Risk sources

5.5.1.1. Wheat-cotton farmers

Applying exploratory factor analysis on the 15 expectation values of the relevant RS for wheat-cotton farms resulted in four factors with a total variance explained of 65.53%, which is considered as satisfactory in social sciences (Hair et al. 2010). The KMO value was 0.713, and the Cronbach’s alpha values for factors ranged from 0.61 to 0.79.

Two RS (Land tenure fragmentation by inheritance and loss by land reform ownership), which are conditional on farm ownership type, were not included in the factor analysis, because they have many missing values.

The factors are described in Table 5.7. Based on the concentration of factor loadings, the four interpretable and feasible factors can be labelled as follows:

Factor 1 was named ‘agriculture shrinkage’ because of the relatively high loadings of RS variables that influence the besetting threats affecting wheat-cotton cultivation in Syria. It involves high loadings associated with a decrease of farm business effectiveness, decline of cultivation preference in the region, insolvency due to drying and decline of annual average rainfall. Factor 1 explains about 21% of the total variation in the observed risk perceptions across the farmers in the sample.

Factor 2 incorporates a number of RSs related to the ‘subsidy policy’ for strategic crops’ inputs and outputs. It includes high loadings for elimination of governmental support as well as the special compensation payment program. The high loading for the decrease of cotton and wheat prices which are considered as state supported prices can be noticed on this factor. Factor 2 explains almost 18% of the total variance.

Factor 3 was labelled ‘cotton related policy’ with high loadings for three political aspects: Cotton license rules, irrigation modernization policy, and fuel price. Cotton cultivation operations seem to be more affected these risks than wheat cultivation. Cotton operators were actually more worried about decline of cotton licences. Furthermore, cotton irrigation is more affected by fuel price rising than wheat, since cotton requires about 15 irrigation operations during its growing season compared with about 5 for wheat, which means more fuel consumption to run irrigation pumps. The forenamed high water requirements for cotton mean that cotton cultivation is more targeted by irrigation modernization rules than wheat.

Factor 4 refers to ‘input prices’. It includes high loadings of other operating input prices, and brokers’ dominance. Risks related to operating input costs are unprecedented aspects in wheat-cotton farms. These related risks occurred recently due to lifting of input subsidies for strategic crops which to an increase in brokers’ dominance. The last two factors interpret nearly 14 and 12% of the total variation respectively.

Table 5.7: Varimax rotated factor loadings of relevant risk sources for Syrian wheat-cotton farmers, (n=103)

Relevant risk sources Factors ^a

1 2 3 4

Precipitation shortage 0.51 -0.36 -0.09 0.20

Drying of rivers and underground water 0.70 0.48 -0.07 -0.08

Irrigation modernization policy 0.17 0.06 0.73 0.04

Governmental support elimination 0.34 0.67 0.09 0.08

Special compensation program elimination 0.12 0.81 0.08 -0.12

Loss by land reform ownership ^b ---- ---- ---- ----

Cotton license -0.10 0.10 0.78 0.18

Eigenvalues 3.70 2.29 1.33 1.20

Per cent of total variance explained 20.96 17.79 14.33 12.46

Cumulative per cent of the variance explained 20.95 38.75 53.07 65.53

Cronbach’s alpha 0.79 0.76 0.61 0.66

Number of variables 5 4 3 2

a Factors 1 to 4 are agriculture shrinkage, subsidy policy, cotton related policy and input prices respectively. Factor loadings

> |0.40| are in bold

b Risk sources conditional on farm ownership type Source: Survey data

5.5.1.2. Pistachio farmers

The number of variables of expectation values for the relevant pistachio RS data was reduced from 14 to 5 by applying the exploratory factor analysis (Table 5.8). Five factors explain 74% of the total variance. The KMO value was 0.603, and with regard to the reliability test Cronbach’s alpha values for resultant factors range from 0.61 to 0.73. Factors of pistachio operators were extremely different from those of wheat-cotton.

Referring to the results presented in Table 5.8, the five factors can be explained as follows:

Factor 1 was related to ‘production’ because of the high loadings of risks that affect directly the pistachio productivity. These risks are represented by the precipitation shortage, drying, plant pests and diseases, and insufficiency of agricultural extension system in the target region.

Factor 2 can be described as ‘farm business environment’ due to the high loadings associated with rainfall shortage accompanied by other climate factors such as frost, overheating, moisture fluctuation, etc. Furthermore, high loading for pistachio price decrease can be noticed in this factor. Given that pistachio trees are fairly resistant to drought, trees’

yield is affected by other unfavourable climate factors more than the precipitation shortage, particularly when such factors coincide with flowering stage. Losses caused by the affected yield are exacerbated when they are combined with low market prices due to increasing supply of Turkish and Iranian pistachio in Syrian markets. Indeed, high loading of theft of farm equipment was noticed on this factor, indicating a bad situation grips the general farm environment.

Factor 3 is strongly associated with ‘market risks’ and involves large loadings of pistachio price’ decrease and variability, brokers’ dominance of inputs and outputs, and competition from neighboring countries. Each of the three previous factors interprets about 16% of the total variation.

Factor 4 is called ‘input prices’ because of the highest factor loading of the fuel price, and other operating input prices on this factor.

Factor 5 reflects ‘pistachio expansibility’. It includes risks that constrict farmers’

willing to horizontally expand their pistachio farm business. High loadings resulted from increasing farm land price and prohibition of additional pistachio farm licence. Close to 13%

of the total variation can be explained by each of the two last factors.

Table 5.8: Varimax rotated factor loadings of relevant risk sources for Syrian pistachio Other climate factors (frost, overheating, dust storm) 0.37 0.83 -0.19 0.02 -0.04

Plant pests and diseases 0.83 0.18 0.18 0.15 -0.05

Competition from neighbour countries 0.12 -0.17 0.78 0.07 0.09 Agricultural extension’ insufficiency 0.43 0.32 0.31 -0.17 0.51

Pistachio license -0.05 0.21 0.06 0.03 0.83

Eigenvalues 3.59 2.06 1.86 1.60 1.25

Per cent of total variance explained 16.38 16.13 15.97 12.90 12.58 Cumulative per cent of the variance explained 16.38 32.50 48.47 61.37 73.95

Cronbach’s alpha 0.70 0.69 0.73 0.67 0.61

Number of variables 4 4 4 3 2

a Factors 1 to 5 are production, farm business environment, market, input prices and pistachio expansibility respectively Factor loadings > |0.40| are in bold

Source: Survey data

5.5.2. Risk management strategies