Estimating Herbage Production on Semiarid Ranges in the Intermountain Region

Sneva, Forrest A.; Hyder, D. N.

doi:10.2307/3894868

Cited by 63 publications

(44 citation statements)

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“…Grass production occurred during a narrow period in spring, whereas senescence was more evenly distributed throughout the year. The production peak occurred ϳ2 Sneva and Hyder (1962) Szarek (1979) Herbel et al (1972) Herbel et al (1972) Herbel et al (1972) Herbel et al (1972 This study † Rain use efficiency.…”

Section: Discussionmentioning

confidence: 95%

Controls of Grass and Shrub Aboveground Production in the Patagonian Steppe

Jobbágy

Sala

2000

Ecological Applications

216

109

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We studied the temporal dynamics of aboveground net primary production (ANPP) and senescence in a semiarid steppe representative of the Occidental District of Patagonia. We focused on the association of ANPP and senescence with climatic fluctuations at annual and seasonal scales. We estimated annual ANPP based on biomass harvests of grasses and shrubs (the dominant functional groups) at the peak of the growing season (January). The ANPP and senescence of grasses alone were also estimated for several periods within each year. The mean annual ANPP of the studied community for 10 years was 56 g·m 2 ·yr 1 , and the coefficient of variation was 26%. Grass and shrub mean annual ANPP had similar levels. Annual shrub production was associated with cumulative precipitation for the whole year, whereas annual grass production was not associated with precipitation. The average seasonal dynamics of grass ANPP showed its peak during spring, and there was no grass production during late summer and early fall. The peak green biomass of grass was achieved in early summer. Average senescence rates were more constant throughout the year, except for winter, when senescence was almost interrupted. Winter grass ANPP was positively associated with fall temperature, spring grass ANPP was positively related to winter precipitation , and summer grass ANPP was positively related to spring precipitation. Grass se-nescence during spring was negatively related to precipitation in the same season. Our results highlight the importance of separating functional types in productivity studies. The differential effect of precipitation on grass and shrub ANPP masked the association when these functional groups were pooled. ANPP association with climatic variables in the Occidental District of the Patagonian steppe shifted from precipitation alone to precipitation and temperature when the temporal scale of analysis changed from annual to seasonal.

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Section: Discussionmentioning

confidence: 95%

Controls of Grass and Shrub Aboveground Production in the Patagonian Steppe

Jobbágy

Sala

2000

Ecological Applications

216

109

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“…For decades, ecologists and land managers have sought to understand the factors regulating the interannual variability of grassland primary production (e.g., Smoliak 1956, Sneva and Hyder 1962, Herbel et al 1972, Noy-Meir and Walker 1984, Le Houerou et al 1988, Smart et al 2007). …”

Section: Introductionmentioning

confidence: 99%

Anticipating changes in variability of grassland production due to increases in interannual precipitation variability

Hsu

Adler

2014

Ecosphere

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Abstract. Expected increases in interannual precipitation variability due to climate change will lead to increases in the variability of primary production, with potentially important consequences for natural resource management. Previous work has suggested that various biotic and abiotic processes might amplify or buffer variation in production in response to variation in precipitation. In particular, production to rain variability ratios (PRVR), the coefficient of variation of production divided by the coefficient of variation of precipitation, indicate that production is often relatively more variable than precipitation. We used 37 long-term data sets from grasslands across the globe to test how future increases in precipitation variability might alter the variability of aboveground net primary production (ANPP). We demonstrate that PRVR is not a useful metric: it is predicted by a site's precipitation-production relationship and a PRVR greater than 1 need not imply that increases in the variability of ANPP will be disproportionately greater than increases in precipitation variability. Instead, it is the form of the precipitation-ANPP relationship that determines how increases in precipitation variability will impact ANPP variability. We fit linear, lag effect, and nonlinear precipitation-ANPP relationships to each data set. At most sites, the precipitation-ANPP relationship is weakly nonlinear, though the lag effect model, incorporating previous year ANPP, performed best at several sites. To test whether the three models project differences in the response of ANPP to future increases in precipitation variability, we directly perturbed the observed precipitation time series and quantified the results of this perturbation on ANPP variability. Under simple linear or lag effect models, relative increases in ANPP variability were always equal to the relative increases in precipitation variability. When we modeled ANPP as a nonlinear, saturating function of precipitation, projected increases in ANPP variability were disproportionately high, with production dropping more in dry years than it increases in wet years. In six cases, increases in ANPP variability were twice as large as increases in precipitation variability. Based on Akaike model weights, a 5% increase in precipitation variability would cause a 6.3% increase in ANPP variability on average.

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“…Mean aboveground net primary production (ANPP) of widely different arid and semiarid systems is strongly correlated with mean annual precipitation (for example, see Sneva and Hyder 1962;Noy-Meir 1973;Lauenroth 1979;McNaughton 1985;Le Houé rou and others 1988;Sala and others 1988a;Paruelo and others 1999). Additional factors that control ANPP include temperature (Christie 1981;Epstein and others 1997), evapotranspiration (Snyman 1998), soil water-holding capacity (Sala and others 1988a;Epstein and others 1997;Snyman 2000), nutrient availability (Chapin 1991;Du Preez and Snyman 1993), species composition (Milchunas and Lauenroth 1993), grazing (O'Connor and Roux 1995;Oesterheld and McNaughton 2000), and fire (Oesterheld and McNaughton 2000).…”

Section: Introductionmentioning

confidence: 99%

Do Grasslands Have a Memory: Modeling Phytomass Production of a Semiarid South African Grassland

et al. 2004

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We analyzed data sets on phytomass production, basal cover, and monthly precipitation of a semiarid grassland in South Africa for good, medium, and poor rangeland condition (a) to investigate whether phytomass production per unit of basal cover differed among rangeland conditions, (b) to quantify the time scales of a carryover effect from production in previous months, and (c) to construct predictive models for monthly phytomass. Finally, we applied the best models to a 73-year data set of monthly precipitation data to study the long-term variability of grassland production. Our results showed that mean phytomass production per unit of basal cover did not vary significantly among the rangeland conditions-that is, vegetated patches in degraded grassland have approximately the same production as vegetated patches in grassland in good condition. Consequently, the stark decline in production with increasing degradation is a first-order effect of reduced basal area. Current-year precipitation accounted for 64%, 62%, and 36% of the interannual variation in phytomass production for good, medium, and poor condition, respectively. We found that 61%, 68%, and 33%, respectively, of the unexplained variation is related to a memory index that combines mean monthly temperature and a memory of past precipitations. We found a carryover effect in production from the previous 4 years for grassland in good condition and from the previous 1 or 3S month for grassland in medium and poor condition. The memory effect amplified the response of production to changes in precipitation due to alternation of prolonged periods of dry or wet years/months at the time scale of the memory. The interannual variability in phytomass production per unit basal cover (coefficient of variation [CV] ϭ 0.42-0.50 for our 73-year prediction, CV ϭ 0.57-0.71 for the 19-year data) was greater than the corresponding temporal variability in seasonal rainfall (CV ϭ 0.29).

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Estimating Herbage Production on Semiarid Ranges in the Intermountain Region

Cited by 63 publications

References 0 publications

Controls of Grass and Shrub Aboveground Production in the Patagonian Steppe

Controls of Grass and Shrub Aboveground Production in the Patagonian Steppe

Anticipating changes in variability of grassland production due to increases in interannual precipitation variability

Do Grasslands Have a Memory: Modeling Phytomass Production of a Semiarid South African Grassland

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