Pub Date : 2023-11-29DOI: 10.1017/s0021859623000540
Peter Doyle, Edward G O'Riordan, Mark McGee, Paul Crosson, Alan K Kelly, Aidan Moloney
The objective was to evaluate steer performance, meat nutritional value, land-use, food–feed competition and both economic and environmental sustainability within temperate pasture-based suckler weanling-to-beef systems with or without (forage-only) concentrates. Post-weaning, 8-month-old, late-maturing breed steers (333 kg) were assigned to one of three systems: (1) grass silage + 1.2 kg concentrate DM (148 days), followed by pasture (123 days) and finished on ad libitum concentrates (120 days) – slaughter age, 21 months (GRAIN); (2) as per (1) but pasture (196 days) and finished on grass silage ad libitum + 3.5 kg concentrate DM (124 days) – slaughter age, 24 months (SIL + GRAIN); and (3) grass silage-only (148 days), pasture (196 days), silage-only (140 days) and finished on pasture (97 days) – slaughter age, 28 months (FORAGE). The mean target carcass weight was 390 kg for each system. Data generated were used to parameterize a farm-level beef systems model. Concentrate DM intake was 1187, 606 and 0 kg/head for GRAIN, SIL + GRAIN and FORAGE, respectively. The forage-only (FORAGE) system offers several advantages, including improved farm profitability, enhanced meat fatty acid profile and only utilized inedible human feed. Consequently, associated greenhouse gas (GHG) emissions per net human edible food produced were more favourable for FORAGE. However, compared to GRAIN, the FORAGE system had an older age at slaughter and associated increased pasture land-use and GHG emissions per animal, meat weight gain and essential amino acids gain. There are therefore inevitable trade-offs, as one beef system does not improve all sustainability and GHG emission metrics.
{"title":"Temperate pasture- or concentrate-beef production systems: steer performance, meat nutritional value, land-use, food–feed competition, economic and environmental sustainability","authors":"Peter Doyle, Edward G O'Riordan, Mark McGee, Paul Crosson, Alan K Kelly, Aidan Moloney","doi":"10.1017/s0021859623000540","DOIUrl":"https://doi.org/10.1017/s0021859623000540","url":null,"abstract":"The objective was to evaluate steer performance, meat nutritional value, land-use, food–feed competition and both economic and environmental sustainability within temperate pasture-based suckler weanling-to-beef systems with or without (forage-only) concentrates. Post-weaning, 8-month-old, late-maturing breed steers (333 kg) were assigned to one of three systems: (1) grass silage + 1.2 kg concentrate DM (148 days), followed by pasture (123 days) and finished on <jats:italic>ad libitum</jats:italic> concentrates (120 days) – slaughter age, 21 months (GRAIN); (2) as per (1) but pasture (196 days) and finished on grass silage <jats:italic>ad libitum</jats:italic> + 3.5 kg concentrate DM (124 days) – slaughter age, 24 months (SIL + GRAIN); and (3) grass silage-only (148 days), pasture (196 days), silage-only (140 days) and finished on pasture (97 days) – slaughter age, 28 months (FORAGE). The mean target carcass weight was 390 kg for each system. Data generated were used to parameterize a farm-level beef systems model. Concentrate DM intake was 1187, 606 and 0 kg/head for GRAIN, SIL + GRAIN and FORAGE, respectively. The forage-only (FORAGE) system offers several advantages, including improved farm profitability, enhanced meat fatty acid profile and only utilized inedible human feed. Consequently, associated greenhouse gas (GHG) emissions per <jats:italic>net</jats:italic> human edible food produced were more favourable for FORAGE. However, compared to GRAIN, the FORAGE system had an older age at slaughter and associated increased pasture land-use and GHG emissions per animal, meat weight gain and essential amino acids gain. There are therefore inevitable trade-offs, as one beef system does not improve all sustainability and GHG emission metrics.","PeriodicalId":501199,"journal":{"name":"The Journal of Agricultural Science","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-24DOI: 10.1017/s0021859623000564
Josep Gasa, S. Capalbo, Jennifer Ellis, David Sola, James France
The objective of this paper was to investigate how the predicted level of body energy mobilized and the stage of lactation affects performance and energy partitioning in lactating sows kept under commercial conditions. Seventy-seven lactating sows from three consecutive batches were weaned at 28 d and all measures were taken over the first 20 d. Total feed consumption was measured and sows’ live weight was registered when entering the lactation facilities and at 21 d of lactation. Blood samples were collected at farrowing and once a week thereafter. Net energy (NE) mobilization or loss was calculated by difference using the general NRC equation for ME partitioning. Compared to low mobilizers (low NE loss values), high mobilizing sows had lower feed intake and higher loss of live weight, body fat and body protein. High mobilizers also weaned more piglets and had heavier litters than low mobilizers. Energy mobilization (NE loss) was higher from day 1 to 10 of lactation compared to day 11 to 20, and the difference in mobilized energy between high and low mobilizing sows was also higher in the first than in the second half of lactation. Body weight and back fat thickness losses were significantly correlated with NE loss. A more accurate prediction of the changes in live weight or back fat thickness over lactation should help better predict total amount of energy mobilized, and more research is needed to assess the relative contribution of lean and fat to mobilized tissue.
{"title":"The effect of tissue mobilization and stage of lactation on energy partitioning in lactating sows: an analysis of commercial data","authors":"Josep Gasa, S. Capalbo, Jennifer Ellis, David Sola, James France","doi":"10.1017/s0021859623000564","DOIUrl":"https://doi.org/10.1017/s0021859623000564","url":null,"abstract":"The objective of this paper was to investigate how the predicted level of body energy mobilized and the stage of lactation affects performance and energy partitioning in lactating sows kept under commercial conditions. Seventy-seven lactating sows from three consecutive batches were weaned at 28 d and all measures were taken over the first 20 d. Total feed consumption was measured and sows’ live weight was registered when entering the lactation facilities and at 21 d of lactation. Blood samples were collected at farrowing and once a week thereafter. Net energy (NE) mobilization or loss was calculated by difference using the general NRC equation for ME partitioning. Compared to low mobilizers (low NE loss values), high mobilizing sows had lower feed intake and higher loss of live weight, body fat and body protein. High mobilizers also weaned more piglets and had heavier litters than low mobilizers. Energy mobilization (NE loss) was higher from day 1 to 10 of lactation compared to day 11 to 20, and the difference in mobilized energy between high and low mobilizing sows was also higher in the first than in the second half of lactation. Body weight and back fat thickness losses were significantly correlated with NE loss. A more accurate prediction of the changes in live weight or back fat thickness over lactation should help better predict total amount of energy mobilized, and more research is needed to assess the relative contribution of lean and fat to mobilized tissue.","PeriodicalId":501199,"journal":{"name":"The Journal of Agricultural Science","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-24DOI: 10.1017/s0021859623000503
Ketema Tilahun Zeleke, Muhuddin Anwar, Livinus Emebiri, David Luckett
When water and nutrients are not limiting, and pests and disease are effectively controlled, crop growth and yield is determined by weather conditions such as temperature and solar radiation. To determine the relationship between weather indices and crop yield, multiple wheat varieties were sown at two sowing times, for five sowing seasons and at two locations. The following weather indices around the 50% anthesis stage were recorded and analysed: mean temperature (Tmean), maximum temperature (Tmax), number of days with temperature >30°C (T30), vapour pressure deficit (VPD), photosynthetically active radiation, photothermal quotient (PQ) and photothermal quotient corrected for vapour pressure deficit (PQvpd). Overall, for every 1°C rise in temperature, crop yield decreased by 370 kg/ha. For every 1°C rise in temperature, normal sowing window yield decreased by 360 kg/ha while late-sown wheat yield decreased by 640 kg/ha. Correlation analysis was conducted between the weather indices and grain number, grain yield and grain protein. There was a significant positive correlation between PQ and PQvpd and grain number and grain yield. There was a significant negative correlation between Tmean, Tmax, T30 and VPD and grain number and grain yield. Grain protein content showed a positive correlation with maximum air temperature and a negative correlation with the weather indices PQ and PQvpd. PQ and PQvpd can be used to predict grain number and grain yield potential. This study showed that grain number and grain yield predicted using PQ and PQvpd are more reliable than using temperature and radiation individually.
{"title":"Weather indices during reproductive phase explain wheat yield variability","authors":"Ketema Tilahun Zeleke, Muhuddin Anwar, Livinus Emebiri, David Luckett","doi":"10.1017/s0021859623000503","DOIUrl":"https://doi.org/10.1017/s0021859623000503","url":null,"abstract":"When water and nutrients are not limiting, and pests and disease are effectively controlled, crop growth and yield is determined by weather conditions such as temperature and solar radiation. To determine the relationship between weather indices and crop yield, multiple wheat varieties were sown at two sowing times, for five sowing seasons and at two locations. The following weather indices around the 50% anthesis stage were recorded and analysed: mean temperature (<jats:italic>T</jats:italic><jats:sub>mean</jats:sub>), maximum temperature (<jats:italic>T</jats:italic><jats:sub>max</jats:sub>), number of days with temperature >30°C (T30), vapour pressure deficit (VPD), photosynthetically active radiation, photothermal quotient (PQ) and photothermal quotient corrected for vapour pressure deficit (PQ<jats:sub>vpd</jats:sub>). Overall, for every 1°C rise in temperature, crop yield decreased by 370 kg/ha. For every 1°C rise in temperature, normal sowing window yield decreased by 360 kg/ha while late-sown wheat yield decreased by 640 kg/ha. Correlation analysis was conducted between the weather indices and grain number, grain yield and grain protein. There was a significant positive correlation between PQ and PQ<jats:sub>vpd</jats:sub> and grain number and grain yield. There was a significant negative correlation between <jats:italic>T</jats:italic><jats:sub>mean</jats:sub>, <jats:italic>T</jats:italic><jats:sub>max</jats:sub>, T30 and VPD and grain number and grain yield. Grain protein content showed a positive correlation with maximum air temperature and a negative correlation with the weather indices PQ and PQ<jats:sub>vpd</jats:sub>. PQ and PQ<jats:sub>vpd</jats:sub> can be used to predict grain number and grain yield potential. This study showed that grain number and grain yield predicted using PQ and PQ<jats:sub>vpd</jats:sub> are more reliable than using temperature and radiation individually.","PeriodicalId":501199,"journal":{"name":"The Journal of Agricultural Science","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Activity biosensors have been used recently to measure and diagnose the physiological status of dairy cows. However, owing to the variety of commercialized activity biosensors available in the market, activity data generated by a biosensor need to be standardized to predict the status of an animal and make relevant decisions. Hence, the objective of this study was to develop a standardization method for accommodating activity measurements from different sensors. Twelve Holstein dairy cows were monitored to collect 12 862 activity data from four types of sensors over five months. After confirming similar cyclic activity patterns from the sensors through correlation and regression analyses, the gamma distribution was employed to calculate the cumulative probability of the values of each biosensor. Then, the activity values were assigned to three levels (i.e., idle, normal and active) based on the defined proportion of each level, and the values at each level from the four sensors were compared. The results showed that the number of measurements belonging to the same level was similar, with less than a 10% difference at a specific threshold value. In addition, more than 87% of the heat alerts generated by the internal algorithm of three of the four biosensors could be assigned to the active level, suggesting that the current standardization method successfully integrated the activity measurements from different biosensors. The developed probability-based standardization method is expected to be applicable to other biosensors for livestock, which will lead to the development of models and solutions for precision livestock farming.
{"title":"A statistical method to standardize and interpret the activity data generated by wireless biosensors in dairy cows","authors":"Wang-Hee Lee, Mingyung Lee, Dae-Hyun Lee, Jae-Min Jung, Hyunjin Cho, Seongwon Seo","doi":"10.1017/s0021859623000576","DOIUrl":"https://doi.org/10.1017/s0021859623000576","url":null,"abstract":"Activity biosensors have been used recently to measure and diagnose the physiological status of dairy cows. However, owing to the variety of commercialized activity biosensors available in the market, activity data generated by a biosensor need to be standardized to predict the status of an animal and make relevant decisions. Hence, the objective of this study was to develop a standardization method for accommodating activity measurements from different sensors. Twelve Holstein dairy cows were monitored to collect 12 862 activity data from four types of sensors over five months. After confirming similar cyclic activity patterns from the sensors through correlation and regression analyses, the gamma distribution was employed to calculate the cumulative probability of the values of each biosensor. Then, the activity values were assigned to three levels (i.e., idle, normal and active) based on the defined proportion of each level, and the values at each level from the four sensors were compared. The results showed that the number of measurements belonging to the same level was similar, with less than a 10% difference at a specific threshold value. In addition, more than 87% of the heat alerts generated by the internal algorithm of three of the four biosensors could be assigned to the active level, suggesting that the current standardization method successfully integrated the activity measurements from different biosensors. The developed probability-based standardization method is expected to be applicable to other biosensors for livestock, which will lead to the development of models and solutions for precision livestock farming.","PeriodicalId":501199,"journal":{"name":"The Journal of Agricultural Science","volume":"20 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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