Source: TEXAS A&M UNIVERSITY submitted to NRP
IMPROVEMENTS IN SIMULATION MODELING FOR ESTIMATING PATCH AND LANDSCAPE FORAGE BIOMASS ON RANGELANDS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1009337
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Mar 21, 2016
Project End Date
Feb 19, 2021
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
Temple-Blackland Res Cntr
Non Technical Summary
The ability to characterize the productivity of vegetation over large landscapes can be an important component in the assessment of drought impacts, natural resource management options, environmental degradation, wildfire potential, and economic impacts of changing technologies. At the ranch or firm level, assessment of forage productivity for carrying capacity or stocking rate determination is one of the most critical factors for both economic and ecological sustainability. However, the time and resources required to conduct assessments of vegetation productivity over large landscapes are prohibitive, and are not always recoverable. Another complicating factor is that decisions regarding stocking rate may require near real-time information, especially in the face of drought. Vegetation productivity assessment is almost impossible to conduct on a near real-time basis, thus the information needed for stocking rate decisions is not always available when needed. The inability to make stocking rate decisions at critical times could lead to vegetation overuse and lead to degradation. Improvements in computing, along with near real-time production of climate data and remote sensing imagery, offer the opportunity to develop near-real time systems for monitoring vegetation on rangelands. The emergence of technology for estimating precipitation using techniques such as cold cloud, radar, and other remote sensing technology have made spatially explicit climate data, thus increasing their potential for use in near real-time systems. Improved computing has also increased the use of simulation modeling for rangelands. The application of these models includes simulations for hydrology, soil erosion, plant growth, or combinations of these. Many of these models could be modified or adapted for use in real-time monitoring systems. However, more work is needed to improve their ability to predict biomass at the landscape level. Another limitation of simulation models for estimating forage biomass is the ability to simulate animal movement. Elevation, slope, distance to water, and the species composition of the vegetation at the site can influence how different kinds and classes of animals use the landscape. Recent studies, using Global Positioning System (GPS) technology, are offering new insights into how grazing animals use the landscape with regard to specific soil types, rock cover, location of supplements, and location of water. For simulation modeling to be effective, additional study is needed to incorporate the differential use of the landscape by livestock. Lastly, the ability to dynamically model vegetation state and transition changes on rangelands using simulation models is a limiting factor. The capability to model state and transition change within the simulation modeling environment will be extremely important for assessing impacts of climate change, shifts in composition between C3 and C4 plants, carbon sequestration, and assessing impacts to ecosystem services. This capability will also be needed for developing adaptive management strategies and for building next generation early warning systems.
Animal Health Component
60%
Research Effort Categories
Basic
20%
Applied
60%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
12107991070100%
Knowledge Area
121 - Management of Range Resources;

Subject Of Investigation
0799 - Rangelands and grasslands, general;

Field Of Science
1070 - Ecology;
Goals / Objectives
The ability to characterize the productivity of vegetation over large landscapes can be an important component in the assessment of drought impacts, natural resource management options, environmental degradation, wildfire potential, and economic impacts of changing technologies. At the ranch or firm level, assessment of forage productivity for carrying capacity or stocking rate determination is one of the most critical factors for both economic and ecological sustainability. However, the time and resources required to conduct assessments of vegetation productivity over large landscapes are prohibitive, and are not always recoverable. Another complicating factor is that decisions regarding stocking rate may require near real-time information, especially in the face of drought. Vegetation productivity assessment is almost impossible to conduct on a near real-time basis, thus the information needed for stocking rate decisions is not always available when needed. The inability to make stocking rate decisions at critical times could lead to vegetation overuse and lead to degradation. Improvements in computing, along with near real-time production of climate data and remote sensing imagery, offer the opportunity to develop near-real time systems for monitoring vegetation on rangelands. The emergence of technology for estimating precipitation using techniques such as cold cloud, radar, and other remote sensing technology have made spatially explicit climate data, thus increasing their potential for use in near real-time systems. Improved computing has also increased the use of simulation modeling for rangelands. The application of these models includes simulations for hydrology, soil erosion, plant growth, or combinations of these. Many of these models could be modified or adapted for use in real-time monitoring systems. However, more work is needed to improve their ability to predict biomass at the landscape level. Another limitation of simulation models for estimating forage biomass is the ability to simulate animal movement. Elevation, slope, distance to water, and the species composition of the vegetation at the site can influence how different kinds and classes of animals use the landscape. Recent studies, using Global Positioning System (GPS) technology, are offering new insights into how grazing animals use the landscape with regard to specific soil types, rock cover, location of supplements, and location of water. For simulation modeling to be effective, additional study is needed to incorporate the differential use of the landscape by livestock. Lastly, the ability to dynamically model vegetation state and transition changes on rangelands using simulation models is a limiting factor. The capability to model state and transition change within the simulation modeling environment will be extremely important for assessing impacts of climate change, shifts in composition between C3 and C4 plants, carbon sequestration, and assessing impacts to ecosystem services. This capability will also be needed for developing adaptive management strategies and for building next generation early warning systems.ObjectivesThe following are an integrated set of objectives defining the research approach that will be used for improving simulation modeling to estimate patch and landscape forage biomass on rangelands:Objective 1. Continue the examination of remotely-sensed high resolution climate products for use in near real-time simulation modeling on rangelands through the comparison of the variables predicted by the remotely-sensed climate products to that measured with meteorological instruments on rangeland study sites.Objective 2. Analyze the suitability for using moderate resolution vegetation indices for use in mapping rangeland biomass at the patch (e.g., homogenous unit of a plant community) and landscape (a mosaic of patches over a large area) level.Objective 3. Develop improvements in the APEX model to incorporate animal movement and vegetation state and transition models, and conduct verification of model outputs in space for time substitution studies, and through collaborative studies with participating ranches and research sites.
Project Methods
For Objective 1, research will be conducted to examine correspondence between climate variables measured at the site with traditional weather stations and that predicted with remotely-sensed, high resolution climate products produced by NOAA and NASA. At participating ranches and research areas, weather stations will be established to collect rainfall and other climate variables. Data will be stored on microloggers in order to collect hourly data and to build a temporal dataset for comparisons with currently available data, as well as any new products that become available during this study period. Comparisons with current near-real time products will include CMORPH (Joyce et al. 2004), QMORPH (http://www.cpc.noaa.gov/products/janowiak/cmorph_description.html) and Next Generation (NEXRAD) Radar (http://water.weather.gov/) and the Climate Prediction Center (CPC) unified daily gauge analysis http://www.cpc.ncep.noaa.gov/products/precip/realtime/GIS/USMEX/USMEX-precip.shtml). Since the majority of the climate products represent pixel sizes on the order of 4 km to 30 km in size, an examination of sub-pixel variability is needed to understand variability of climate (especially rainfall) within the climate pixels across the landscape. To examine this for rainfall, 5 to 10 tipping bucket rain gauges with small microloggers will be randomly placed within a randomly selected rainfall grid to gather sub-pixel precipitation information for a specific grid during a 3-month long period. At the end of each 3-month period, the rain gauges will be moved to another randomly selected grid. This will be done for a 3-year period.For objective 2, the MODIS Vegetation Indices products (Huete et al. 2002) will be examined for use in empirical determination of rangeland herbaceous biomass (forage) production, as well as used as a covariate in geostatistical analyses to produce landscape maps of forage. The MODIS products include the NDVI and the EVI. They are produced at 250 m, 500 m, and 1km resolutions.At a study site in the Trans Pecos region of Texas, representative sites (transects) have previously been established for collection of plant community data and biomass production data for remote sensing studies and to parameterize the PHYGROW simulation model for use in real-time prediction of forage biomass. For examining empirical models between biomass production and remote sensing indices, the forage biomass collected at monitoring sites will be co-located with EVI and NDVI to develop regression relationships between the two variables. If the regression relationship is robust, the regression will be applied to the remote sensing data for the entire study area to produce landscape maps of forage biomass. To examine simulation modeling output as a covariate with remote sensing indices, the output from the simulation model for each of the representative sites will be subjected to the geostatistical method of cokriging to determine the feasibility of mapping herbaceous biomass at the landscape scale. For the cokriging analysis, the forage biomass will serve as the primary variable and the MODIS VI products will each be tested separately as the secondary variable to determine their suitability for use in mapping of herbaceous biomass. Herbaceous biomass maps will be created for select 16-day periods during the study. Cross validation will be conducted on both the empirically derived and cokriging derived maps to compare geostatistical predictions to model output and field collected data. Map verification will be conducted by selecting a series of randomly selected grids where forage biomass is collected and compared to map predictions. Statistical analyses will be conducted to assess which MODIS product is most suitable for use in mapping herbaceous biomass, and for use in near real-time and annual stocking rate assessments.For Objective 3, the APEX model will be updated to improve its grazing algorithm to include selective grazing, animal production, and animal movement. The selective grazing components added will allow the model to simulate multi-species livestock grazing based on the preferences of the livestock for the forage resources (Stuth et al. 2003c). Animal production components will be added that allow estimates of weight gain/loss, reproduction efficiency, and milk production as influenced by forage quantity and quality predicted by the model. Animal movement will be incorporated into the model by establishing a grazing suitability index for each polygon or grid being modeled. The grazing suitability index would assign a probability that a given polygon or grid would be grazed based on factors that influence animal grazing such as distance to water, slope, brush thickness, surface rock cover, and location of minerals and supplements. GPS collaring of cattle will be conducted at the Trans-Pecos study site to test the ability of the grazing suitability index at predicting the incidence and frequency of grazing of the polygons being simulated within the study area.Work will continue on developing a framework for including rangeland state and transition models into the APEX modeling environment. As part of this framework, a Bayesian Belief Network will be developed that would allow changes in state to be modeled. Where appropriate, the APEX model output will be used to inform the Bayesian network nodes of the transition conditions that could result in a state change (e.g., amount of basal area, amount of biomass, etc.). For other nodes that cannot be serviced by the model, expert opinion or data from other sources will be used for those transitions (e.g., incidence of fire, mechanical disturbance, etc.). The end product of this would be an example Bayesian Network that incorporates APEX, expert opinion, and data from other sources that can be used to develop an initial publication on the concept and to solicit grant funding for further development and field testing.

Progress 03/21/16 to 02/19/21

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems:Nothing, PI left the organization. What opportunities for training and professional development has the project provided?Conducted several training programs and workshops including train the trainers in US, Brazil, China, Mangolia, and Keyna over the years. How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet and email. Posters were presented at virtual Society for Range Management national meetings on the patch burning work What do you plan to do during the next reporting period to accomplish the goals?Nothing, PI left the organization.

Impacts
What was accomplished under these goals? In collaboration with the United Nations Food and Agriculture (FAO) Organization, the expansion of the water and forage monitoring component of the Predictive Livestock Early Warning System developed by Texas A&M Agrilife Research was expanded to other countries in East Africa (Sudan, South Sudan, Uganda, and Somalia). A new agreement was established in July 2020, which was moved over to a new Principal Investigator in September 2020. Studies were continued in the Texas Hill Country to evaluate patch burning on mesquite-oak savannaecosystems. These studies were implemented to evaluate how mixed livestock herds use areas that have been patch burned compared to unburned areas. Livestock were fitted with GPS collars to monitor use across approximately 2,000 hectares of nativep astures where approximately one fifth of the area was treated with prescribed burns in February 2019. GPS data were collected from cattle, sheep and goats and processed each quarter. Camera traps were also installed in burned and unburned areas to evaluate use by different animal species not outfitted with GPS collars. Photos were collected fromt the cameras every two months, and machine learning algorithms were used to extract photos from the photostream that had animals in them. Livestock nutrition monitoring was continued using fecal near infrared reflectance scanning of manure and hand plucking of vegetation in selected vegetation types. Samples were sent to laboratory for chemical analyses and near infrared scans. This study will be continued into 2021 will additional burns and data collection. As part of work to implement a forage forecasting dashboard, field data was processed from 12 private ranches across Texas and Phygrow model were conducted for monitoring sites The models were evaluation by select producer's to examine the model'sability to predict forage amounts and the usefulness of statistical forecasting. Work continued on building dashboard components to deliver the forage information.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Assessing wildfire burn severity in western United States rangelands from 1984 to 2017. Z Li, XB Wu, J Angerer. AGU Fall Meeting Abstracts 2020, NH008-0004
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Effects of Terrain on Litter Decomposition and Nutrient Release in Typical Steppe of Eastern Gansu Loess Plateau. A Hu, J Angerer, Y Duan, L Xu, S Chang, X Chen, F Hou. Rangeland Ecology & Management 73 (5), 611-618
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Grazing Seasons and Stocking Rates Affects the Relationship between Herbage Traits of Alpine Meadow and Grazing Behaviors of Tibetan Sheep in the QinghaiTibetan Plateau. X Xiao, T Zhang, J Peter Angerer, F Hou. Animals 10 (3), 488


Progress 10/01/19 to 09/30/20

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?COVID-19 restrictions limited ability for training and professional development How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet and email. Posters were presented at virtual Society for Range Management national meetings on the patch burning work What do you plan to do during the next reporting period to accomplish the goals?The PI has left the project, so no work will be done to accomplish goals and the project will be closed out.

Impacts
What was accomplished under these goals? Progress on objectives was hindered by the COVID-19 pandemic after March 1, 2020. In addition, PI left project in August 2020. In collaboration with the United Nations Food and Agriculture (FAO) Organization, the expansion of the water and forage monitoring component of the Predictive Livestock Early Warning System developed by Texas A&M AgriLife Research was expanded to other countries in East Africa (Sudan, South Sudan, Uganda, and Somalia). A new agreement was established in July 2020, which was moved over to a new Principal Investigator in September 2020. Studies were continued in the Texas Hill Country to evaluate patch burning on mesquite-oak savannaecosystems. These studies were implemented to evaluate how mixed livestock herds use areas that have been patch burned compared to unburned areas. Livestock were fitted with GPS collars to monitor use across approximately 2,000 hectares of nativep astures where approximately one fifth of the area was treated with prescribed burns in February 2019. GPS data were collected from cattle, sheep and goats and processed each quarter. Camera traps were also installed in burned and unburned areas to evaluate use by different animal species not outfitted with GPS collars. Photos were collected fromt the cameras every two months, and machine learning algorithms were used to extract photos from the photostream that had animals in them. Livestock nutrition monitoring was continued using fecal near infrared reflectance scanning of manure and hand plucking of vegetation in selected vegetation types. Samples were sent to laboratory for chemical analyses and near infrared scans. This study will be continued into 2021 will additional burns and data collection. As part of work to implement a forage forecasting dashboard, field data was processed from 12 private ranches across Texas and Phygrow model were conducted for monitoring sites The models were evaluation by select producer's to examine the model'sability to predict forage amounts and the usefulness of statistical forecasting. Work continued on building dashboard components to deliver the forage information.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Tolleson, D. R., J. P. Angerer, U. P. Kreuter, and J. E. Sawyer. 2020. Growing Degree Day: Noninvasive Remotely Sensed Method to Monitor Diet Crude Protein in Free-Ranging Cattle. Rangeland Ecology & Management 73:234-242.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Fox, W., J. Angerer, and D. Tolleson. 2019. Conservation Effects Assessment ProjectGrazing Lands: An Introduction to the Special Issue. Rangelands 41:199-204.


Progress 10/01/18 to 09/30/19

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training was conducted on the PHYGROW model for personnel in Texas and Oregon who will be using the model for early warning systems. How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet and email. Posters were presented at Society for Range Management state and national meetings on the patch burning work. What do you plan to do during the next reporting period to accomplish the goals?I will continue to work toward improving technology for simulation modeling on rangelands that includes integrating remote sensing products for improved prediction of forage biomass at the ranch level, drought forecasting at the regional level, and decision support tool development to provide risk management solutions and support for adaptive management. This will include working with the NRCS to develop decision support tools for the NUTBAL model to make use of the new and improved algorithms for livestock growth and supplemental feed use. Additionally, we will be developing the forage outlook dashboard for the NRCS Conservation Innovation Grant project and gathering rancher feedback on the design and outputs. Research in the Grazingland Animal Laboratory will continue with examining the use of fecal DNA analysis for estimating plant species composition inanimal diets and evaluation of anomalous NIRS scans. Lastly, I will work to continue to work with current partnerships in Brazil, Peru, and East Africa for developing and or expanding livestock early warning and livestock market information capabilities to reduce the uncertainty of animal production in these climate-vulnerable areas of the world

Impacts
What was accomplished under these goals? In collaboration with the United Nations Food and Agriculture (FAO) Organization, the expansion of the water and forage monitoring component of the Predictive Livestock Early Warning System developed by Texas A&M AgriLife Research in Kenya was continued. A publication was prepared on the outcome of the pilot projects and published in Weather and Climate Extremes journal. In May, 2019, a presentation was given at the FAO offices in Rome Italy on the Kenya work which was highlighted as an accomplishment under the Texas A&M and FAO memorandum of agreement. Studies were implemented in the Texas Hill Country to begin the evaluation of patch burning on mesquite-oak savanna ecosystems. These studies will evaluate how mixed livestock herds use areas that have been patch burned compared to unburned areas. Livestock were implemented with GPS collars to monitor use across approximately 2,000 hectares of native pastures where approximately one fifth of the area was treated with prescribed burns in February 2019. Camera traps were also installed in burned and unburned areas to evaluate use by different animal species not outfitted with GPS collars. Livestock nutrition monitoring was initiated using fecal near infrared reflectance scanning of manure and hand plucking of vegetation in selected vegetation types. This study will be continued into 2020 will additional burns and data collection. As part of work to implement a forage forecasting dashboard, field data was collected at 12 private ranches across Texas and data were input into the Phygrow model. Simulations were conducted for monitoring sites at these 12 ranches resulting in almost 200 monitoring sites being evaluated. The models were calibrated and will be used in 2020 as part of an evaluation of the model's ability to predict forage amounts and the usefulness of statistical forecasting to give producers a 60 to 90 projection of forage conditions. This information, along with other climate and satellite data, will be used as part of a dashboard being developed for livestock producers.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Fox, W., J. Angerer, and D. Tolleson. 2019. Conservation Effects Assessment ProjectGrazing Lands: An Introduction to the Special Issue. Rangelands 41:199-204.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Matere, J., P. Simpkin, J. Angerer, E. Olesambu, S. Ramasamy, and F. Fasina. 2019. Predictive Livestock Early Warning System (PLEWS): Monitoring forage condition and implications for animal production in Kenya. Weather and Climate Extremes:100209.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Tolleson, D. R., E. C. Rhodes, L. Malambo, J. P. Angerer, R. R. Redden, M. L. Treadwell, and S. C. Popescu. 2019. Old School and High Tech: A Comparison of Methods to Quantify Ashe Juniper Biomass as Fuel or Forage. Rangelands.
  • Type: Book Chapters Status: Published Year Published: 2019 Citation: Fern�ndez-Gim�nez, M. E., A. Allegretti, J. Angerer, B. Baival, B. Batjav, S. Fassnacht, C. Jamsranjav, K. Jamiyansharav, M. Laituri, and R. S. Reid. 2019. Sustaining Interdisciplinary Collaboration Across Continents and Cultures: Lessons from the Mongolian Rangelands and Resilience Project. Collaboration Across Boundaries for Social-Ecological Systems Science: Palgrave Macmillan, Cham. p. 185-225.


Progress 10/01/17 to 09/30/18

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Introduction to PHYGROW and Livestock Early Warning Systems. Federal University of Ceara. Fortaleza, Brazil. 10 Participants How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet, email, and regular mail. What do you plan to do during the next reporting period to accomplish the goals?I will continue to work toward improving technology for simulation modeling on rangelands that includes integrating remote sensing products for improved prediction of forage biomass at the ranch level, drought forecasting at the regional level, and decision support tool development to provide risk management solutions and support for adaptive management. This will include working with the ARS and NRCS to develop decision support tools for the APEX model to make use of the new grazing algorithms to examine effects of conservation practices on vegetation and livestock production, as well as ecosystem services. Additionally, we will be developing the forage outlook dashboard for the NRCS Conservation Innovation Grant project and gathering rancher feedback on the design and outputs. Research in the Grazingland Animal Laboratory will continue with working to improve NIRS sample processing efficiencies and transfer of NIRS calibrations to new instruments, in addition to continuing our work in examining the use of fecal DNA analysis for estimating plant species composition in animal diets and evaluation of anomalous NIRS scans. Lastly, I will work to continue to work with current partnerships in Brazil, Peru, East Africa, Mongolia, and Namibia for developing and or expanding livestock early warning and livestock market information capabilities to reduce the uncertainty of animal production in these climate-vulnerable areas of the world.

Impacts
What was accomplished under these goals? In collaboration with the United Nations Food and Agriculture (FAO) Organization the expansion of the water and forage monitoring component of the Livestock Early Warning System developed by Texas A&M AgriLife Research in Kenya was continued. The system was expanded to include new areas in western, central and southern Kenya. Additional products are being developed to provide the Kenya National Drought Monitoring Authority (NDMA) to use as indicators as part of their national drought contingency and mitigation. These indicators will be used directly for determining if counties will receive emergency response and disaster recovery funding in counties experiencing drought and provide triggers for drought payments. A workshop was held in Rome where agreements were made to continue production of products for NDMA review and action. The selective grazing components that were included in the APEXmodel to assess if incorporating selective grazing resulted in a change in species selection by grazers, overall diet quality, nutrient excretion, and standing vegetation residue. Simulations were conducted based on the conditions reported for a 20-year historic grazing treatment conducted in Kansas. Two grazer types (naïve and selective) and 3 stocking densities (high, medium, low) were evaluated. For the simulations, the Naïve grazers represented model behavior prior to the selective grazing modifications. For the selective grazers, forage species were selected based on crude protein content, digestibility, and anti-quality factors. Results of the simulations indicated that the new selective grazing algorithm resulted in a change in grazer diet quality, nutrient excretion, and standing vegetation residue. Differences between selective and naïve grazers varied over the course of the grazing season, with greatest differences occurring early in the year. Results indicated that C3 and C4 grasses had differential preferences with C3 grasses generally preferred in the spring and C4 grasses preferred through summer. Diet quality for the simulations was relatively insensitive to stocking density, although stocking density did impact standing vegetation residue. Intra-year variation in urinary and fecal N excretion varied across seasons and appeared to be influenced most by weather-induced plant water stress. The revised model's ability to simulate differences in vegetation residue, diet quality, and nutrient excretion provide necessary building blocks to use or improve the model for the simulation of animal production, sediment erosion, water runoff, N and P runoff, and soil C accretion.

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Fernández-Giménez, M. E., Allington, G. R., Angerer, J., Reid, R. S., Jamsranjav, C., Ulambayar, T., . . . Altanzul, T. (2018). Using an integrated social-ecological analysis to detect effects of household herding practices on indicators of rangeland resilience in Mongolia. Environmental Research Letters, 13(7), 075010.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Jamiyansharav, K., Fernández?Giménez, M. E., Angerer, J. P., Yadamsuren, B., & Dash, Z. (2018). Plant community change in three Mongolian steppe ecosystems 1994⿿2013: applications to state?and?transition models. Ecosphere, 9(3).
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Zilverberg, C. J., Angerer, J., Williams, J., Metz, L. J., & Harmoney, K. (2018). Sensitivity of diet choices and environmental outcomes to a selective grazing algorithm. Ecological Modelling, 390, 10-22.


Progress 10/01/16 to 09/30/17

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Experimental Approach to Modeling Grassland Ecosystems. Graduate and Undergraduate Short Course.Federal Univerisity of Mato Grosso.Sinop, Mato Grosso State. Brazil.Two week short course for 15 graduate and 5 undergraduate students. November 2017. PHYGROW simulation modeling.Wallowa Resources. Wallowa Oregon.June 2017. 10 participants. Nutritional Monitoring with the NUTBAL System. Worland, Wyoming.April 2017.25 participants. How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet, email, and regular mail. What do you plan to do during the next reporting period to accomplish the goals?For objective 1,we will focus on the continued validation of the PHYGROW rangeland model and incorporation of model outputs into a risk management decision support system. Under funding from a USDA Conservation Innovation Grant, the PHYGROW model will be used as the foundation for a forage early warning and forecasting decision support tool thatwill bedeveloped for livestock producers.Our team would work with 10-20 livestock producers across Texas to implement PHYGROW simulations for the dominant plant communities on their properties to track forage conditions over time and provide a short-term forecast of likely forage conditions.This information, along with climate and remote sensing data, will be incorporated into a dashboard for producers to use in tracking changes and provide early warning of emerging conditions.Our team will use surveys and analytics of the decision support system use to assess information access by producers and any changes in management that may result from use of the system over time. For objective 2, the first phase in developing a national drought early warning system based on remote sensing data for Namibia has been completed. During this year, we will be working to develop proposals for continued funding, and completing a manuscript comparing various rainfall products to data collected from rain gauges in Namibia to provide information on the usefulness of each rainfall product for early warning. We are also working to complete an NDVI-based biomass prediction model to use for mapping biomass on the landscape. If successful, we will report the findings of this study in a remote sensing journal.

Impacts
What was accomplished under these goals? In collaboration with researchers from Kansas and University of Maryland, the Grazingland Nutrition Laboratory conducted an analysis of the spatial and temporal trends in crude protein on more than 36,000 samples received by the GANLAB for forage quality analysis since 1995.Results indicated that, after correcting for spatial and temporal variations in quality due to effects of drought, crude protein in diets has been declining during this period which could be attributed to elevated atmospheric CO2, increased climatic warming, and loss of nutrients through animal export.If trends continue, increased supplemental feeding may be required to maintain production at current levels. In economic terms, the replacement costs of reduced protein provision to US cattle are estimated to be the equivalent of $1.9 billion. Given these trends, nitrogen enrichment of grasslands might be necessary if further reduction in protein content of forages is to be prevented

Publications

  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Craine, J. M., Elmore, A., & Angerer, J. P. (2017). Long-term declines in dietary nutritional quality for North American cattle. Environmental Research Letters, 12(4), 044019.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Fox, W. E., Medina-Cetina, Z., Angerer, J., Varela, P., & Chung, J. R. (2017). Water Quality & natural resource management on military training lands in Central Texas: Improved decision support via Bayesian Networks. Sustainability of Water Quality and Ecology, 9, 39-52.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Herrick, J. E., Karl, J. W., McCord, S. E., Buenemann, M., Riginos, C., Courtright, E., . . . Brown, J. R. (2017). Two new mobile apps for rangeland inventory and monitoring by landowners and land managers. Rangelands, 39(2), 46-55.
  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Zilverberg, C. J., Williams, J., Jones, C., Harmoney, K., Angerer, J., Metz, L. J., & Fox, W. (2017). Process-based simulation of prairie growth. Ecological Modelling, 351, 24-35.
  • Type: Book Chapters Status: Published Year Published: 2017 Citation: Tedeschi, L. O., Fonseca, M. A., Muir, J. P., Poppi, D. P., Carstens, G. E., Angerer, J. P., & Fox, D. G. (2017). A glimpse of the future in animal nutrition science. 2. Current and future solutions. Revista Brasileira de Zootecnia, 46(5), 452-469.


Progress 03/21/16 to 09/30/16

Outputs
Target Audience:Livestock producers and agriculture risk management professionals Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training conducted: Introduction to PHYGROW and Livestock Early Warning Systems. EMBRAPA Caprinos y Ovinos, Sobral, Brazil. 8 Participants. July 2016. Nutritional Monitoring with the NUTBAL System. Texas A&M AgriLife Research. 8 Webinars, 1 held each week in April and May 2016. 255 participants. How have the results been disseminated to communities of interest?For the early warning system work, maps and situation reports are delivered via internet, email, and regular mail. What do you plan to do during the next reporting period to accomplish the goals?For objective 1, data from weather stations will continue to be collected and compared to remote sensing rainfall products. Field data will be collected to evaluate if vegetation change has occurred at monitoring sites. For objective 2, map products that use MODIS products for products to assess drought and winter disasters will continueto used and improved for Mongolia, Namibia, and East Africa. Workshops with livestock producers will be conducted to receive feedback on the products and to assist with disaster planning. For objective 3, verification of the new algorithms for diet quality estimation and preferential grazing in the APEX model will be carried out for Texas, Arizona, and South Dakota.

Impacts
What was accomplished under these goals? A letter of agreement was established with the United Nations Food and Agriculture (FAO) Organization to expand the water and forage monitoring component of the Livestock Early Warning System developed by Texas A&M AgriLife Research in Kenya. The system was expanded to include new areas in northern Kenya and a predictive component was added to forage monitoring. The new system was launched and the Kenya Drought Monitoring Authority has agreed to adopt the system's forage and water monitoring indicators as part of their national drought contingency and mitigation. The outputs from the Livestock Early Warning System will be used directly for determining if counties will receive emergency response and disaster recovery funding in counties experiencing drought.

Publications

  • Type: Book Chapters Status: Published Year Published: 2016 Citation: ANGERER, J. P.; FOX, W. E.; WOLFE, J. E. Land Degradation in Rangeland Ecosystems. In: (Ed.). Biological and Environmental Hazards, Risks, and Disasters: Academic Press, 2016. p.277-311.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Berg, M. D., Popescu, S. C., Wilcox, B. P., Angerer, J. P., Rhodes, E. C., McAlister, J., & Fox, W. E. (2016). Small farm ponds: overlooked features with important impacts on watershed sediment transport. JAWRA Journal of the American Water Resources Association, 52(1), 67-76.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Berg, M. D., Wilcox, B. P., Angerer, J. P., Rhodes, E. C., & Fox, W. E. (2016). Deciphering rangeland transformation⿿complex dynamics obscure interpretations of woody plant encroachment. Landscape ecology, 31(10), 2433-2444.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Craine, J. M., Angerer, J. P., Elmore, A., & Fierer, N. (2016). Continental-scale patterns reveal potential for warming-induced shifts in cattle diet. PloS one, 11(8), e0161511.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2016 Citation: Fernandez-Gimenez, M. E., Venable, N. H., Angerer, J., Fassnacht, S., & Jamyansharav, K. (2016). Ecological-cultural feedbacks in Mongolian social-ecological systems. Paper presented at the Proceedings of the X International Rangeland Congress.