Assessing the uncertainty associated with intermittent rainfall fields

[1] In many practical situations where spatial rainfall estimates are needed, rainfall occurs as a spatially intermittent phenomenon. An efficient geostatistical method for rainfall estimation in the case of intermittency has previously been published and comprises the estimation of two independent components: a binary random function for modeling the intermittency and a continuous random function that models the rainfall inside the rainy areas. The final rainfall estimates are obtained as the product of the estimates of these two random functions. However the published approach does not contain a method for estimation of uncertainties. The contribution of this paper is the presentation of the indicator maximum likelihood estimator from which the local conditional distribution of the rainfall value at any location may be derived using an ensemble approach. From the conditional distribution, representations of uncertainty such as the estimation variance and confidence intervals can be obtained. An approximation to the variance can be calculated more simply by assuming rainfall intensity is independent of location within the rainy area. The methodology has been validated using simulated and real rainfall data sets. The results of these case studies show good agreement between predicted uncertainties and measured errors obtained from the validation data.

Comparison of methane produced by straw fed sheep in open-circuit respiration with methane predicted by fermentation characteristics measured by an in vitro gas procedure

Reducing carbon conversion of ruminally degraded feed into methane increases feed efficiency and reduces emission of this potent greenhouse gas into the environment. Accurate, yet simple, predictions of methane production of ruminants on any feeding regime are important in the nutrition of ruminants, and in modeling methane produced by them. The current work investigated feed intake, digestibility and methane production by open-circuit respiration measurements in sheep fed 15 untreated, sodium hydroxide (NaOH) treated and anhydrous ammonia (NH3) treated wheat, barley and oat straws. In vitro fermentation characteristics of straws were obtained from incubations using the Hohenheim gas production system that measured gas production, true substrate degradability, short-chain fatty acid production and efficiency of microbial production from the ratio of truly degraded substrate to gas volume. In the 15 straws, organic matter (OM) intake and in vivo OM digestibility ranged from 563 to 1201 g and from 0.464 to 0.643, respectively. Total daily methane production ranged from 13.0 to 34.4 l, whereas methane produced/kg OM matter apparently digested in vivo varied from 35.0 to 61.8 l. The OM intake was positively related to total methane production (R2 = 0.81, P<0.0001), and in vivo OM digestibility was also positively associated with methane production (R2 = 0.67, P<0.001), but negatively associated with methane production/kg digestible OM intake (R2 = 0.61, P<0.001). In the in vitro incubations of the 15 straws, the ratio of acetate to propionate ranged from 2.3 to 2.8 (P<0.05) and efficiencies of microbial production ranged from 0.21 to 0.37 (P<0.05) at half asymptotic gas production. Total daily methane production, calculated from in vitro fermentation characteristics (i.e., true degradability, SCFA ratio and efficiency of microbial production) and OM intake, compared well with methane measured in the open-circuit respiration chamber (y = 2.5 + 0.86x, R2 = 0.89, P<0.0001, Sy.x = 2.3). Methane production from forage fed ruminants can be predicted accurately by simple in vitro incubations combining true substrate degradability and gas volume measurements, if feed intake is known.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Dietary supplements of whole linseed and vitamin E to increase levels of alpha-linolenic acid and vitamin E in bovine milk

The potential to increase the concentrations of n-3 polyunsaturated fatty acids (PUFAs) in milk fat was investigated by studying the effects of feeding a xylose-treated, whole cracked linseed supplement ( rich in alpha-linolenic acid) to dairy cows. Also the effect of increasing the dietary intake of vitamin E on the vitamin E status of milk was investigated. The effect of pasteurisation on milk fatty acid composition was also examined. Using a 3 x 2 factorial design, a total of 60 Holstein dairy cows were fed a total mixed ration based on grass silage supplemented with one of three levels of whole cracked linseed (78, 142 or 209 g . kg(-1) diet dry matter (DM); designated LL, ML or HL, respectively) in combination with one of two levels of additional dietary vitamin E intake ( 6 or 12 g vitamin E . animal(-1) . day(-1); designated LE or HE, respectively). Increasing lipid supplementation reduced (P < 0.01) diet DM intake and milk yield, and increased (P < 0.001) the overall content of oleic, vaccenic, alpha-linolenic and conjugated linoleic acids, and total PUFAs and monounsaturated fatty acids (MUFA). Myristic and palmitic acids in milk fat were reduced ( P < 0.001) through increased lipid supplementation. While α-linolenic acid concentrations were substantially increased this acid only accounted for 0.02 of total fatty acids in milk at the highest level of supplementation (630 g α-linolenic acid &BULL; animal(-1) &BULL; day(-1) for HL). Conjugated linoleic acid concentrations in milk fat were almost doubled by increasing the level of lipid supplementation (8.9, 10.4 and 16.1 g &BULL; kg(-1) fatty acids for LL, ML and HL, respectively). Although milk vitamin E contents were generally increased there was no benefit (P > 0.05) of increasing vitamin E intake from 6 to 12 g . animal(-1) . day(-1). The fatty acid composition of milk was generally not affected by pasteurisation.