with cotton, fruits, and vegetables are fully irrigated. Overall, 37 % of the total area in the sample is irrigated. The three major products that are grown in Asopos are wheat, olives, and potatoes. We can see from this table that farmers do not combine wheat, olives, or potatoes with the growing of other products in most cases.

Crop	Farmers growing wheat also grow…	Farmers growing olives also grow…	Farmers growing potatoes also grow…
Barley	2	0	0
Beetroot	0	0	0
Carrot	0	0	0
Corn	0	0	1
Cotton	1	0	1
Crop	0	1	0
Cabbage	0	0	1
Oats	0	0	0
Okra	1	0	0
Onions	2	0	10
Potatoes	3	1	–
Tomatoes	0	0	0
Watermelon	0	0	0
Wheat	-	4	3
Beans	0	0	1
Cauliflower	0	0	0
Melon	0	0	1
Peas	0	0	0
Spinach	0	0	0
Grapes	0	0	0
Olives	4	–	1
Organic olives	0	0	0
Pistachios	0	0	0
Vineyard	0	1	2

The three major crops in the area are wheat, potatoes and olives, which we will consider in turn.

1.1 Wheat Producers

In what follows we consider the 59 farmers who grow only wheat (overall 72 farmers grow wheat in our sample). The following inputs are considered: fertilizers, pesticides and labor. Fertilizers and pesticides use are farmers’ statements while labor is calculated as follows: number of days of casual workers + number of permanent workers × 250. Some basic statistics are shown below. There are all on a per acre basis.

Variable	Obs.	Mean	Std. Dev.	Min	Max
Production (tonnes/acre)	59	0.27	0.19	0.02	0.80
Fertilizer use (kg/acre)	58	17.07	15.37	0.00	60.00
Pesticides use (kg/acre)	55	0.27	0.60	0.00	2.14
Labor use (days/acre)	59	0.15	0.27	0.00	1.50
Land (acre)	59	70.61	85.86	6.00	500.00

All these statistics are on a per acre basis so the figures should not vary too much from one farmer to the other. However we observe very large variations. For example, fertilizer use varies from 0 kg/acre to 60 kg/acre, with a mean of 17 kg/acre. The farmers stating 0 use of fertilizer, pesticides or labor probably did not want to answer or did not know. For these farmers, I have replaced 0 by the median value in the sample of farmers growing wheat only.

1.2 Statistics on Yield

1 Acre – US, = 0.4046873 ha, 1 hectare (ha), = 2.471044 acre (US)

Farmers in the sample produce on average 0.27 tonnes per acre, which corresponds to 0.67 tonnes per hectare (or 670 kg per ha). The average wheat yield in Greece is 1,900–3,000 kg/ha. The average yield on the sample thus seems a bit low. Once all variables are transformed in logs a Cobb Douglas production function is estimated. Because of the small sample size, it is not reasonable to estimate a Translog production function.

OLS estimation results – Cobb Douglas production function (59 obs)

	Coef.	Std. Err.	P>t
Fertilizer	0.026	0.093	0.778
Pesticides	0.204	0.119	0.092
Labor	0.140	0.080	0.087
Constant	−0.127	0.146	0.386

In this model the dependent variable is wheat yield. The explanatory factors are the three inputs measured in physical terms: fertilizer use per acre, pesticides use per acre, labor use per acre. The three estimated coefficients have the expected positive sign but only two are significant at the 10 % level. However, the model is not significant overall (p-value of the Fisher test is 0.1251). As a consequence the adjusted R-squared is also quite low: 0.0491.

1.3 Potatoes Producers

In what follows we consider the 34 farmers who grow only potatoes (overall 55 farmers grow potatoes in our sample). Some basic statistics are shown below.

Variable	Obs	Mean	Std. Dev.	Min	Max
Production (tonnes/acre)	34	2.04	1.13	0.20	5.00
Fertilizer use (kg/acre)	34	20.90	19.71	0.23	75.30
Pesticides use (kg/acre)	25	0.94	1.18	0.00	4.00
Labor use (days/acre)	34	0.80	1.28	0.00	6.50
Water use (m3/acre)	33	2.02	11.61	0.00	66.67
Land (acre)	34	52.15	37.60	7.00	150.00

Here too some figures are really surprising: fertilizer use varies from a low of 0.23 kg/acre to a high of 75.30 kg/acre. Again the zeroes for pesticides and labor do not make much sense.

1.4 Olive Producers

In what follows we consider the 117 farmers who grow only olives (overall 125 farmers grow olives in our sample). Some basic statistics are shown below.

Variable	Obs.	Mean	Std. Dev.	Min	Max
Production (tonnes/acre)	104	0.14	0.18	0.02	1.13
Fertilizer use (kg/acre)	112	20.42	18.55	0.00	80.00
Pesticides use (kg/acre)	107	0.19	0.84	0.00	6.00
Labor use (days/acre)	117	2.99	23.20	0.00	250.38
Land (acre)	117	29.91	23.84	5.00	120.00