Progress 03/01/19 to 02/29/24
Outputs
Target Audience:Developed LINC 2.0 - Enhanced Goat Management and Education Tool is being used extensively throughout the world. In addition to a valuable tool for researchers, technology transfer personnel, and goat producers, it also serves as a teaching aid in college classes as well as in international development training functions. Langston University students and visiting scientists were actively involved in all aspect of the project namely designing, development, troubleshooting and testing of LINC 2.0. Undergraduate students have been providing LINC training to their peers studying Animal Science and help train clientele during annual Goat Field Day organized in April of each year and other training/demonstration sessions. LINC demonstrations and training have been provided to individual farmers, farmers groups and organizations in USA and around the world. In person training has been provided during Oklahoma Goat Field Days that are held on last Saturday each April; Climate Smart Conference held in Tuskegee University; conferences held at the College of Agriculture, Science and Education, Port Antonio and Rural Agricultural Development Authority, Jamaica; El Bachir El Ibrahimi University in Bordj Bou Arreridj and the University of Ferhat Abbas, Setif, Algeria, Warsaw Agricultural University, Warsaw, Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland. Online training sessions have been provided as the way to provide additional training experience for individuals and small groups. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Developed LINC 2.0 - Enhanced Goat Management and Education Tool is being used extensively throughout the world. In addition to a valuable tool for researchers, technology transfer personnel, and goat producers, it also serves as a teaching aid in college classes as well as in international development training functions. Langston University students and visiting scientists were actively involved in all aspect of the project namely designing, development, troubleshooting and testing of LINC 2.0. Undergraduate students have been providing LINC training to their peers studying Animal Science and help train clientele during annual Goat Field Day organized in April of each year and other training/demonstration sessions. How have the results been disseminated to communities of interest?LINC demonstrations and training have been provided to individual farmers, farmers groups and organizations in USA and around the world. In person training has been provided during Oklahoma Goat Field Days that are held on last Saturday each April; Climate Smart Conference held in Tuskegee University; conferences held at the College of Agriculture, Science and Education, Port Antonio and Rural Agricultural Development Authority, Jamaica; El Bachir El Ibrahimi University in Bordj Bou Arreridj and the University of Ferhat Abbas, Setif, Algeria, Warsaw Agricultural University, Warsaw, Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland. Online training sessions have been provided as a way to provide additional training experience for individuals and small groups. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
As planned new means of predicting positive and negative feedstuff associative effects in goats were developed and published (https://doi.org/10.1016/j.smallrumres.2024.107234). Langston Interactive Nutrient Calculation System (LINC) was updated to LINC 2.0 by improving accuracy of adjustments for positive and negative feedstuff associative effects in the supplemental concentrate and total mixed ration calculators. Additionally, Raspberry PI 4 and 5, barebone computers were equipped with ubuntu operating system, server setup, wifi network, and LINC 2.0 software. Such e system will be very beneficial where there is no access to internet and electricity. Due to low power requirement Raspberry PI based LINC system can be used with solar panel and provide access to LINC 2.0 in the field condition. Several training sessions using LINC have been provided for undergraduate students, visiting scientists, individual farmers, and farmer associations. Moreover, there has been LINC 2.0 training for international projects in Jamaica (College of Agriculture, Science and Education; Rural Agricultural Development Authority), Algeria (Ferhat Abbas University of Satif; University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj), Poland (Warsaw Agricultural University; Polish Society of Veterinary Sciences), China (China Agricultural University, Beijing Campus) and Japan (Kyoto University). There has been training provided for the American Cashmere Association and at events such as the annual Small Ruminant and Small Farmer Conferences of Langston University and in undergraduate student courses. LINC was incorporated into undergraduate students classes. Support for LINC 2.0 is provided in foreign languages and access to LINC 2.0 is regularly tested to assure uninterrupted access. Recently,Spanish Goat Registry recommended LINC as excellent tool to increase animal productivity and profitability.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Tsukahara, Y.; Gipson, T. A.; Puchala, R.; Goetsch, A. L. Prediction of Associative Effects between Forages and Supplements in Goats. Small Ruminant Research 2024, 107234. https://doi.org/10.1016/j.smallrumres.2024.107234
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Progress 03/01/22 to 02/28/23
Outputs
Target Audience:The broad target audience for outcomes of this project includes researchers in animal science, extension specialists, technology transfer and outreach personnel, livestock producers, teachers, agricultural undergraduate and graduate students in the USA and other countries. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The PD, Co-PD, Visiting Scholars, and undergraduate students have received training and professional development in this project in methods of searching the literature, construction of the new database for addressing associative effects between feedstuffs, and basic statistics and statistical computer programming. How have the results been disseminated to communities of interest?Several training sessions using Langston Interactive Nutrient Calculation System have been provided for undergraduate students, visiting scientists, individual farmers, and farmers associations. Recent trainings include classes for undergraduate students, presentation during Langston University Small Ruminants Field Day, zoom and in person activities for Jamaican Farmers, presentation for International Cashmere Goat Association. What do you plan to do during the next reporting period to accomplish the goals?Recent findings concerning negative and positive associative effect will be incorporated into Langston Interactive Nutrient Calculation System. Outreach for Increased LINC 2.0 Usage) activities, both versions of the current LINC system (Raspberry PI and web-based) will be used to train researchers, producers, and students.
Impacts
What was accomplished under these goals?
Equations to predict feedstuff associative effects in goats were developed with a database constructed through a literature survey. The database was divided into three datasets based on the forage CP concentration. Model development employed two statistical methods and achieved interpretable equations for the datasets. Equations for each dataset resulted in relatively high goodness of fit. Forage NDF concentration was the first selected variable for the Low-quality basal forage dataset, forage OM digestibility was selected for the Moderate-quality basal forage dataset, and supplement ME intake was selected for all the prediction equations developed in this study. Incorporation of these equations in tools such as LINC should improve the estimation of supplemental concentrate needs as well as the formulation of least-cost total mixed rations. The manuscript was prepared and will be submitted for publication within the next month. Work on upgrading Langston Interactive Nutrient Calculation System LINC to LINC 2.0 continues. There are two completely independent versions of the current LINC calculation packages available. There is a web-based version that is intended for users with internet access. In addition, there is a Raspberry PI version for users with no or limited internet access. The Raspberry PI mobile server creates its own network to provide access to tablets, phones, and laptops using solar power, which means that this can be used in developing countries where internet or electricity conditions can be suboptimal. Both versions of LINC are implemented through an ubuntu server, which allows quick updates of both completely independent systems dedicated to different audiences. Feedback from users (students, visiting scientists, clientele) is constantly incorporated into LINC software to improve user experience. There are two main versions of the nutrient requirement calculators available, Professional and Producer-oriented versions. Modifications have been made to the Professional Version Calculator because of its considerable usage and much greater utility relative to the Producer version. The Professional version consists of two 'diet formulation' modules and individual calculators for determination of energy and protein requirements, feed intake prediction, and calcium and phosphorus needs for animals of different breeds, production types, and ages. Diet formulation modules are a 'Estimating Supplemental Concentrate Needs' calculator system currently with the original equations for addressing feedstuff associative effects and a markedly upgraded 'Total Mixed Ration Calculator,' sometimes referred to as 'Least Cost Ration Balancer.' The latter system allows users to determine least cost diets and set minimum and maximum percentages of feedstuffs and supplements. The system now includes options for setting several limits that allow the determination of not only least cost rations but also ones optimal for particular types of animals.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Tsukahara, Y., Puchala, R., and Goetsch, A. 2021. Predicting feedst9uff associative effects in goats. Oklahoma Research Day. Cameron State University, Lawton, Oklahoma. March 5-19.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Tsukahara, Y., Gipson, T., Puchala, R., and Goetsch, A.L. 2022. Selection methods for models to predict feedstuff associative effects in goats. J. Anim. Sci. 99(Suppl. 2). doi.org/10.1093/jas/skab096.072.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Prediction of associative effects between forages and supplements in goats.Yoko Tsukahara, Terry A. Gipson, Ryszard Puchala, and Arthur L. Goetsch
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Progress 03/01/21 to 02/28/22
Outputs
Target Audience:The broad target audience for outcomes of this project includes researchers in animal science, extension specialists, technology transfer and outreach personnel, livestock producers, teachers, agricultural undergraduate and graduate students in the USA and other countries. Changes/Problems:There have not been changes or problems in this reporting period affecting the scope of the objectives. Although, as mentioned above, the Visiting Scholar working on the project left and a scientist will join the team as a Visiting Scholar in the summer for a 1-year sabbatical period. What opportunities for training and professional development has the project provided?The PD, Co-PD, Visiting Scholars, and undergraduate students have received training and professional development in this project in methods of searching the literature, construction of the new database for addressing associative effects between feedstuffs, and basic statistics and statistical computer programming. The Visiting Scholar who had been the primary person working on the project resigned to accept a permanent position in her home country. However, four other Visiting Scholars focusing on other projects provided a great deal of assistance to the PD and Co-PD in beta-testing LINC to improve functionality and increase simplicity of usage as noted earlier. Moreover, a researcher who is ideally suited to continue the associative effect work has been identified and has agreed to spend a 1-year sabbatical period here beginning in the summer of 2022. How have the results been disseminated to communities of interest?A 'Goat Nutrition and LINC' workshop was provided as a part of the annual Goat and Hair Sheep Field Day on March 14, 2021 held virtually. There were participants from the USA, Jamaica, Madagascar, Venezuela, and Japan. Attendees from Jamaica were from the College of Agriculture, Science and Education and Rural Agricultural Development Authority participating in a collaborative project with the American Institute for Goat Research of Langston University entitled 'Targeted Research and Extension Activities for Enhanced Goat Production in Jamaica'. An abstract and oral presentation describing the analyses conducted thus far are to be presented at the International Conference on Goats in September, 2022 in Hungary. What do you plan to do during the next reporting period to accomplish the goals?As a part of Phase 3 (Outreach for Increased LINC 2.0 Usage) activities, both versions of the current LINC system (Raspberry PI and web-based) will be used to train researchers, producers, and students. As noted above, training is ongoing for personnel of the College of Agriculture, Science and Education and Rural Agricultural Development Authority in Jamaica, with the first of two upcoming virtual workshops scheduled for March 10, 2022. Personnel of the China Agricultural University also will attend. The Cashmere Goat Association of the USA has requested a training workshop as well, which is scheduled for March 31. Visiting Scholars and students will continue training and testing of both platforms for further enhancements in function and simplicity in use.
Impacts
What was accomplished under these goals?
Some preliminary findings thus far from the project are described in Tsukahara et al. (2020, 2021). For example, the equation of Tsukahara et al. (2020) explaining the most variation (R2 = 0.77) and with the least bias was MEIMBWFOR = 155.9 ± 135.75 - (0.5403 ± 0.04381 × MEIMBWSUP) - (41.0063 ± 20.71841 × PTCPFOR) + (5.3497 ± 2.31608 × AOMDIGFOR) + (0.74056 ± 0.345002 × PTCPFOR × AOMDIGFOR) (P values of 0.253, <0.001, 0.050, 0.023, and 0.034, respectively), with ME = metabolizable energy, BW = body weight, CP = crude protein, OM = organic matter, MEIMBWFOR = ME intake from forage in kJ/kg BW0.75, MEIMBWSUP = ME intake from concentrate or supplement in kJ/kg BW0.75, PTCPFOR = CP concentration in dietary forage (%), and AOMDIGFOR = OM digestibility of forage without supplemental concentrate (%). The difference between observed and predicted MEIMBWFOR (379 ± 15.0 and 382 ± 12.8, respectively) averaged -3 ± 7.4, with minimum and maximum values of -181 and 167, respectively. The equation for the regression of residual MEIMBWFOR (observed - predicted) against observed values was -99 ± 16.6 + (0.255 ± 0.0404 × MEIMBWFOR) (R2 = 0.27), reflecting some over- and under-predication with low and high MEIBWFOR, respectively. Therefore, efforts will continue to develop more accurate means of prediction. Relatedly, a proposal was submitted to the USDA NIFA 1890 Institution Capacity Building Grant program for a project to conduct experiments for nearly a 100% increase in the number of observations in the database. During this most recent reporting period, considerable attention has been given to improving functionality and increasing simplicity in use of LINC. Change in the server hosting LINC a few years ago resulted in many problems in usage of LINC. These malfunctions of the current version of LINC (http://www.luresext.edu/?q=Nutrient-Calculators) have been solved and user-friendly operation modifications have been made through editing with HTML, JavaScript, and PHP. An new version of LINC consisting of several new and updated modules has been developed. There are two main versions of the nutrient requirement calculators available, Professional and Producer-oriented versions. Modifications have been made to the Professional Version Calculator because of its considerable usage and much greater utility relative to the Producer version. The Professional version consists of two 'diet formulation' modules and individual calculators for determination of energy and protein requirements, feed intake prediction, and calcium and phosphorus needs for animals of different breeds, production types, and ages. Diet formulation modules are a 'Estimating Supplemental Concentrate Needs' calculator system currently with the original equations for addressing feedstuff associative effects and a markedly upgraded 'Total Mixed Ration Calculator,' sometimes referred to as 'Least Cost Ration Balancer.' The latter system allows users to determine least cost diets and set minimum and maximum percentages of feedstuffs and supplements. The system now includes options for setting several limits that allow the determination of not only least cost rations but also ones optimal for particular types of animals. There are two completely independent versions of the current LINC calculation packages available. There is a web-based version at http://40.65.112.141/ that is intended for users with internet access. In addition, there is a Raspberry PI version for users with no or limited internet access. The Raspberry PI mobile server creates its own network to provide access to tablets, phones, and laptops using solar power, which means that this can be used in developing countries where internet or electricity conditions can be suboptimal. Both versions of LINC are implemented through a ubuntu server, which allows quick updates of both completely independent systems dedicated to different audiences.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Tsukahara, Y., Puchala, R., and Goetsch, A. 2021. Predicting feedst9uff associative effects in goats. Oklahoma Research Day. Cameron State University, Lawton, Oklahoma. March 5-19.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Tsukahara, Y., Gipson, T., Puchala, R., and Goetsch, A.L. 2021. Selection methods for models to predict feedstuff associative effects in goats. J. Anim. Sci. 99(Suppl. 2). doi.org/10.1093/jas/skab096.072.
|
Progress 03/01/20 to 02/28/21
Outputs
Target Audience:The broad target audience for outcomes of this project includes researchers in animal science, extension specialists, technology transfer and outreach personnel, livestock producers, teachers, agricultural undergraduate and graduate students in Arabic, Chinese, English, French, Japanese, and Spanish speaking countries/regions. Changes/Problems:There have not been changes or problems in this reporting period affecting the scope of the objectives. What opportunities for training and professional development has the project provided?An undergraduate student of the School of Agriculture and Applied Sciences, Langston University, has received training and professional development in this project. The student participated in all of the activities described above, which includes work with the original LINC database as well as construction of a new database being used to develop means of predicting negative associative effects between feedstuffs. Specific areas in which training has been derived are: 1) basic and ruminant nutrition including nutrient composition (e.g., dry matter, organic matter, crude protein, neutral detergent fiber, acid detergent fiber, acid detergent lignin, ether extract, ash, non-fiber carbohydrates, starch, and total digestible nutrients) of feedstuffs, categorization of forages, legumes, cereal grains, and by-products, different classifications of energy (e.g., gross, digestible, metabolizable, and net energy), physical forms of feedstuffs, and experimental designs for feeding and nutrition research; 2) physiological status of animals, classification/biotype of goats, production stages of small ruminants, impacts of climatic/geographic regions on animal production, management regimen, and feeding strategies; 3) estimation and conversion of nutrient and energy components (e.g., metabolizable energy to digestible energy, nitrogen to crude protein, calorie to joule, and metabolic body weight); 4) the process of searching and identifying (i.e., doi; digital object identifier system and ISBN; international standard book number) literature and literature citation formats; and 5) basic statistics and statistics programming (e.g., normal distribution, linear regression, general linear model, and non-linear model) with SAS and R languages. The student has been providing assistance in the database construction, especially extracting and validating values and calculations from the literature. A Postdoctoral Research Associate of Langston University received training and professional development in this project through the database creation and setting up a headless raspberry pi4 computer using computer languages such as HTML, JavaScript, Python, and PHP on the Ubuntu operation system. She has provided assistance to and training of collaborating researchers, visiting research scientists, and undergraduate students in methods of searching the literature, construction of the new database for addressing negative associative effects between feedstuffs, and basic statistics and statistical computer programming since she started the position in late August in 2019. As the Phase 2 (LINC Enhancement to LINC 2.0) activities, malfunctions of the current version of LINC (http://www.luresext.edu/?q=Nutrient-Calculators) have been solved and user-friendly operation modifications have been made through editing with HTML, JavaScript, and PHP programming languages on the Ubuntu operation system. How have the results been disseminated to communities of interest?The LINC system equipped with a mobile server using the raspberry pi4 computer is being developed. The advantage of this mobile server is that this creates its own network to provide access to tablets, phones, and laptops using solar power, which means this can be used in developing countries where internet or electricity conditions can be suboptimal. Using this system, producers can optimize production and cost efficiency in both quality and quantity. The Postdoctoral Research Associate used this system to provide technical and practical training on usage of LINC for researchers from Namibia on the Cheetah Conservation Fund Program in March, 2020. This system was also used to conduct the 'Goat Nutrition and LINC' workshop on May 14, 2021, via Zoom as a part of the annual Goat and Hair Sheep Field Day. Participants were mainly from the US, Jamaica, Madagascar, Venezuela, and Japan. The system is planned to be used in some other international programs as well. An abstract and poster presentation of the preliminary analysis was presented at the national meeting of the American Society of Animal Science held virtually in July, 2020 and the 2021 Oklahoma Research Day. Another abstract and oral presentation describing the analyses conducted thus far is to be presented at the 2021 Southern Section meeting of the American Society of Animal Science in July in Louisville, KY. Some results from these analyses will be presented at the International Conference on Goats in September 2022 in Hungary. What do you plan to do during the next reporting period to accomplish the goals?As a part of Phase 3 (Outreach for Increased LINC 2.0 Usage) activities, a small (88 × 58 × 19.5 mm) portable computer has been equipped with LINC and its own wireless network as noted earlier. This enables the use of LINC in places/countries without reliable internet access. Relatedly, an updated version of LINC 2.0 will include detailed features of the system incorporating recent research findings, adding more options for different management settings and conditions, and enhanced user-friendly operation. After beta testing and validation, the enhanced and upgraded clone version will be uploaded to the server to replace the current version. Other upcoming activities are to prepare an oral presentation on the aforementioned work for the Southern Section meetings of the American Society of Animal Science in July in Louisville, KY. The manuscript mentioned earlier is to be submitted to an international journal soon.
Impacts
What was accomplished under these goals?
Phase 1 - Associative Effects The references and other materials from the USDA 1890 Institution Capacity Building Grant project entitled "Nutrient requirements of goats: an update and reevaluation" (Project No. 98-38814-6241) were assembled. This is the project that led to development of the Langston Interactive Nutrient Calculation ('LINC') system. There were 1,070 references from feeding and nutrition studies with goats, sheep, and cattle published between 1936 and 2005. Each reference hard copy was classified, sorted, and restored. For archiving and future use, portable document format (pdf) files were obtained from the internet when possible, and others were scanned. The list of references was not available and, thus, was recreated. Some of the database files were corrupted and could not be accessed/opened. The accessed database, which consisted of 1,685 treatment mean observations with 125 variables from 317 references, was evaluated. There were 48 references missing and data from 9 references were duplicates. Differences among publications in analytical methods and variables, and other considerations such as the detail of descriptions, have been challenging factors. A database was constructed for use in a preliminary meta-analysis. Among 72 examined publications, 26 between 1985 and 2018 with data for investigating negative feedstuff associative effects have been compiled from sources such as the Journal of Animal Science, Journal of Dairy Science, Small Ruminant Research, Tropical Animal Health and Production, and Asian-Australasian Journal of Animal Science. The database consists of 135 treatment mean observations representing 511 animals. The variables include animal characteristics such as age, breed, and production stage, supplement type, feeding method, physical form of the diet/feedstuffs, feed composition, concentrations for each supplement and basal forage of constituents such as dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), and energy, and digestibilities of various fractions. In many instances, when not directly available, it has been necessary to calculate some variables based on other information present. As with the LINC system, data are being used to determine concentrations of metabolizable energy (ME) and metabolizable protein (MP). Statistical methods including stepwise regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses have been with the Statistical Analysis System (SAS, Cary, NC). A manuscript has been prepared and is under in-house review, which is close to submission. An abstract and poster presentation on results from preliminary analysis was presented at the annual meeting of the American Society of Animal Science held virtually in July, 2020 and Oklahoma Research Day also held virtually in March 2021. Another abstract describing the analyses conducted thus far was accepted for an oral presentation at the annual meeting of the Southern Section American Society of Animal Science to be held in conjunction with the national meetings in July, 2021.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Tsukahara, Y., Puchala, R., and Goetsch, A. 2020. Predicting feedstuff associative effects in goats. J. Anim. Sci. 98(Suppl.).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Tsukahara, Y., Puchala, R., and Goetsch, A. 2021. Predicting feedstuff associative effects in goats. Oklahoma Research Day. Cameron State University, Lawton, Oklahoma. March 5-19.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Tsukahara, Y., Gipson, T., Puchala, R., and Goetsch, A.L. 2021. Selection methods for models to predict feedstuff associative effects in goats. J. Anim. Sci. 99(Suppl.).
|
Progress 03/01/19 to 02/29/20
Outputs
Target Audience:The broad target audience for outcomes of this project includes researchers in animal science, extension specialists, technology transfer and outreach personnel, livestock producers, teachers, agricultural undergraduate and graduate students in Arabic, Chinese, English, French, Japanese, and Spanish speaking countries/regions. Changes/Problems:There have not been changes or problems in this reporting period affecting the scope of the objectives. What opportunities for training and professional development has the project provided? An undergraduate student of the School of Agriculture and Applied Sciences, Langston University, has received training and professional development in this project starting in the fall semester of 2019. The student participated in all of the activities described above, which includes work with the original LINC database as well as construction of a new database being used to development means of predicting negative associative effects between feedstuffs. Specific areas in which training has been derived are: 1) basic and ruminant nutrition including nutrient composition (e.g., DM, OM, CP, NDF, acid detergent fiber, acid detergent lignin, ether extract, ash, non-fiber carbohydrates, starch, and total digestible nutrients) of feedstuffs, categorization of forages, legumes, cereal grains, and by-products, different classifications of energy (e.g., gross, digestible, metabolizable, and net energy), physical forms of feedstuffs, and experimental designs for feeding and nutrition research; 2) physiological status of animals, classification/biotype of goats, production stages of small ruminants, impacts of climatic/geographic regions on animal production, management regimen, and feeding strategies; 3) estimation and conversion of nutrient and energy components (e.g., ME to DE, nitrogen to CP, calorie to joule, and metabolic body weight); 4) the process of searching and identifying (i.e., doi; digital object identifier system and ISBN; international standard book number) literature and literature citation formats; and 5) basic statistics and statistics programming (e.g., normal distribution, linear regression, general linear model, and non-linear model) with SAS and R languages. The student has been providing assistance in the database construction, especially extracting and validating values and calculations from the literature. A Postdoctoral Research Associate arrived at Langston University in August, 2019, to work on the project. The Research Associate has received training and professional development in a variety of areas, including database creation and setting up a headless raspberry pi4 computer as well as computer languages such as HTML, JavaScript, and Python. The Research Associate also has provided assistance to and training of others, including collaborating researchers, visiting research scientists, and undergraduate students in methods of searching the literature, construction of the new database for addressing negative associative effects between feedstuffs, and basic statistics and statistical computer programming. Relating to the Phase 2 (LINC Enhancement to LINC 2.0) activities, some issues in how the current version of LINC (http://www.luresext.edu/?q=Nutrient-Calculators) functions have been noted by users and communicated to personnel involved in this project. Some minor issues have been solved through editing with HTML programming by the Postdoctoral Research Associate. Functions relating to mathematical operations will be addressed in the future. How have the results been disseminated to communities of interest?Abstracts of the preliminary analysis will be submitted for the 2020 national meeting of the American Society of Animal Science in July in Madison, WI and the International Conference on Goats in October, 2020 in Hungary. What do you plan to do during the next reporting period to accomplish the goals?Work to expand the new database for use to predict negative feedstuff associative effects will continue. The database will be enhanced by inclusion of treatment mean observations from journals such as Animal Feed Science and Technology, Livestock Production Science/Livestock Science, Journal of Applied Animal Research, and The Professional Animal Scientist/Applied Animal Science. Moreover, efforts will be initiated to develop a corresponding database to address positive associative effects between high-protein supplements and low-protein basal forages. When the database is of sufficient size, equations to estimate associative effects will be developed with consideration of influences on associative effects of factors such as the production stage of animals, feeding method, and physical form of feedstuffs. A relatively smaller database will be used for evaluation of the prediction methods arising from the development database. As a part of Phase 3 (Outreach for Increased LINC 2.0 Usage) activities, a small (88 × 58 × 19.5 mm) portable computer has been equipped with LINC and its own wireless network. This enables the use of LINC in places/countries where have no reliable internet access. Relatively, the Postdoctoral Research Associate will use this system to provide technical and practical training on usage of the LINC for researchers from Namibia on the Cheetah Conservation Fund Program in March, 2020. A clone of the current version of LINC will be used to address mathematical function issues and new or modified calculations. After beta testing and validation, the enhanced and upgraded version will be uploaded to the server to replace the current version available on the Institute's website.
Impacts
What was accomplished under these goals?
Phase 1 - Associative Effects The references and other materials from the USDA 1890 Institution Capacity Building Grant project entitled "Nutrient requirements of goats: an update and reevaluation" (Project No. 98-38814-6241) were assembled. This is the project that led to development of the Langston Interactive Nutrient Calculation ('LINC') system. There were 1,070 references from feeding and nutrition studies with goats, sheep, and cattle published between 1936 and 2005. Each reference hard copy was classified, sorted, and restored. For archiving and future use, portable document format (pdf) files were obtained from the internet when possible, and others were scanned. The list of references was not available and, thus, was recreated. Some of the database files were corrupted and could not be accessed/opened. The accessed database, which consisted of 1,685 treatment mean observations with 125 variables from 317 references, was evaluated. There were 48 references missing and data from 9 references were duplicates. Differences among publications in analytical methods and variables, and other considerations such as the detail of descriptions, have been challenging factors. A new database is being constructed for use in a preliminary meta-analysis. Currently more than 60 publications with data for investigating negative feedstuff associative effects have been compiled from sources such as the Journal of Animal Science, Journal of Dairy Science, Small Ruminant Research, Tropical Animal Health and Production, and Asian-Australasian Journal of Animal Science. The literature from 1988 to 2020 has been selected and the database now consists of approximately 500 treatment mean observations with a large number of variables. The variables include animal characteristics such as age, breed, and production stage, supplement type, feeding method, physical form of the diet/feedstuffs, feed composition, concentrations for each supplement and basal forage of constituents such as dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), and energy, and digestibilities of various fractions. In many instances, when not directly available, it has been necessary to calculate some variables based on other information present. As with the LINC system, data are being used to determine concentrations of metabolizable energy (ME) and metabolizable protein (MP). The statistical analyses are being conducted with the Statistical Analysis System (SAS, Cary, NC). Once the preliminary analysis is completed with the determination of appropriate expressions of negative feedstuff associative effects, then the database will be expanded for more accurate prediction of associative effects. In addition, there will be attention given to prediction of positive associative effects when high-protein supplemental feedstuffs increase the level of intake and(or) digestibility of low-protein basal forage. Abstracts based on these initial findings will be submitted for presentation at the annual national meeting of the American Society of Animal Science in July, 2020 and the International Conference on Goats in October, 2020.
Publications
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