The primary objective of this dissertation is to investigate whether Canadian whole farm programs with both income-supporting and income-stabilizing attributes, which are considered as decoupled based on the WTO criterion, are actually decoupled from production. The dissertation began with the review of the existing theoretical and empirical literature on the impact of programs designed to be decoupled payments on acreage response including studies related to the wealth and insurance effects. The review revealed that previous studies lack a detailed theoretical model of how acreage decisions will be affected by stabilizing the farm profit (insurance effect) as well as the higher expected profit (wealth effect). Given the nature of Canadian whole farm programs which attempt to smooth income, to examine the whole farm programs, a model is needed to capture the insurance effect arising from these programs as well as the wealth effect.<p> To address this gap, the theoretical framework developed by Chavas and Holt (1990) was extended, in this dissertation, to incorporate the insurance effect into the farmers' acreage decisions under uncertainty. In particular, by developing theoretical restrictions, which consider the relationship between income stabilization compensated and uncompensated acreage decision functions, the insurance effect emphasized in the literature was explicitly derived within the theoretical model. The acreage allocated to each crop was derived as a function of expected crop profits, elements of the variance-covariance matrix of crop profits, expected total wealth (initial wealth plus market profit), and variance of total wealth. The government payments were incorporated into the model through truncation of the probability distribution of profits. Specifically, the whole-farm programs truncated the total (farm) profit distribution which affected the expected total wealth and variance of total wealth.<p> The theoretical model was then used to develop an empirical model. The econometric model was applied to acreage data in the Canadian Prairies from 1970 to 2006 in order to statistically test if the whole farm programs were really decoupled. The results revealed that coefficients of expected total wealth (wealth effect) and variance of total wealth (insurance effect) were statistically significant in the whole system, which implied the whole-farm programs were production and therefore trade distorting and were not actually decoupled, even if they satisfied the WTO criteria. The statistically significant coefficients for expected total wealth and variance of total wealth variables were then used to simulate the impact of recent whole-farm programsthe Western Grain Stabilization Act (WGSA), the Net Income Stabilization Account (NISA) and the Canadian Agricultural Income Stabilization (CAIS)on crop choices.<p> The results suggested that the WGSA, NISA and CAIS programs have increased the acreage allocated to spring wheat and peas (through both wealth and insurance effects, although the insurance effect appears to dominate) while they have decreased the acreage for barley (through the wealth effect), canola and hay (through the insurance effect) in the prairie provinces. In general, the size of the wealth effect was quite small, while the insurance effect was always significant. Specifically, the acreage allocated to wheat increased by 7.79 percent on average across Prairies while canola acreage decreased by 8.86 percent under the CAIS. Thus, the empirical results revealed that for Canadian whole-farm programs the impact of the effects related to risk is important. Particularly, the results showed the inherent difficulty in divorcing the stabilization effect received by Canadian whole-farm programs from farmers' production decisions.

The primary objective of this dissertation is to investigate whether Canadian whole farm programs with both income-supporting and income-stabilizing attributes, which are considered as decoupled based on the WTO criterion, are actually decoupled from production. The dissertation began with the review of the existing theoretical and empirical literature on the impact of programs designed to be decoupled payments on acreage response including studies related to the wealth and insurance effects. The review revealed that previous studies lack a detailed theoretical model of how acreage decisions will be affected by stabilizing the farm profit (insurance effect) as well as the higher expected profit (wealth effect). Given the nature of Canadian whole farm programs which attempt to smooth income, to examine the whole farm programs, a model is needed to capture the insurance effect arising from these programs as well as the wealth effect.<p> To address this gap, the theoretical framework developed by Chavas and Holt (1990) was extended, in this dissertation, to incorporate the insurance effect into the farmers' acreage decisions under uncertainty. In particular, by developing theoretical restrictions, which consider the relationship between income stabilization compensated and uncompensated acreage decision functions, the insurance effect emphasized in the literature was explicitly derived within the theoretical model. The acreage allocated to each crop was derived as a function of expected crop profits, elements of the variance-covariance matrix of crop profits, expected total wealth (initial wealth plus market profit), and variance of total wealth. The government payments were incorporated into the model through truncation of the probability distribution of profits. Specifically, the whole-farm programs truncated the total (farm) profit distribution which affected the expected total wealth and variance of total wealth.<p> The theoretical model was then used to develop an empirical model. The econometric model was applied to acreage data in the Canadian Prairies from 1970 to 2006 in order to statistically test if the whole farm programs were really decoupled. The results revealed that coefficients of expected total wealth (wealth effect) and variance of total wealth (insurance effect) were statistically significant in the whole system, which implied the whole-farm programs were production and therefore trade distorting and were not actually decoupled, even if they satisfied the WTO criteria. The statistically significant coefficients for expected total wealth and variance of total wealth variables were then used to simulate the impact of recent whole-farm programsthe Western Grain Stabilization Act (WGSA), the Net Income Stabilization Account (NISA) and the Canadian Agricultural Income Stabilization (CAIS)on crop choices.<p> The results suggested that the WGSA, NISA and CAIS programs have increased the acreage allocated to spring wheat and peas (through both wealth and insurance effects, although the insurance effect appears to dominate) while they have decreased the acreage for barley (through the wealth effect), canola and hay (through the insurance effect) in the prairie provinces. In general, the size of the wealth effect was quite small, while the insurance effect was always significant. Specifically, the acreage allocated to wheat increased by 7.79 percent on average across Prairies while canola acreage decreased by 8.86 percent under the CAIS. Thus, the empirical results revealed that for Canadian whole-farm programs the impact of the effects related to risk is important. Particularly, the results showed the inherent difficulty in divorcing the stabilization effect received by Canadian whole-farm programs from farmers' production decisions.

This thesis examines the use of two specific types of precision agriculture technologies: cost maps and lightbar. Cost maps visually depict spatial differences in production costs. The visual depictions of these costs are represented using ArcGIS in an attempt to aide farmers in further decision making. Results will show that cost maps have great possibilities in their addition to the set of tools that farmers use in decision making. This thesis will expand the understanding of lightbar from a partial budget study to a whole farm model incorporating competition across different enterprises for labor and capital. The results from the study of cost maps indicate that inaccuracy of machinery movement, whether in the application stage or the harvesting stage is very costly. As a result, the suggestion of lightbar as a guidance aide to improve farm profitability is recommended under the conditions analyzed and shows a net farm return increase in just over 6%.

Animal agriculture plays a vital role in the provision of food for the world population; however, in the wake of global warming and greenhouse gas (GHG) emissions, the industry has been under scrutiny as one of the net emitters causing global warming. The same scrutiny applies to beef production in western Canada. The objective of this study is to evaluate the economic impact of GHG mitigation practices (GHGMP) for beef operations, and in the process identify economic and environmental sustainable scenarios. This study was an extension to a study by Beauchemin et al (2011) who studied the mitigation of GHG emissions from beef production in western Canada A beef simulation model was developed to measure the impacts of adopting GHGMPs on the profitability of a mixed farm in Vulcan County, Southern Alberta. Feed for the herd was produced on the farm, and calves were born and finished on the farm. Whole farm gross margin was used as a profitability measure of the farm over a period of 9 years, which is a full beef production cycle. Eleven GHGMPs were examined and compared to the baseline scenario. These scenarios were adopted from Beauchemin et al (2011), and included dietary modifications (change in use of forages, use of canola seed, and corn distillers grains, and improvement in quality of forage), and improvement in animal husbandry (increased weaning rates, and increased longevity of breeding stock). Simulation results showed a discounted whole farm gross margin of $11.38 per acre for the baseline scenario. Feed costs accounted for 47.1 percent of total costs of beef production. The change in whole farm gross margin per acre from implementation of different GHGMPs ranged from an increase of 4 percent to a decrease of 5 percent. Six scenarios were identified as ‘win-win’ scenarios as they improved both environment and economics of the farm. The profit of these scenarios ranged from $238.11 to $30.31 per tonne of GHG reductions expressed in carbon dioxide equivalent). The loss from the other scenarios capable of reducing GHG emissions range from $92.06 to $582.46 per tonne GHG reduced. Based on these results, it was concluded that western Canadian beef producers can adopt sustainable GHGMPs without substantial changing the structure of their operations. Scenarios that improved both the environment and the economics of the farm were: Scenario 7: use of corn distillers dried grain (CDDG) in finishing ration; Scenario 4: use of canola seed in finishing ration; Scenario 8: use of CDDG in breeding stock ration; Scenario 10: increased calve weaning rate (85% to 90%); Scenario 5: use of canola seed in breeding stock ration; and Scenario 9 : improved hay for breeding stock.

Agricultural runoff is the largest source of nitrogen and phosphorus pollution entering the Chesapeake Bay, contributing 38% of nitrogen and 45% of phosphorus (USEPA, 2010). Since agricultural runoff is the number one contributing source of nitrogen and phosphorus entering the Chesapeake Bay, action needs to be taken to reduce nitrogen and phosphorus on agriculture production facilities, such as dairy farms. The impact of feed management software on whole-farm nutrient balance was studied on 18 dairy farms located in Virginia from 2006 to 2010. Nine farms began using the TMR Tracker feed management software in 2006 and were compared to 9 control farms not using feed management software. Each of the treatment farms were visited on a monthly basis to collect ration and feed ingredient samples and feed management data. Whole-farm nutrient balance was calculated using University of Nebraska software. Herd sizes and crop hectares averaged 314 and 366 for treatment and 298 and 261 for control farms. Milk production averaged 3,226 and 2,650 tonnes per year respectively. Measures of surplus (input-output) and use efficiency (input/output) for nitrogen and phosphorus were analyzed over a four year time span and did not differ between treatment and control farms whether expressed on a per farms, cow or hectare basis. Due to the large variation in feeding accuracy within farms, the use of feed management software did not influence whole-farm nutrient balance. Sources of variation that contributed to loading errors were investigated within the feed management data. Percent load deviation increased over time from 2007 to 2009 from 0.94 ± 0.53 to 2.37 ± 0.50 percent of the actual load weight. Effects of month, day of the week and time of day on percent load deviation were not significant. There was no effect of percent load deviation on milk production. No relationship was observed between percent load deviation and whole-farm nutrient balance. / Master of Science

Impact of precision feeding with feed management software was assessed for whole farm nutrient balance (WFNB) and feeding management from January through December 2006. Nine treatment and six control farms were selected in four regions of the Chesapeake Bay Watershed of Virginia. Herd sizes averaged 271 and 390 lactating cows for treatment and control farms while milk yield averaged 30 and 27 kg/d per lactating cow, respectively. Crop hectares grown averaged 309 and 310 ha for treatment and control farms, respectively. Treatment farms purchased and installed feed management software (TMR Tracker, Digi-Star LLC, Fort Atkinson WI) between May and October 2006 and received more frequent feed analysis and feedback. Data were collected for calendar year 2005 and 2006 to compute WFNB using software from the University of Nebraska. On treatment farms, up to five feed samples were obtained monthly from individual feedstuffs and each total mixed ration (TMR) fed to lactating cows. Control farms submitted TMR samples every 2 mo. Standard wet chemistry analysis of samples was performed. Data stored in the software were collected monthly from each treatment farm concurrent with feed sampling. Producers from each treatment farm participated in a 24-question personal interview in December 2006 addressing installation, operation, and satisfaction with the software. Daily feeding deviation of all ingredients across treatment farms averaged 173 ± 163 kg/d. This corresponded to average daily overfeeding of CP and P of 17.6 ± 17 and 0.4 ± 0.3 kg/d, respectively. Feeding deviation did not differ between feeders. Milk production was negatively associated with kg total deviation and kg CP deviation, but positively related to P deviation. Whole farm nutrient balance did not differ between treatment and control farms. All producers indicated TMR Tracker met expectations. Change made to the feeding program due to TMR Tracker was correlated (r=0.80) with perceived improvement in ration consistency. In conclusion, producers perceived feed management software as beneficial, but WFNB was not reduced after 3 to 6 mo of using feed management software; however, the large variation in daily over or under feeding indicates potential for future reductions in WFNB through reduced feeding variability. / Master of Science

The thesis presented consists of two essays that analyze input substitution and decision making in crop and livestock production systems. The first essay consists of a whole-farm analysis that sought to optimize feed mixes and enterprise combinations for an organic dairy operation in the Southeastern United States. This was accomplished through mathematical programming where whole-farm net returns were maximized, and total feed costs were minimized simultaneously for four milk production level cases. Additionally, the sensitivity of the system and break-even milk price were explored. Results suggest substitutability in ration components where an increase in supplemental feeds is justified by additional milk output and sales. The second essay utilizes econometric methods and hedonic modeling to explore factors that drive the price of row crop planters on the used machinery market. Factors relating to make, age, condition, planter specifications, sale type, spatial aspects, seasonality, and year of the sale were analyzed. Results suggest non-linear relationships for row number and age relative to price and interactions between variables make and age that imply varying depreciation depending on the manufacturer. An additional break-even analysis relating to pasture yields and planter purchase price was conducted to explore these primary concepts in further detail.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

Master of Agribusiness / Department of Agricultural Economics / Jason Bergtold / The dramatic changes that have taken place in the production agriculture industry in the last decade have the Long Family Partnership wanting to reassess their farm land management strategy. As land owners, they feel as though they might be missing out on profit opportunity by continuing their current lease agreements as status quo. The objective of this research is to determine the optimal land management strategy for the Partnership farm that maximizes net returns for crop production, but also taking into account input costs and risk. Three scenarios were built: (1) a Base Case of the current share-crop and cash lease Agreements; (2) the possibility of farming their own irrigated farm land and continuing to cash lease land used to produce dryland wheat; and (3) deciding to farm all the irrigated and dry land farm acreage themselves. In order to do this, a whole-farm budget spreadsheet model was generated to assess alternative land management scenarios. The difference in net returns between alternative land rental scenarios were then compared and followed by a sensitivity analysis and stochastic analysis using @RISK software. The findings concluded that there was greater potential to increase net farm income while still conservatively managing risk by investing into their own farm land, as not only owners but also as operators. The stochastic and sensitivity analysis confirmed that farming their own land was more sensitive to changes in yields, prices and input expenses. However, even in consideration of the additional risk, the probability of increasing net farm income was greater for the scenarios in which they farmed their own land.