To construct a new calibration method that combines usability and accuracy for estimating herbage mass from rising‐plate meter readings, we derived four models differing in the way their parameters are related to sampling date and compared their estimation accuracies using cross‐validation. The parameters of the linear regression for each sampling date showed seasonal variations, which had a steep decrease from early April to early June and a gradual increase thereafter. The pooled models were less accurate for estimating herbage mass than a separate model, which had specific parameters for each sampling date (S model). Among the pooled models, however, those in which the parameters were assumed to be linear functions (PL model) or combined functions (PC model) of the sampling date showed substantively improved estimation accuracy compared with the traditional pooled model, in which the parameters were assumed to be fixed throughout the year (PF model). Moreover, at the beginning of the season, the models derived from previous years' data were suggested to be applicable as a practical method. Thus, it can be concluded that these types of pooled calibration could be used as ‘compromise methods’ that combine both accuracy and usability.

Appropriate pasture management requires implementation of methods that reduce the time and labour required for assessing herbage mass. In this study, Monte Carlo simulations and a field study were used to evaluate a simple method for estimating herbage mass based on the corrected average of the single highest and lowest mass values in a pasture. The necessary correction factor for the method was objectively derived using a single nonlinear equation based on the scaled difference between the highest and lowest herbage mass regardless of the mean and variance of mass and pasture size. The accuracy of estimation using this method is acceptable for on-farm use: more than 90% of the estimates had error rates below 20% of the true value when the plot size assessed was larger than 64 m 2 in the simulation study; in the field experiment, approximately half of the estimates had error rates below 20% of the observed mean of random samples (R 2 = 0Á71). The advantages of the proposed method are that it requires only two assessments per pasture and that selection of assessment plots is straightforward. This method has potential for obtaining approximate on-farm estimates of herbage mass that could help improve pasture management.

To evaluate the carbon (C) sequestration function of grassland soils in Japan, soil C stocks were measured in 24 grasslands (3–43‐year‐old pastures) across 14 livestock farms nationwide. Soil C stocks varied among soil types, and the values in the upper 25 and 50 cm were higher in Andosols (mean, 12.4 and 19.3 kg m−2, respectively) than in Brown Forest soils (7.5 and 13.7 kg m−2) and other soil types (5.5 and 7.5 kg m−2). At the same time, C stocks varied among pastures within each soil type. Compared to data from the published work on the C content shortly after pasture establishment, aged pastures had decreasing C concentrations as the soil depth increased, suggesting substantial C accumulation in the top soil layers during pasture aging. This C accumulation caused grassland soils to store as much C as adjacent forest soils. Although the C stocks in the grassland soils were not statistically different from those in the adjacent native forest soils, some grassland areas stored greater amounts of C than the forests, indicating a possibility of increasing soil C stocks through improved grassland management.

No abstract

In order to clarify the effect of land‐use change from forest to grazing pasture on the organic carbon storage in Andosol soil, the Rothamsted carbon turnover model for volcanic soil was applied to a pasture situated at the National Livestock Breeding Center (37°09′N, 140°03′E). The top 25‐cm soil layer was considered to be an active soil carbon pool. The carbon storage in the soils of native forest surrounding the pastures ranged 111–163 t C ha−1 with an average of 133 t C ha−1, which was adjusted according to an equivalent soil weight of pasture. The pasture soil carbon stocks ranged 88–135 t C ha−1, with variations according to site and/or pasture age. The carbon inputs to the soil through the above‐ and below‐ground dead material from pasture plants and cattle feces were estimated to be 1.1, 1.8 and 0.9 t C ha−1 year−1, respectively. As the model outputs of 14C content of the soil, which is an index of carbon dating corresponding to nuclear weapons testing, showed a relatively close agreement with the observations, the modeling was acceptable for the purpose of predicting the turnover of organic carbon in Andosol soil. The model simulation demonstrated that, in order to maintain the average forest carbon level, 3–4 t ha−1 year−1 of the organic carbon input would be needed. These inputs would be provided in a grazing pasture producing 8–9 t ha−1 year−1 of above‐ground dry matter.