Ronaldo Vibart - Academia.edu (original) (raw)
Papers by Ronaldo Vibart
Soil Use and Management, Sep 2, 2023
... Oliphant, and Bianca Thompson, for their continuous support, assistance and encouragement. Sp... more ... Oliphant, and Bianca Thompson, for their continuous support, assistance and encouragement. Special thanks to Aaron Maye for his invaluable assistance in sample collection and field ... cover, persistence, and plant survival, as suggested by Barnes (1990) and De Battista and ...
Journal of New Zealand grasslands, May 16, 2023
Journal of Environmental Quality, Jun 25, 2022
Agricultural Systems, 2021
Animal Production Science, 2017
Farm-scale models were integrated with spatially discrete estimates of pasture production to exam... more Farm-scale models were integrated with spatially discrete estimates of pasture production to examine the potential farm and regional implications of removing palm-kernel expeller (PKE) as a supplementary feed from dairy farms in Southland, New Zealand. The following two farm-production systems representing the majority of dairy farms in the region were modelled: a System 3 farm (D3; mid-intensification, with 10–20% of imported feed) and a System 4 farm (D4; mid- to high intensification, with 20–30% of imported feed). Within each system, the impact of the following four PKE options was explored: (1) a control with PKE (Baseline); (2) no PKE, with fewer cows producing the same amount of milk per cow as in Baseline; (3) no PKE, with the same number of cows producing less milk per cow than in Baseline; and (4) PKE replaced with barley grain. Barley grain provides for similar flexibility (timing of purchase and feeding), and can be sourced locally. Faced with the need to remove PKE as a dietary ingredient, farmers would benefit from adopting the second PKE option (no PKE, with fewer cows producing the same amount of milk per cow as in Baseline); farm-operating profits were reduced by only 3% (compared with 30% of System 4 farms adopting the third PKE option, i.e. no PKE, with the same number of cows producing less milk per cow than in Baseline) relative to the Baseline farms. The narrow range of mean annual nitrate-nitrogen (nitrate-N) leaching losses (estimates ranged from 30 to 33 kg N/ha) reflects similar estimates of N intake and N excreted in urine across the modelled options. Substantial amounts of barley grain would need to be transported into the region or produced locally to replace PKE.
Agricultural Systems, Jun 1, 2017
Science of The Total Environment, Jun 1, 2014
Agricultural Systems, Jul 1, 2019
Journal of Environmental Management, Jun 1, 2015
Using a novel approach that links geospatial land resource information with individual farm-scale... more Using a novel approach that links geospatial land resource information with individual farm-scale simulation, we conducted a regional assessment of nitrogen (N) and phosphorous (P) losses to water and greenhouse gas (GHG) emissions to air from the predominant mix of pastoral industries in Southland, New Zealand. An evaluation of the cost-effectiveness of several nutrient loss mitigation strategies applied at the farm-scale, set primarily for reducing N and P losses and grouped by capital cost and potential ease of adoption, followed an initial baseline assessment. Grouped nutrient loss mitigation strategies were applied on an additive basis on the assumption of full adoption, and were broadly identified as 'improved nutrient management' (M1), 'improved animal productivity' (M2), and 'restricted grazing' (M3). Estimated annual nitrate-N leaching losses occurring under representative baseline sheep and beef (cattle) farms, and representative baseline dairy farms for the region were 10 ± 2 and 32 ± 6 kg N/ha (mean ± standard deviation), respectively. Both sheep and beef and dairy farms were responsive to N leaching loss mitigation strategies in M1, at a low cost per kg N-loss mitigated. Only dairy farms were responsive to N leaching loss abatement from adopting M2, at no additional cost per kg N-loss mitigated. Dairy farms were also responsive to N leaching loss abatement from adopting M3, but this reduction came at a greater cost per kg N-loss mitigated. Only dairy farms were responsive to P-loss mitigation strategies, in particular by adopting M1. Only dairy farms were responsive to GHG abatement; greater abatement was achieved by the most intensified dairy farm system simulated. Overall, M1 provided for high levels of regional scale N- and P-loss abatement at a low cost per farm without affecting overall farm production, M2 provided additional N-loss abatement but only marginal P-loss abatement, whereas M3 provided the greatest N-loss abatement, but delivered no additional P abatement, and came at a large financial cost to farmers, sheep and beef farmers in particular. The modelling approach provides a farm-scale framework that can be extended to other regions to accommodate different farm production systems and performances, capturing the interactions between farm types, land use capabilities and production levels, as these influence nutrient losses and GHG emissions, and the effectiveness of mitigation strategies.
Journal of Dairy Science, Sep 1, 2021
Several studies have been conducted to improve grazing management and supplementation in pasture-... more Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.
Journal of Animal Science, Sep 5, 2018
Science of The Total Environment, May 1, 2020
Journal of New Zealand grasslands, Feb 2, 2022
Nutrient Cycling in Agroecosystems, Sep 20, 2022
Proceedings of the New Zealand Grassland Association, 2013
Science of The Total Environment, May 1, 2021
This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their abil... more This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.
Soil Use and Management, Sep 2, 2023
... Oliphant, and Bianca Thompson, for their continuous support, assistance and encouragement. Sp... more ... Oliphant, and Bianca Thompson, for their continuous support, assistance and encouragement. Special thanks to Aaron Maye for his invaluable assistance in sample collection and field ... cover, persistence, and plant survival, as suggested by Barnes (1990) and De Battista and ...
Journal of New Zealand grasslands, May 16, 2023
Journal of Environmental Quality, Jun 25, 2022
Agricultural Systems, 2021
Animal Production Science, 2017
Farm-scale models were integrated with spatially discrete estimates of pasture production to exam... more Farm-scale models were integrated with spatially discrete estimates of pasture production to examine the potential farm and regional implications of removing palm-kernel expeller (PKE) as a supplementary feed from dairy farms in Southland, New Zealand. The following two farm-production systems representing the majority of dairy farms in the region were modelled: a System 3 farm (D3; mid-intensification, with 10–20% of imported feed) and a System 4 farm (D4; mid- to high intensification, with 20–30% of imported feed). Within each system, the impact of the following four PKE options was explored: (1) a control with PKE (Baseline); (2) no PKE, with fewer cows producing the same amount of milk per cow as in Baseline; (3) no PKE, with the same number of cows producing less milk per cow than in Baseline; and (4) PKE replaced with barley grain. Barley grain provides for similar flexibility (timing of purchase and feeding), and can be sourced locally. Faced with the need to remove PKE as a dietary ingredient, farmers would benefit from adopting the second PKE option (no PKE, with fewer cows producing the same amount of milk per cow as in Baseline); farm-operating profits were reduced by only 3% (compared with 30% of System 4 farms adopting the third PKE option, i.e. no PKE, with the same number of cows producing less milk per cow than in Baseline) relative to the Baseline farms. The narrow range of mean annual nitrate-nitrogen (nitrate-N) leaching losses (estimates ranged from 30 to 33 kg N/ha) reflects similar estimates of N intake and N excreted in urine across the modelled options. Substantial amounts of barley grain would need to be transported into the region or produced locally to replace PKE.
Agricultural Systems, Jun 1, 2017
Science of The Total Environment, Jun 1, 2014
Agricultural Systems, Jul 1, 2019
Journal of Environmental Management, Jun 1, 2015
Using a novel approach that links geospatial land resource information with individual farm-scale... more Using a novel approach that links geospatial land resource information with individual farm-scale simulation, we conducted a regional assessment of nitrogen (N) and phosphorous (P) losses to water and greenhouse gas (GHG) emissions to air from the predominant mix of pastoral industries in Southland, New Zealand. An evaluation of the cost-effectiveness of several nutrient loss mitigation strategies applied at the farm-scale, set primarily for reducing N and P losses and grouped by capital cost and potential ease of adoption, followed an initial baseline assessment. Grouped nutrient loss mitigation strategies were applied on an additive basis on the assumption of full adoption, and were broadly identified as 'improved nutrient management' (M1), 'improved animal productivity' (M2), and 'restricted grazing' (M3). Estimated annual nitrate-N leaching losses occurring under representative baseline sheep and beef (cattle) farms, and representative baseline dairy farms for the region were 10 ± 2 and 32 ± 6 kg N/ha (mean ± standard deviation), respectively. Both sheep and beef and dairy farms were responsive to N leaching loss mitigation strategies in M1, at a low cost per kg N-loss mitigated. Only dairy farms were responsive to N leaching loss abatement from adopting M2, at no additional cost per kg N-loss mitigated. Dairy farms were also responsive to N leaching loss abatement from adopting M3, but this reduction came at a greater cost per kg N-loss mitigated. Only dairy farms were responsive to P-loss mitigation strategies, in particular by adopting M1. Only dairy farms were responsive to GHG abatement; greater abatement was achieved by the most intensified dairy farm system simulated. Overall, M1 provided for high levels of regional scale N- and P-loss abatement at a low cost per farm without affecting overall farm production, M2 provided additional N-loss abatement but only marginal P-loss abatement, whereas M3 provided the greatest N-loss abatement, but delivered no additional P abatement, and came at a large financial cost to farmers, sheep and beef farmers in particular. The modelling approach provides a farm-scale framework that can be extended to other regions to accommodate different farm production systems and performances, capturing the interactions between farm types, land use capabilities and production levels, as these influence nutrient losses and GHG emissions, and the effectiveness of mitigation strategies.
Journal of Dairy Science, Sep 1, 2021
Several studies have been conducted to improve grazing management and supplementation in pasture-... more Several studies have been conducted to improve grazing management and supplementation in pasture-based systems. However, it is necessary to develop tools that integrate the available information linking the representation of biological processes with animal performance for use in decision making. The objective of this study was to evaluate the precision and accuracy of the Molly cow model predictions of ruminal fermentation, nutrient digestion, and animal performance by cows consuming pasture-based diets to identify model strengths and weaknesses, and to derive new digestive parameters when relevant. Model modifications for adipose tissue, protein synthesis in lean body mass and viscera representation were included. Data used for model evaluations were collected from 25 publications containing 115 treatment means sourced from studies conducted with lactating dairy cattle. The inclusion criteria were that diets contained ≥45% perennial ryegrass (Lolium perenne L.), and that dry matter intake, dietary ingredient composition, and nutrient digestion observations were reported. Animal performance and N excretion variables were also included if they were reported. Model performance was assessed before and after model reparameterization of selected digestive parameters, global sensitivity analysis was conducted after reparameterization, and a 5-fold cross evaluation was performed. Although rumen fermentation predictions were not significantly improved, rumen volatile fatty acids absorption rates were recalculated, which improved the concordance correlation coefficient (CCC) for rumen propionate and ammonia concentration predictions but decreased CCC for acetate predictions. Similar degradation rates of crude protein were observed for grass and total mixed ration diets, but rumen-undegradable protein predictions seemed to be affected by the solubility of the protein source as was the intestinal digestibility coefficient. Ruminal fiber degradation was greater after reparameterization, driven primarily by hemicellulose degradation. Predictions of ruminal and fecal outflow of neutral detergent fiber and acid detergent fiber, as well as total fecal output predictions, improved significantly after reparameterization. Blood urea N and urinary N excretion predictions resulted in similar accuracy using both sets of model parameters, whereas fecal N excretion predictions were significantly improved after reparameterization. Body weight and body condition score predictions were greatly improved after model modifications and reparameterization. Before reparameterization, yield predictions for daily milk, milk fat, milk protein, and milk lactose were greatly overestimated (mean bias of 61.0, 58.7, 73.7, and 64.6% of mean squared error, respectively). Although this problem was partially addressed by model modifications and reparameterization (mean bias of 3.2, 1.1, 1.7, and 0.4% of mean squared error, respectively), CCC values were still small. The ability of the model to predict grass digestion and animal performance in dairy cows consuming pasture-based diets was improved, demonstrating the applicability of this model to these productive systems. However, the failure to predict grass digestion based on standard model inputs without reparameterization indicates there are still fundamental challenges in characterizing feeds for this model.
Journal of Animal Science, Sep 5, 2018
Science of The Total Environment, May 1, 2020
Journal of New Zealand grasslands, Feb 2, 2022
Nutrient Cycling in Agroecosystems, Sep 20, 2022
Proceedings of the New Zealand Grassland Association, 2013
Science of The Total Environment, May 1, 2021
This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their abil... more This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.