III. An Empirical Field Study : Comparisons of Energy
3.4 Results of Energy Use
3.4.8 Driving Factors Determining the EE
If so, what is the driving factors determining the EE on energy inputs? The evaluation for this was conducted on two aspects - input categories and farming practices, by using the multiple regression analysis.
3.4.8.1 Input Categories Determining the EE
Regardless of farming systems, input category which can strongly influence the result of EE was explored by the multi-regression analysis.
<Table 3-33> Relations of Inputs Categories (GJ/ha) and EE Input Variables
(category) B t value p value
(Constant) 2.215 6.542 .000
Machinery .594 1.264 .212
Fuel -.156 -2.482 .016*
Electricity -.216 -.733 .467
Seeds .042 .167 .868
Compost &
Fertilizers -.038 -2.582 .013*
Natural & Chemical
Pesticides -.036 -.266 .791
Mulch
Plastic -.124 -3.928 .000**
Labor .921 2.737 .009**
R=0.641 R-square=0.411 Corrected R-square=0.318 Durbin-Watson=1.628 F=4.446 p=0.000**
-Dependent variable: EE, *p<.05 **p<.01
Significant driving factors of input categories for EE are presented in Table 3-33.
The strongest driving factor to determine the outcome of EE beyond farm systems is mulching film (p=0.000) and labor(p=0.009). fuels (p=0.016), and composts and fertilizers (p=0.013) were also significantly related to the results of EE at the level of 5%. These results mean that the differences of these energy inputs vary largely between farms than other energy inputs.
The more the mulching film, fertilizers and fuels were used, the poorer the EE was (minus t value). On the other hand, the more the labor used, the better the EE obtained, because the energy coefficients of labor is relatively much lower than that of other inputs.
<Table 3-34> Relations of Inputs Categories (GJ/ha) and EE in CFS Input Variables
(category) B t value p value
(Constant) 2.446 4.350 .000
Machinery .201 .224 .825
Fuel -.128 -1.007 .325
Electricity -.087 -.175 .863
Seeds .869 .805 .430
Compost &
Fertilizers -.056 -2.496 .021*
Natural & Chemical
Pesticides -.288 -1.471 .156
Mulch Plastic -.183 -2.296 .032*
Labor 1.305 2.539 .019*
R=0.722 R-square=0.522 Corrected R-square=0.340 Durbin-Watson=2.071 F=2.864 p=0.025*
-Dependent variable: EE, *p<.05 **p<.01
In addition, Tables 3-34 and 35 show the results when conventional and organic soybean production systems are separately analyzed from each other,
Table 3-34 shows the influential input variables to determine the outcome of conventional EE.
The result indicates that the massive commitment of fertilizers (p=0.021), mulching film (p=0.032) and labor (0.019) decreased the efficiency of conventional EE in soybean production. This means that the amounts of these input materials have big variations between conventional farms.
Hence, by regulating these inputs, conventional farms are able to effectively improve their EE.
On the other hand, the driving factors of EE in organic soybean farms are revealed with extraordinary results as shown in Table 3-35.
<Table 3-35> Relations of Inputs Categories (GJ/ha) and EE in OFS Input Variables
(category) B t value p value
(Constant) 1.558 4.932 .000
Machinery .757 1.983 .061
Fuel -.052 -.975 .341
Electricity -.130 -.392 .699
Seeds -.203 -1.190 .247
Compost &
Fertilizers -.038 -1.706 .103
Natural & Chemical
Pesticides -1.610 -.447 .659
Mulch
Plastic -.046 -1.573 .131
Labor -.174 -.465 .646
R=0.591 R-square=0.349 Corrected R-square=0.101 Durbin-Watson=2.834 F=1.407 p=0.251
-Dependent variable: EE, *p<.05 **p<.01
In organic soybean production, only machinery has a weak relationship with EE (p=0.061). No other inputs variable have relations with the outcome of EE in organic farms.
These results imply that in general organic farmers typically use the same kind and amount of inputs within the limits of allowances under the organic standards.
Therefore, to improve the efficiency of energy use in organic farms, it is suggested that the development of alternative materials for low energy input is rather better than changing amount of currently used inputs.
3.4.8.2. Farming Practices Determining the EE
Which farming practices have serious impacts on the results of EE in both systems?
<Table 3-36> Relations of Farming Practices (GJ/ha) and EE Input Variables
(practices) B t value p value
(Constant) 2.374 7.696 .000
Cultivation -.118 -2.335 .024*
Fertilizing -.023 -1.659 .104
Mulching -.102 -3.404 .001**
Seeding -.100 -.872 .388
Weeding .021 .159 .874
Spraying -.074 -.594 .555
Pruning 1.718 .862 .393
Irrigation -.080 -.172 .864
Harvesting .157 .890 .378
Threshing -.138 -2.066 .044*
Grading 2.572 3.002 .004**
R=0.666 R-square=0.444 Corrected R-square=0.317 Durbin-Watson=1.484 F=3.484 p=0.001*
-Dependent variable: EE, *p<.05 **p<.01
Table 3-36 shows the results of relationship with energy use for farming practices and EE regardless of farm management systems.
The result indicates that the mulching (p=0.001) and grading (p=0.004) practices have strong affected on the outcome of EE regardless of the farming systems. In addition, cultivation and threshing are significantly related to the EE at the level of 5%.
On the other hand, the results of the conventional and organic farming practices in relation to the EE are separately shown in Tables 37 and 38, respectively.
<Table 3-37> Relations of Farming Practices and EE in CFS Input Variables
(practices) B t value p value
(Constant) 3.521 7.557 .000
Cultivation -.181 -2.436 .025*
Fertilizing -.030 -1.631 .120
Mulching -.107 -1.626 .121
Seeding -.245 -.448 .659
Weeding .081 .195 .848
Spraying -.540 -1.933 .069
Pruning 1.197 .422 .678
Irrigation 5.689 1.496 .152
Harvesting .344 .514 .614
Threshing -.203 -.926 .367
Grading 3.066 2.626 .017*
R=0.799 R-square=0.639 Corrected R-square=0.418 Durbin-Watson=1.717 F=2.892 p=0.022*
-Dependent variable: EE, *p<.05 **p<.01
In CFS, cultivation (p=0.025) and grading (p=0.017) energy have influenced on the outcome of the EE. This means that all other practices except cultivation and grading have no variations between practices. Also, cultivation and grading are meant to be adjustable practice for the improvement of EE at least in conventional farms.
Table 3-38 presents the results of the relationship of farming practices and EE in OFS. The results show that seeding (p=0.046) and harvesting (p=0.015) have significant variations to determine the result of EE in organic soybean farms. That is, seeding and harvesting can be the adjustable practice variables to determine EE in organic farms.
<Table 3-38> Relations of Farming Practices and EE in OFS Input
Variables
(practices) B t value p value
(Constant) 1.256 4.017 .001
Cultivation -.043 -.903 .379
Fertilizing -.022 -1.197 .247
Mulching -.017 -.706 .489
Seeding -.169 -2.141 .046*
Weeding .057 .640 .530
Spraying 1.015 1.434 .169
Pruning -3.557 -1.201 .245
Irrigation .183 .530 .603
Harvesting .376 2.695 .015*
Threshing .004 .082 .936
Grading .739 .864 .399
R=0.731 R-square=0.534 Corrected R-square=0.250 Durbin-Watson=1.854 F=1.878 p=0.113*
-Dependent variable: EE, *p<.05 **p<.01
Therefore, to improve the efficiency of energy use, the alternative ways for cultivation and grading in CFS and for seeding and harvesting in OFS can be suggested as applicable solutions.