ESTIMACIÓN DEL COSTO DEL EQUIPO

It was important to note that concentrating on only one factor, such as fertilizer or fuel, cannot affect the appropriate energy use reductions in crop production. It was important to look at a farm as a complex network, containing several technical, social, and financial parameters. Some of these parameters may have positive or negative direct or indirect effects on each other and energy consumption. Management was a key factor to reduce energy use on farms. Improving operational efficiency and using new methods and technologies can significantly enhance energy conservation on farms.

For estimating energy consumption in wheat production, selecting the correct number of samples, designing an appropriate survey, measuring the direct and indirect inputs, and selecting accurate conversion coefficients were the key points. Some differences in other studies came from selecting different conversion coefficients. Additionally, as explained previously, data collection with sufficient number of samples was a complex and quite time consuming process. Therefore, designing a flexible survey and selecting the right method for data collection can improve the accuracy of the final results.

The lack of standard protocols to estimate the energy consumption on farms resulted in some difficulty in comparing different studies. Estimating national energy equivalents (conversion coefficients) and updating them after a period of time would increase the accuracy of final energy estimations. An international protocol should clearly identify the inputs and boundaries; also, it should define the standard method for data collection. The protocol should be flexible, taking into account social, technical, and financial limitations. For example, in some studies, post-harvesting processes and transportation have been estimated as energy inputs and not in others. Also, comparing a fully mechanized farming system with traditional farming based on human labour would be very difficult.

The energy consumption in wheat production was estimated at 22,566 MJ/ha. The main source of energy was fertilizer consumption (especially urea) with 10,651 MJ/ha (47%), which was by far the most important source of energy. Electricity (22%) was the second

most important source of energy in wheat production while fuel (14%) ranked third. The energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. The main source of energy in both systems was fertilizer with around 10,193 MJ/ha and 11,430 MJ/ha for irrigated farming and dryland farming, respectively.

Fertilizer management, particularly in relation to the use of urea to reduce indirect energy requirements in fertilizer manufacture, the method and timing of fertilizer distribution and the amount of fertilizer use must be taken into consideration. Using controlled release nitrogen fertilizers and appropriate rotations can also reduce fertilizer consumption. Appropriate plans for reducing fertilizer use on farms not only deliver financial benefits to the farmers, but also importantly, can reduce environmental impacts. The high proportion of the total energy consumption by fertilizers would increase the concern about NO+ emissions and water pollution in the future. Due to a significant correlation between N use and yields, reducing N consumption on farms would reduce wheat production. From the results of this study, it appeared that animal urine and manure were applied instead of N on mixed farms; however, the environmental effects of using animal urine and manure should be investigated.

Some studies about energy consumption in wheat production were available; however, due to different technological levels and environmental conditions, the lack of basic information, and the use of different energy conversion coefficients, comparing those results with this study was difficult. For example, Safa & Tabatabaeefar (2002) estimated energy use in irrigated and dryland farming for wheat production in Iran at around 45,970 and 17,106 MJ/ha, respectively; however, Safa et al. (2010) estimated energy use in irrigated and dryland farming in the same area around 51,587 and 12,543 MJ/ha, MJ/ha, respectively. The most important difference between these two studies was the severe drought during the second study, which increased fuel and electricity consumption for irrigation. However, during the time between the two studies, many old diesel pumps incorporated in the first study have been converted to electric pumps, thus saving energy on many irrigated farms. Consequently, understanding such details is essential when judging and comparing different energy studies, but these are not always available from

journal articles. For example, Singh and Mittal (1992) estimated energy consumption in irrigated and dryland wheat production in India at around 18,881 and 5,458 MJ/ha, respectively. However, comparing their result with this study involving different farming systems and technology was pointless. As another example, in New Zealand, Nguyen (1995) compared energy use in wheat production on conventional and biodynamic farms. Due to different study bases, again, comparison of the results would not be beneficial. Barber (2004) estimated energy consumption in irrigated and dryland farming in wheat production in New Zealand at around 34,150 and 20,190 MJ/ha, respectively. The amount and percentage of Barber‘s (2004) estimations for dryland farms were not far from the results of this study. However, there was a significant difference between the results of the two studies for energy use on irrigated farms. The most important difference in Barber‘s estimation and the result of this study for irrigated farming was electricity use in irrigation. Barber estimated electricity use in wheat production at around 16,000 MJ/ha; however, estimate in this study is approximately 7,700 MJ/ha. Barber (2004) did not mention the area of his two case studies or the irrigation systems; therefore, it was difficult to investigate his results. Electricity use on farms depended on several factors, such as climate, irrigation system, depth of well and soil type and it may even change in different years due to different amounts and distribution of precipitation. Incidentally, in this study, there were some farmers who used more electricity than Barber‘s (2004) estimation. As discussed before, thirty farms were investigated in this study to estimate energy consumption on irrigated farms and this number of case studies would have increased the accuracy of results.

Pimentel et al. (2002) carried out one of the most detailed studies of energy use on farms in the US. He estimated 15,000 MJ/ha energy use for winter wheat production in dryland farming. As discussed previously, several factors influenced the final energy use estimation, such as environmental factors, conversion coefficients, and farming method. However, Pimentel‘s (2002) estimations were not far from the results of this study.

On average, operational energy consumption found in this study was 7,997 MJ/ha. This was much higher in irrigated farming systems than in dryland farming systems.

Operational energy consumption was 10,870 MJ/ha on irrigated farms and 3,153 MJ/ha on dryland farms. The major difference was due to irrigation operations consuming 71% of the total operational energy consumption in irrigated farming. Tillage ranked high in both systems. It ranked first (46%) in dryland farming and second (13%) in irrigated farming. There was no significant difference between energy consumption of tillage operations and other operations in the two systems. In other words, farmers used similar operations, methods, and farming patterns in both irrigated and dryland farming systems.

6.3

Neural Network Model

The second main objective of this study was to design a model to predict energy consumption using different direct and indirect parameters. In creating a practical model, the number of samples and data collection method play a large role. Varied environmental/farming conditions and farmers‘ background make each farm unique; therefore, the number of samples and accurate data are critical in modelling studies in agriculture. Without a sufficiently large sample and accurate data, models cannot accurately predict energy use in agricultural production; this has reduced the interest of scientists in energy studies, especially modelling agricultural production. No study on modelling energy consumption in wheat and other agricultural production was found to compare the results of this study. Due to different conversion coefficients and environmental and farm conditions, models can have dissimilar outcomes.

Using a large number of correlated inputs can give results with minimum error; but the final model becomes unnecessary complex and unstable as the correlated inputs introduce redundancy into the model. Therefore, selecting a small number of independent inputs can lead to a more robust model. This study emphasized the complexity of the relationships between different parameters in wheat production (agriculture). Consequently, before any modelling study in agriculture, the relationship between different variables should be explored cautiously. In this study, it was attempted to select the minimum number of uncorrelated variables and variables that were easy to estimate and calculate. Consequently, the estimation error of the model would be minimised. To this end, the most important direct and indirect factors and their correlation were

investigated carefully. After an initial data pre-processing involving correlation analysis followed by PCA, five uncorrelated technical and social factors were selected as input variables to the ANN and these were N, P, irrigation frequency, crop area, and farmer‘s education.

Using genetic algorithms to optimise the network structure and neuron activation functions in conjunction with a search for the best training algorithm, it was possible to find a two layer modular neural network with high performance. Here, we reported the best ANN model found in this study. This ANN model can predict energy use in wheat production in Canterbury with acceptable accuracy (±2970 MJ/ha). The final ANN model showed the possibility of using direct and indirect technical factors combined with social attributes to predict technical parameters such as energy consumption in the agriculture sector. It would be advantageous to design future studies on modelling energy use in agriculture based on these results.

As discussed previously in Chapters 4 and 5, some variables influenced energy use directly; whereas; others showed indirect links to it. Similarly, out of the five variables used in the ANN model, some were directly linked to energy and others seemed to influence energy consumption indirectly. For example, the size of crop area and farmers‘ education, two variables used in the ANN model, had indirect links to energy. In other words, they did not have a direct cause and effect relationship with energy consumption. The size of crop area and education would indicate part of farmers‘ professional practice. To reduce energy use, prominent links should be recognised and carefully investigated in an extensive study.

The other three variables in the ANN model, N and P consumption and irrigation frequency, had direct correlations with energy consumption in wheat production. Irrigation frequency as well as irrigation system affected energy consumption through electricity use and its reduction can reduce energy use directly. Reduction of the use of N and P would also reduce energy consumption on farms. These three therefore were important variables in the ANN model. The direct effect of N, P, and electricity on energy consumption would be interesting to focus on to reduce energy use on farms.

However, as mentioned in section 5.3.3, the contribution of N and irrigation frequency were the highest in the ANN model and P use featured relatively low in the model. Thus, exploring the network of links between these variables would improve the utility of the final model.

It was expected that the ANN model would predict energy use in wheat production better than other modelling methods. A Multiple Linear Regression (MLR) model, a common modelling method in agricultural studies, was established. Comparison of results (r, r2, and MSE) of the ANN model with the linear regression model showed that the ANN model performed remarkably better than MLR in predicting energy consumption. It is possible that ANN models in other agricultural studies could provide better estimations with minimum errors.

The final model was capable of predicting energy use on a single farm or in a specific region. This will help farmers estimate energy use on their farms and compare it with other farms. It would also help decision makers to have a better view of energy use in wheat production.

Compared to other sectors, in the agriculture sector, uncontrolled factors had more influence on the final products. Therefore, comparing energy consumption of the same agricultural products in different years, without an understanding of the environmental parameters, would not be very beneficial. These differences could even change the structure and results of the models; therefore, each model could work only for a particular area and for a short period of time. Therefore, models should be updated with new data, which could possibly alter the model structure, input variables and the results.

Chapter 7

Conclusions and Recommendations

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In this study, a wide range of farming parameters was investigated to determine and model energy consumption in wheat production. The most important results of this study were presented and discussed in Chapters 5 and 6. In this Chapter, the conclusions of this study are discussed briefly.

7.1

Fuel Consumption

Fuel consumption was related to several direct and indirect factors. In this study, it was hypothesised that investigating these factors further would be important to reduce fuel consumption on farms. This study showed that fuel consumption in both irrigated and dryland farming followed similar patterns and tillage in both systems ranked as the highest fuel consuming activity. Given the findings of this study, the main conclusions are as follows:

-Tillage ranked the highest, with 45% of total fuel consumption. Mouldboard ploughs and field cultivators were used more than other equipment in tillage and fuel use in mouldboard ploughs was more than in other tillage operations. Using new techniques and machinery instead of mouldboard plough operations can significantly reduce fuel consumption on farms. In addition, reducing the number of tractor passes on farms can also reduce fuel consumption significantly.

- During 2007 and 2008, the price of oil and agricultural production increased simultaneously. This increased farmers‘ interest in using more powerful tractors for agricultural operations and a few of them used a combination of machines for tillage and sowing. It seemed that oil price had an important role in encouraging farmers to improve their technology. However, due to the direct link between the price of oil and the price of

agricultural production, it is not recommended to push farmers to reduce fuel and nitrogen use by increasing taxes or using other price manipulation methods.

- The study showed that there were some significant correlations between social factors and technical factors, which would be useful for fuel conservation in agriculture. For example, new generation of farmers preferred to use more powerful tractors, or the number of passes of some machinery was significantly correlated with farmer‘s age. From the results, it appeared old farmers preferred to use more conventional tillage than new tillage methods.

- Educated farmers had accepted new methods and machines to reduce fuel use in farm operations. Therefore, it would be beneficial to encourage farmers to employ educated farm managers or consultants on their farms. Improving knowledge and awareness of new technologies would be the best way to encourage new generation farmers to reduce fuel consumption in future.

7.2

Energy Consumption

Given the findings of this study, the most significant areas for improving overall energy efficiency on wheat farms in Canterbury region are as follows:

- In wheat production, fertilizer was by far the most important source of energy and electricity was the second most important source. Fuel came second on dryland farms and third on irrigated farms. Therefore, it is necessary to focus more on fertilizer, electricity, and fuel consumption than the other factors. Fertilizers, mainly nitrogen, have a significant influence on energy consumption, accounting for 47% of total energy consumption.

- Electricity consumption on irrigated farms, mostly for irrigation, is the most important difference in energy consumption between irrigated and dryland farms in wheat production.

- Comparison of the correlations between wheat production and direct and indirect energy sources showed that it could be possible to reduce the direct (operational) energy

sources, especially electricity and fuel, using better technology and management and with minimum yield reduction. However, reducing some indirect energy sources, such as nitrogen fertilizers and pesticides, would significantly reduce yields.

7.3

Neural Network Model

For the first time, in this study, an ANN model was designed to predict energy consumption in wheat production. Additionally, this study was the first to include several indirect factors, such as social factors and farm conditions. The final model was developed based on a modular neural network with two hidden layers that can predict energy consumption based on farm conditions (size of crop area), social factors (farmers‘ educational level), and energy inputs (N and P use, and irrigation frequency). The main conclusions from the ANN model developed to predict energy use in wheat production are as follows:

- The final ANN model can predict energy use in Canterbury wheat farms with an error margin of ± 2970 MJ/ha. This size of error in agricultural studies with several uncontrolled factors was quite acceptable. Furthermore, comparison between the ANN model and Multiple Linear Regression model (MLR) (the most common model in agricultural studies) showed that the ANN model can predict energy consumption better than the MLR model.

- The ANN model showed that it was possible to reduce energy use in wheat production by affecting direct and indirect parameters. Improving the model to predict the energy consumption of all farm products can provide more practical results for decision makers. It was clear that changing some of the effective variables in the short term was impossible; however, the model can help scientists and decision makers find the best direction for energy reductions in the future.

- The result of this study showed the ability of ANN model to predict energy consumption in wheat production by using heterogeneous data. Use of dissimilar variables, such as farm conditions and social factors, would improve the ability of

open new doors for scientists to investigate agricultural and environmental topics using a combination of direct and indirect technical and social parameters.