Range Measurements RS 332
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This 34 page Class Notes was uploaded by Marietta Wisozk on Monday September 21, 2015. The Class Notes belongs to RS 332 at Colorado State University taught by Staff in Fall. Since its upload, it has received 52 views. For similar materials see /class/210047/rs-332-colorado-state-university in Rangeland Ecosystem Science at Colorado State University.
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Date Created: 09/21/15
Monitoring Frequency amp Density Vegetation Attribute An inherent characteristic of the vegetation at a site The number oftimes a plant species is present within a given number of sample quadrats of uniform size placed across a stand of vegetation The percentage of possible quadrats within a sampled area occupied by the target species 39 EXAMPLES Frequency Density Cover Biomass y Rangeland Measurements amp Monitoring RS332 Frequency Frequency Presence only not number or size Function of quadrat size DO NOT compare frequencies gathered in quadrats of different sizes Function of both density and dispersion Do not need to de ne a counting unit What s the frequency What s the frequency Frequency USES Monitoring changes over time in one location Comparing between locations and management histories Example monitoring for invasions of nonnative species NOT USEFUL FOR Descriptive studies unless accompanied by other attributes Evaluating plantvigor production ordominance Frequency Sampling Design Random or Systematic Systematic used more often because More efficient Easier to mark permanent plots more powerful for detecting change Sampling unit is the transect Frequency Example Sand Wash OHV Study BLM land in NW Colorado Area with unregulated OHV recreation Question Do invasive species have higher frequency adjacent to OHV roads Attribute to be measured CoverLinePoint Frequency Freuency Example Frequency Example Interested in invasive species Halogeton Cheatgrass and Yellow allysum Results No difference found between road and core Wdespreadthroughout Management Implications No clear relationship between OHV and invasive specnes Confounding factors ku Horses FrequencyAdvantages Objective Fast Simple Low sensitivity to periodic fluctuations stable throughout growing season No need to distinguish individuals Function of both density and dispersion detects change in plant abundance amp distribution FrequencyDisadvantages Function of both density amp dispersion difficult to interpret Data nonabsolute Values depend on quadrat size Not wellsuited to large shrubs or rare plants Not easy to visualize or estimate visually Frequency Sampling Considerations 1 Plot size determines frequency Scenarici l Frequency tcirnatcies 25 Scenarici 2 Frequency tcirnatcies 55 Frequency Sampling Consideration 2 Need to allow for detection of chan e Desire 3070 ofquadrats with species of interestg WW Scenarici 2 Frequency cinicins Scenarici l Frequency tcirnatcies 25 4 OK 45 a Hard tci detect cnanqe Frequency Sampling Consideration 3 Number of sample units Depends on the variation ofthe vegetation Area under consideration 7 HOWEVER 1007200 quadrats permacroplot is typical Bonham p 96 25 transects of25 quadrats each Frequency Sampling Consideration 4 Don t calculate composition from freguency data Frequency has no consistent relationship with species abundance density or ecological dominance estimated by cover or biomass Here frequency data ciyerestirnate tne ccirnpcisiticin cit small Wiqeiy distributed ccirnpcisiticin cit less frequent but larger cir clumped species EOCU PRVE FrequencySummary Density Useful for monitoring but not rigorous Characteristics enough for research Density is A function of density amp dispersion 1 of individuals per unit area mm or Depends on quadrat size 2 Average area per individual m21 Optimal quadrat size yields 3070 Less sensitive to nonlethal change in frequency plant vigor than cover or biomass Must identify the counting unit What s the density What s the density I lZZEI Ern O EZEI 4m2 Density Density USES LESS USEFUL FOR When change expected in recruitment or Very iongived Wants except for monailty 9f InlelduaIS recruitmentmortality or ageclass studies 39 Reseed39ng success Very shortlived plants annuals Plant survival following herbicide treatment Demographic Studies Estimating ecological dominance Baseline inventory of vegetation age structure Comparing species of different sizes or life forms Monitoring conditiontrend if cover amp biomass fluctuate dramatically Density Advantages Easy to understand Can compare across quadrats of different sizes and shapes Disadvantages Must identifyindividuals Boundary amp counting errors may bias Doesn t necessarily indicate ecological dominance Not good for comparing species of different sizes or lifeforms Plot Based Boundary Decisions Frequency and Density 1 Pick sides Density 5 a a 1 Density 6 3 50 Density 5 D Distance Methods for Density Basis Mean area per plant can be estimated Mean distance between plants Mean distance between a point and a plant Mean Area MA the average amount of space available to an individual plant Density 1MA A 1density EXAMPLE If MA 100m2plant then Density 1 plantJ100m2 When to Use Distance Methods Population of discrete individuals Singlespecies studies Unable to use quadrats because Individuals are large Individuals are widely spaced Distance Methods Advantages Simple to use No boundary or counting errors No plot size or shape considerations Disadvantages Inef cient in very dense or sparse vegetation May require large sample size Must know if vegetation is randomly distributed or ped Wrong method can result in severe bias Less accurate than plotbased methods Distance Methods Methods for random populations Closest ind v39dual random point to plant Nearest neighbor plant to nearest neighbor Random pairs plant closest to sample point to the nearest plant behind observer when observer facing the rst plant Pointcentered quarter pointto nearest plant in e 0 of4 90El sectors around sample point Distance Methods Distance Methods for Nonrandom opulations Angleorder method Divide area smaller sectors in which individuals are randomly distributed Wandering quarter method Based on dividing the distances into within clump and betweenclump distances assessing the mean clump diame er and determining the number of clumps DensitySummary Distance based methods Used to monitor woody species by age class Seldom used to monitor herbaceous vegetation Plotbased vs distance methods Useful for demographic studies Key things to remember in the field Be aware of vegetation patterns Establish a common wayto make boundary decisions We will use nested framestoday remember that you want a frame that will have 3070 frequenc Measuring presence amp absence ANNOUNCEMENTS Lecture Today and Tuesday Range condition State and Transition Models Rangeland health Lab today Range condition in classroom Lab Tuesday TWOPART REVIEW Part 1 Synthesize information as a class about vegetation measurement methods Part 2 Studentdriven Q amp A ANNOUNCEMENTS Final Exam on Thursday 100 pts 1 hour and 30 minutes Cumulative Opportunity for you to Masterwhat you struggled with from last exam Incorporate new knowledge from last couple of lectures ANNOUNCEMENTS Remaining points in RS332 course 50 pts Lab Report 3 Due Tuesday 25 pts Pseudo Lab Report 4 Due Thursday 100 pts Final Exam on Thursda 25 pts Course evaluation on Thursday EASY PTS 200 pts remaining Range Condition State and Transition Models Rangeland Health Rangeland Monitoring and Measurements RS amp RS532 Helpful and supportlve readings forlecture rnaterial rhul51949 Smlth etal 1995 vvesto t l 1989 lrlterpretlrlg lrldlcators of Rangeland Health 2005 pg 1 15 Early History of Range Assessment 1895 Jared Smith Survey ofWestem rangelands 1899 HQ Cowles Describes plant succession on Michigan dunes 1905 FE Clements Studies succession on Great Plains 1917 JT Jardine Develops rst scienti c range survey method 1923 AW Sampson Introduces succession as a way to assess grazing capacity Early History of Range Assessment 1930 1934 1935 1936 1949 EJ I Dyksterhuis for assesslng Dyksterhuis 1949 Quantitative Climax Method Range ecological site soil climate topography amp vegetation supports a specific plant community Quantitative assessment of successional status based on proportions of increasers decreasers invaders Dyksterhuis 1949 Quantitative Climax Method Assumptions succession is linear continuous and reversible all disturbances have similar and additive effects Grazing Climax SE E Sueeessmn c Retrugressiun Climax Final or stable biotic community Selfperpetuating In equilibrium with physical habitat Potential Natural Community Historical term indicating the stable vegetation community which could occupy a site under current climatic conditions without further influence by humans Range Condition from Dyksterhuis 1949 ndition Relative coverage quotn N m a m m a m Le a 1D 1DEI 75 5D 25 RANGE CONDlTlON PERCENTAGE Percentage eir climax vegetatiun remaining in punse in years eir grazing Range Condition Generic term Relates present plant community to climax potential natural plant community Range Condition Dyksterhuis 1949 SRM 1982 Range Cond Excellent Climax PNC 76100 Good Late seral 5175 Fair Mid seral 2650 Poor Early seral 0 25 Range Condition Score Critiques of Range Condition 1 Fails to describe or predict vegetation dynamics in some cases eg removal of herbivores did not result in retrogression Critiques of Range Condition 2 Assumes only one possible endpoint for succession instead of multiple steady states Critiques of Range Condition 3 Assumes successional change is linear doesn t account for nonlinear change and thresholds threshold linear relatiunship nunrlinearrelatiunship him as biomass nigh ian high graxing intensity uvv graxing intensity Critiques of Range Condition 4 Assumes that management objective is always climax or PN Critiques of Range Condition 5 Doesn t account for nonnative species Critiques of Range Condition 6 Does not tell us about ecosystem functions only structurecomposition Biodiversity Erosion potential Nutrient cycling Etc Critiques of Range Condition Information on the historic climax plant community or PNC may not exist 1 Global climate change may affect what climax or PNC 00 Question Why hasn t range condition been replaced Success of alternative models of range condition may require an underlying theory linked to a field method to successfully capture the consensus of the range community Linda Joyce 1993 JRM Alternatives to Range Condition Rangeland Health Assessment State and Transition Models History of Rangeland Health 1994 NRC and SRM call for national rangeland assessment using common methodology 1995 BLM amp NRCS identify need for rapid assessment technique while quantitative indicators developed 1997 BLM NRCS amp USFS sign MOU to develop common national range assessment technique History of Rangeland Health 1997 NRCS and BLM develop and then merge 2 separate protocols 2000 Rangeland Health technical reference published 2002 David Pyke and others write review 2005 Most recent Rangeland Health technical reference published Rangeland Health The degree to which the integrity of the soil vegetation water and air as well as the ecological processes of the rangeland ecosystem are balanced and sustained Rangeland Health The degree to which the integrity of the soil vegetation water and air as well as the ecological processes of the rangeland ecosystem are balanced and sustained Rangeland Health Integrity maintenance of the functional attributes characteristic of a locale including normal variability Rangeland Health Determine if rangeland is healthy at risk or unhealthy based on 3 criteria 1 Soil or site stability 2 Hydrologic function 3 Integrity of the biotic community Rangeland Health Soil or site stability The capacity of the site to limit the redistribution and loss of soil resources including nutrients and organic matter by wind or water Rangeland Health Hydrologic function The capacity of the site to capture store and safely release water from rainfall runon and snowmelt to resist a reduction in this capacity and to recover this capacity following degradation Rangeland Health Integrity of the biotic community The capacity of the site to support characteristic functional and structural communities in the context of normal variability and to resist loss of this function and structure caused by disturbance and to recover following each disturbance Rangeland Health Assessment 0 Each criteria has a set of indicators 0 17 indicators in total 0 Each indicator is rated based on degree ofdeparture from ecological site description or reference ara Based on 15 scale from noneslight to extreme o Preponderance of evidence rates site as healthy at ris unhealthy Rangeland Health Indicators Rills Plant community composition as distribution Water flow patterns relative to infiltration amp runoff pedestals and or terracettes Compaction layer Bare ground Functionalstrucmral groups Gullies plant mortalitydecadence gland scoured blowous Litter amount Annual above round Litmr movement production 9 Soil ilrface resemnce m Invasive plane erosion I Re reductive ca abili of Soil ilrface loss or pePemial plantsp ty degradation Rangeland Health Intended Uses Point in time qualitative assessment of rangeland status Communication and education tool By experienced and knowledgeable people Management should not be changed solely on basis of RH assessment NOT intended for monitoring Repeated assessments over time to determine tren Rangeland Health Does Rangeland Health represent a theory linked to a method Can it replace the range condition model as a practical assessment tool BIG ANNOUNCEMENT If you are interested in obtaining graduate level credit for this course it is now available RS 332 and RS 532 Drop one add the other Graduate level REQUIRED reading Rick Karban and Mikaela Huntzinger 2006 How to do Ecology A Concise Handbook Princeton University Press Princeton NJ Graduate Level Assignment Design conduct analyze and submit written report on a monitoring project of our choice Due last day of class October 14 Guidance from Natural Areas Program personnel Rangeland Measurements amp Monitoring RS332 2007 Lectures 24 Introduction to Vegetation Sampling Lecture 2 Populations Sampling Accuracy and Precision Types of Error Sampling is o The process of selecting a part of something with the intent of showing the quality style or nature ofthe whole Providing information about part of a population in such a way that inferences about the whole population may be made Elzinga pg El We sample because Counting whole population is dif cult or impossible Sampling can destroy objects of interest Sampling can give a more accurate estimate of the population than a complete census 7 Fewer measurements 7 More intense measurements 7 More accurate measurements Goals of Sampling Make reliable inferences about whole population by making measurements on a limited number of sample units Determine an estimate of uncertainy associated with inferences Minimize sample size while optimizing accuracy and precision What is a population Biological population an interacting collection of organisms ofthe same species occupying a de ned geographical area Statistical population the set of individual objects sample units about which you want to make inferences What is a sample A sample is a set of sample units on which you have made actual measurementsobservations What is a sample unit One plant One plot used to estimate cover One transect from which you compile frequency data 24681012Wl l81 Elzlnga pg 52 More about populations Population parameters are the true descriptive characteristics ofa population a Assumed to be fixed but unknown quantities a cnange onlyvvnen the population cnanges Sample statistics are descriptive measures from a samp e that are used to estimate population parameters a Vary from sample to sample a cnange when population cnanges Pnpularinn estimate populalmn l inmnal 1351 plants Elzinga pg 62 A Mean 03 PINSquad B Mean 96 plntsquad U stsluuuiars Pg 65 39n Elzinga Population of plants 1 hectare area Total number of plants in population 1000 True mean 1000 plants100 hectares 10 plantshectare Population Sample of 8 plots imate number of individuals in each plot Each plot is 1 hectare Each plot is a sample unit 0 O 0 000 O o 0 0 Population N for population 100 hectare sample units 1000 plants in population a o cq 0 Number of hectares Number of plantshectare N for population 100 hectare sarnple units n for sample 8 hectare sarnple units How does mean for sample relate to mean of pop a o 9 0 Number of hectares Number of plantshectare Say we know the number of plans in those eight plus o o N forpopulanor 100 hectare sample units 3 0 1 7 8 hectare sample units How does mean for sample relate to mean of pop Number ofhectares Number ofplantshectam 0 0 3 INFERENTIAL STATISTICS A Mean 08 plnlsquad B Mean 96 plnlsquad SAMPLING ERROR E e a 4 b o r 3 r g 2 e u amid 14 u m A 2 o e sml nmu u u 15 2 Z 10 12 Number ofplantshectare Pg 55 m E zmga c a o a 9 0 0 on a a 3 3 E E o 0 l a a 4 4 LA LA 2 o o b s E 2 2 s S 5 Z Z 39 Number ofplantshectare 10 12 Number ofplantshectam Accuracy Precision amp Bias Accuracy is the closeness of a measured or computed value to its true value Precision is th repeated measuresto the Not the TRUE quantity just the SAME quantity you measured before Which is most accurate Which is most precise Precision The sample standard deviation s gives a measure of precision or repeatability of our sample but does not assess its accuracy Example Which estimate is more precise 7 plantsm2 s 18 7 plantsm2 s 50 systematic distortion arising from a flaw in measurement or inappropriate method of sampling Bias is Which is are most precise Most accurate Most biased agt bgt LL H a y x c b 0M p u 7 Rangeland Monitoring amp Measurements RS332 Lecture 1 Introduction to Monitoring amp A aptive Management August 2mm Class Survey Name What year freshman soph jun sen other Current and future majorfocus in education What do you want to be when you grow up If you could de ne your best learning environment what would it be Name one cool thing you did this summer ReadingsReferences Elzinga etal1998 l Ch 2 pages 1346 cm Outline De nition of monitoring Monitoring objectives Types of monitoring Types of data Uses for monitoring data Related disciplines Comparecontrast monitoring inventory and research What is monitoring Monitoring is Collection and analysis of repeated measurements to evaluate changes in condition and progress toward meeting a management objective Monitoring is Collection and analysis of repeated measurements to evaluate changes in condition and progress toward meeting a management objective Monitoring is Collection and analysis of repeated measurements to evaluate changes in condition and progress toward meeting a management objective Driven by objectives Set within management context ElZlnga er al 1998 Example Objective healthy riparian system Indicator diverse age classes of native riparian tree species Monitor Example Objective healthy riparian system Indicator diverse age classes of native riparian tree species Monitor abundance density oftrees in each age class seedlings poles mature at regular intervals Another Example Objective healthy watershed function Indicator low soil loss high infiltration Monitor Another Example Objective healthy watershed function Indicator low soil loss high infiltration Monitor Percent of ground covered by vegetation as cover j infiltration j What else Another Example Objective healthy watershed function Indicator low soil loss high in ltration Monitor Percent of ground covered by vegetation as cover 7 in ltration 7 Stream sediment load Objectives Objectives Objectives Clearly stated As detailed as possible Describes predictionestimates response Realistic Specific Measurable What is the advantage Makes you think Allows clear communication with others Provides discrete wellde ned target Helps de ne degree ofsuccess What is Adaptive Management Adaptive Management Cycle Adaptive Management Cycle Adagtive Management Cycle Adaptive Management Cycle Adaptive Management Cycle Adaptive Management Cycle Adaptive Management Cycle What happens w Even glven alternatlve management ablemves aren t met Adaptive Management Cycle Adagtive Management Cycle FEEDBACK Adaptive Management Cycle What is Adaptive Management A systematic process for continually improving management policies and practices by learning from the outcomes of previously employed policies and s practice What is Adaptive Management Two types Passive Active Passive Adaptive Management One best management strategy Monitor outcome Passive Adaptive Management One best management strategy Monitor outcome Change management based on outcome Active Adaptive Management Active Adaptive Management Multiple management strategies Monitor outcomes Discriminate between alternatives Multiple management strategies Monitor outcomes Discriminate between alternatives Change management based on BEST outcome Active Adaptive Management PaSSive VS Active AM Multiple management strategies 03 Evaluate Evaluate Monitor outcomes Discriminate between alternatives Change management based on BEST outcome Involves EXPERIMENTATION Results in accelerated learning 9 6 0 9 6 Co 6 Qualitative vs Quantitative What kind of information do we Qualitative need to provide a reliable basis for management decisions Quantitative Qualitative vs Quantitative Qualitative Descriptive info photos categories ranks Example rangeland health checklist Quantitative Qualitative vs Quantitative Qualitative SEMIquantitative data Descriptive info photos categories ranks Example rangeland health checklist Quantitative Qualitative vs Quantitative Qualitative SEMIquantitative data Descriptive info photos categories ranks Example rangeland health checklist Quantitative Measured enumerated Example counts of plants weighing biomass Subjective vs Objective Subjective Objective Subjective vs Objective Subjective Based on personal judgment expertise Example rangeland health checklist Objective Subjective vs Objective Subjective Based on personal judgment expertise Example rangeland health checklist Objective Repeatable different observers overtime Example counts of plants weighing biomass Why monitor Monitor to Determine effectiveness of management Learn what works what doesn39t Justify making changes Document system variability Meet regulatory requirements Monitoring may also Monitoring may also Establish a record oftrends Help different interests arrive at a shared Reveal potential problems early NdSFStENding the GOOSEStem save money and time Build community trust reciprocity ability to work together Improve permitteeagency relationships What ISN T monitoring What is inventory Inventory Natural history studies Research Anything not driven by objectives and set within management context Inventory is Point in time measurement Determine location and condition of resources Used for planning Distribution of resources Identify resource concerns Identify potential monitoring locations Baseline monitoring data Example Ranch planning inventory ecological sites Note productivity Note condition inventory for improvements Location 5 Conditions and goals Another Example Conservation planning The Nature Conservancy inventory tasks Classify ecological types Analyze spatial distribution and abundance Prioritize conservation efforts Inventory Monitoring Point in time Repeated measures snapsh over ime More measurements Fewer measurements Cruder measurements More precise Characterize total area Attributes related to management objectives Monitoringresearch continuum What is the difference between monitoring and research Scientific Research Conceptual model of system Alternative hypotheses Experimental design 7 Treatments randomly assigned 7 Controls 7 REpilEatlun in space and time Analysis using statistics Results more broadly generalized May advance heory Peer reviewed Researchmonitoring continuum mum t M Ma Researchmonitoring continuum Summary As move from monitoring to research Add controls Increase replication Researchmonitoring continuum 1in mum w Monitoring Research but monitoring can be a subset of research ECOLOGICAL Carrying Capacity Rangeland Measurements amp Monitoring Definition Carrying Capacity Graphical relationship ECOLOGICAL Carrying Capacity ECOLOGICAL Carrying Capacity LonQStanqing idea Number of individuals at point of resource VGI hUISt In 1838 saturationquot where births deaths The early 20 s and 30 s 3mm Leopold E mm mm The 60 s and 70 s me Caug y NoyMeir in the 70 s KAN GAROOS KAN GAROOS What s happening to veg What s carrying capac y McLeod i997 McLeod i997 KAN GAROOS VEGETATION Is this realistic KANGAROOS VEGETATION Takehome message Carrying capacity for grazers dependent Stability of vegetation Stability of growing season Population dynamics for grazers Considerations in Determining Grazing Capacity Thingsyuu can t control Things you can manage Weather M nagernent ubiecrives Watertable Cu ditiun ur furage Ruutzune dep Distributiun ur grazing in Suil characteristics space and ti 2 Tupugrapw Types ur grazing animals iniriai quantity uffurage 39 mpmvemems weed Euntrul fenilizatiu i Seeding irrigariun Drinking Water quality iniriai quality uffurage Drinking Waterdistributiun SRM definition of Carrying Capacity The maximum stocking rate possible which is consistent with maintaining or n the am ar e area due to uctuating forage production SR M isine Sucietylur Range Management ECONOMIC Carrying Capacity Number of individuals that provides OPTIMUM pro t per unit area Economic Carrying Capacity NRC high stacking mte nign g s Ghazkgha g a MW icicnnaiiaai 2 R3 return pci a a s Stacking rate high Impacts on Economic Carrying Capacity Reproduction and mama i acmicamaatiutca mm nanncm mm i i i a i ii n Stocking rate Number of animals per unit area per unit time Amount of land allocated to each animal unit for grazable period of year Animal Unit AU AU a 1000 lb 405kg nonlactating cow or its equivalent in forage demand animal intake Forage demand 2 of body weightday eg 405 kg x 002 81 kgday Animal Unit Month AUM AUM potential forage demand of one AU in 1 month 30 days eg 81kgday x 30 days 243 kg Animal Unit Month AUM AUM potential forage demand of one AU in 1 month 30 days eg 81kgday x 30 days 243 kg is yuur rangeland capable cit sustaining thisfurage intake7 Fur nu imals7 Grazing capacity Animal Unit Equivalent AUE AUE forage demand of a particular kind and class of animal relative to that of AU eg mature nonlactating cow 1 mature bull 15 cowcalf pair 135 ewelamb pair 03 saddle horse 125 mature elk 065 Methods of Estimating Grazing Capacity Forage Inventory Method Utilizationbased Methods Comparison Method Stock and Monitor Method Forage Inventory Method Based on determining air dry weight of forage on a per area basis Adjusted for accessibility slope and distance to water allowable use proper use factor amount of forage that can be removed without limiting plant s future production Forage Inventory Method Stratify grazing unit by vegetation type ecological site Determine total forage production for each type Adjust total forage production for inaccessibility Distance from water Slope M A Forage Inventory Method 5 Determine proper use factor for veg type Lower PUF needed if range in POOR condition 01 Calculate forage demand for species of animal 2 of body weight daily intake 0 Divide usable forageforage demand Grazing Capacity I Consider stocking at 90 of GC to buffer against droug t Utilizationbased Methods Compare actual use with proper use current stocking Ave Annual AU Ms Ave AUMs removed annually x PUF Average annual utilization Comparison Method Compare grazing unit with like areas nearby with a history of sustainable management Adjust for differences in ecological site range condition topography water etc Unreliable especially if manager is inexperienced Stock and Monitor Method Use known history of stocking rates and monitoring history to adjust stocking rates Requires experience and judgment Problems with Calculating Grazing CapaCIty Forage inventory method is the most problematic because Vegetation varies over space and time both seasonally and interannually Dif cult to assess total production accurately Not all vegetation is equally preferred by animals Animals do not use range uniforml Proper use factors o en unreliable and not empirically 39ved Estimates offorage demand vary 2 26 and are subject to error Problems with Calculating Grazing Capacity Average stocking rate not useful Variable production yearto year Most systems are stochastic not deterministic Problems with Calculating Grazing Capacity Problem of forage allocation to more than one species especially wildlife How much do diets overlap How does habitat use differ among species How does accessibility differ among species How do social factors impact range use Social interactions Diseasetransmission Economic gain Problems with Calculating Grazing Capacity Must be considered given OBJECTIVES CC Summary Use extreme caution when estimating CC Use utilization mapping comparisons of current and desired utilization interpretation ofcurrent range condition use knowledge of past and current stocking history Monitor monitor monitor Manage adaptively Adjust with conditions Adjust in response to monitoring data DROUGHT in the early 2000 s Effects Estimated losses of blue grama 3085 Degradation of riparian areas Loss of perennial grasses Increase in bare ground NRCS recommendation Decrease stocking rate by 3050 1 cow month45 acres 5060 kg of foragemonthha NRCS prediction With proper stocking may have production back in 5 years
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