Forage Production on a Clay Upland Range Site in Western Kansas

Hulett, G. K.; Tomanek, G. W.

doi:10.2307/3895931

Cited by 19 publications

(8 citation statements)

References 5 publications

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“…Our work continues the tradition of applying statistical techniques to long-term data sets to characterize relationships between climate and primary production (e.g., Hulett and Tomanek 1969, Le Houerou et al 1988, Laurenroth and Sala 1992, Craine et al 2012, Sala et al 2012. Like previous studies, we use simple statistical models to explain historical variation in ANPP as a function of historical variation in precipitation.…”

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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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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“…For individual sites, there are several possibilities. Seasonal precipitation may better predict production than annual precipitation (Hulett & Tomanek 1969; Shiflet & Dietz 1974; Smoliak 1986), there may be a carry‐over effect of production on that of the succeeding year (Hanson et al . 1982) and production may be affected by fire (Briggs & Knapp 1995) or grazing (Milchunas & Lauenroth 1993).…”

Section: Introductionmentioning

confidence: 99%

Influence of precipitation and species composition on phytomass of a semi‐arid African grassland

O’Connor¹,

2001

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Summary 1The influence of botanical composition on annual phytomass production of a semiarid grassland in response to precipitation was tested with a 19-year experiment. Three compositional states reflecting good, medium and poor rangeland condition, whose basal cover increased from poor through to good, were created in 1978.2 Multiple regression models were developed for assessing whether phytomass was influenced by precipitation, composition, phytomass of the previous year, basal cover, abundance of individual species, or diversity. Composition and precipitation accounted for 66% of the variation in phytomass, but a separate model of basal cover by precipitation was equally successful. The linear effect of rainfall on phytomass was enhanced as composition improved from poor (0.076 g m -2 mm ) to good (0.277 g m -2 mm -1 ). Phytomass increased for low precipitation if it had been high the preceding year. Phytomass was more variable over time with deteriorating condition. 3 Species' complementarity ensured greater and more stable production. Setaria sphacelata , Eragrostis chloromelas and Digitaria eriantha increased phytomass on the good or medium condition grasslands during drier years, whereas Themeda triandra had this effect during wetter years. 4 Precipitation-use efficiency (PUE) was influenced mostly by composition and a linear and quadratic effect of precipitation (63% of variance). Optimum PUE of 0.308, 0.203 and 0.096 g m -2 mm -1 for the good, medium and poor condition grasslands, respectively, occurred at intermediate amounts ( ± 680 mm) of precipitation. PUE was increased if phytomass had been high the previous year. 5 Species' complementarity of PUE in response to precipitation was evident for all compositional states. Ten, mostly uncommon, species and their interaction with precipitation explained an extra 21-42% of the variance. Stability of production was related to PUE for medium and poor condition grassland. Uncommon species therefore ensured growth efficiency and stabilized production as condition deteriorated. 6 Diversity had no influence on phytomass or PUE except for a small to moderate effect, respectively, for the medium condition grassland. 7 Vegetation structure, through limiting runoff and promoting infiltration, is an important control on the amount and efficiency of plant production under variable precipitation, whilst composition further influences the amount and stability of production.

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“…The relationship between precipitation and grass production has been investigated, with varying thoroughness, in several Western range types (Craddock and Forsling 1938, Clarke et al 1943, Smoliak 1956, Blaisdell 1958, Sneva and Hyder 1962, Dahl 1963, Currie and Peterson 1966, Hulett and Tomanek 1969. In the Southwest, Nelson (1934), on the Jornada Experimental Range in southern New Mexico, found that current summer rainfall correlated well (r = 0.833) with average height growth of black grama (Bouteloua eriopoda Torr.)…”

Section: Introductionmentioning

confidence: 99%

Influence of Precipitation on Perennial Grass Production in the Semidesert Southwest

Cable¹

1975

Ecology

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Perennial grass production in the semidesert grass-shrub type (with and without a velvet mesquite (Prosopis juliflora var. velutina [Woot.] Sarg.) overstory) was dependent primarily on current summer rainfall and previous summer rainfall. The influence of previous summer rainfall was an interaction effect-not a direct effect. The best overall relationship involved current August rainfall, previous June through September rainfall, and the interaction product of these two. However, in interaction product alone yielded estimates essentially as good as the multiple regression, and explained 64%-91% of the year-to-year variability in grass production. Winter precipitation had no consistent effect on perennial grass production the following summer. The depressing effect of mesquite on perennial grass production was most noticeable at low rainfall levels, and became minor at high rainfall levels.

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Forage Production on a Clay Upland Range Site in Western Kansas

Cited by 19 publications

References 5 publications

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

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

Influence of precipitation and species composition on phytomass of a semi‐arid African grassland

Influence of Precipitation on Perennial Grass Production in the Semidesert Southwest

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