• Background and Aims Specific leaf area (SLA), a key element of the 'worldwide leaf economics spectrum', is the preferred 'soft' plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC? • Methods SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described. • Key Results Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility. • Conclusions Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended.
The present study investigated the application of dielectric spectroscopy as a method for evaluating the dry matter content of potato tubers. Sample specific factors determining the precision of this application were investigated by studying the prediction of the dry material content in agar gel based model systems with known dry matter content. Dielectric spectra were measured with a large custom-made open-ended coaxial probe in the frequency interval from 0.01 GHz to 3 GHz. Both univariate linear models motivated by a two phase mixture model and cross-validated multivariate partial least squares regression (PLSR) models were applied to predict the dry matter content. Results showed that the PLSR models gave markedly better prediction of the dry matter content from the dielectric response in most of the investigated systems compared to the univariate linear models. The highest precision of the predicted dry matter content was observed in chemically and spatially uniform systems, with a root mean square error (RMSE) of the predicted dry-matter content of 0.64 percentage points observed in agar gels containing refined potato starch. A marked decrease in precision is observed in model systems which include chemical variations between potato tuber samples. The added dry material content was predicted with a RMSE of 0.94 percentage points in agar gels with added dried material extracted from separate potato tubers. The local dry matter content from a region within 2 cm of the center location of the dielectric measurement on potato tubers was predicted with a RMSE of 1.21 percentage points. The dry matter content of total potato tubers were predicted with a RMSE of 1.93 percentage points. The difference between the precision of the local and total potato tuber dry matter prediction indicated that spatially non-uniform dry matter distributions were the largest contribution to the observed prediction errors. Variations in chemical composition and air space tissue was also found to be possible factors contributing to the prediction error for potato tubers.
Among Crotalaria L. species, Crotalaria spectabilis stands out for its good adaptation to various production systems in the Brazilian cerrado, high nutrient cycling, reduction of weeds incidence, and antagonistic action on the nematode population. Thus, the aim of this study was to characterize dry matter production and macronutrient accumulation in plant shoots at different growth and development stages. The experimental design consisted of random blocks, with 12 cutting times and 5 repetitions. At each collection, the plants were divided into leaves, stems + branches, pods, and seeds, for macronutrient level determination. The stem + branches are the primary drain on nutrients during the formation of reproductive structures. Macronutrient concentration in the shoots exhibited the following order: K > N>Ca > P>Mg > S and the order of nutrient export in seeds was N > K>P > Ca > S>Mg. Cutting aimed at nutrient supply to the soil should be conducted before pod formation, and at the end of the cycle for biomass production.
Metritis is a disease of particular concern after calving because of its profound negative effects on the reproductive performance of dairy cows. Cows at risk for metritis have shorter feeding times in the days before calving but prepartum dry matter intake (DMI) and water intake may also be useful in identifying cows at risk for this disease. Feeding, drinking, and intake measures may also be affected by social interactions among group-housed cows. The objective of this study, therefore, was to measure intake, feeding, drinking, and social behavior to determine which measures could identify cows at risk for metritis after calving. Feeding and drinking behavior and intake measures were collected from 101 Holstein dairy cows from 2 wk before until 3 wk after calving using an electronic monitoring system. Social behavior at the feed bunk was assessed from video recordings. Metritis severity was diagnosed based on daily rectal body temperature as well as condition of vaginal discharge that was assessed every 3 d after calving until d +21. In this study, 12% of cows were classified as severely metritic and 27% as mildly metritic. Prepartum feeding time and DMI were best able to identify cows at risk for metritis. Cows that developed severe metritis spent less time feeding and consumed less feed compared with healthy cows beginning 2 wk before the observation of clinical signs of infection. For every 10-min decrease in average daily feeding time during the week before calving, the odds of severe metritis increased by 1.72, and for every 1-kg decrease in DMI during this period, cows were nearly 3 times more likely to be diagnosed with this disorder. During the week before calving, cows that were later diagnosed with severe metritis had lower DMI and feeding times during the hours following fresh feed delivery. During this period these cows also engaged in fewer aggressive interactions at the feed bins compared with cows that remained healthy. This research is the first to show that social behavior may play an important role in transition cow health. Research is now required to determine how management should be changed to reduce or prevent illness in transition dairy cows.
Total dry matter (TDM) and nutrient accumulation, nutrient partitioning, and cumulative growing degree days at the time of maximum nutrient accumulation for two‐row spring barley ( Hordeum vulgare L.) are not well quantified under high‐yielding irrigated conditions common in the semi‐arid western United States. Thus, five cultivars of barley were grown under irrigated conditions on a loam soil in the 2015 and 2016 growth seasons to determine these factors. Total nutrient accumulation was greatest at either the soft dough or maturity stage where specific nutrients were greater at one stage as compared to the other. Mean N accumulation was greatest at the soft dough stage (256 kg ha −1 ) where the regression model accounted for 80% of the variation in the data. Additionally, spike N increased from 91 to 105 kg ha −1 from soft dough to maturity. Specific nutrients ( e.g ., K) had significantly greater plant ( i.e ., culms plus leaves) accumulation between soft dough and maturity, 253 and 172 kg ha −1 , respectively, where the spike at the same growth stages had an accumulation of 37 and 42 kg ha −1 , respectively. In contrast, other nutrients ( e.g ., P) were remobilized to the spike as noted by the increase from 14 kg ha −1 at soft dough to 26 kg ha −1 at maturity. In addition to nutrient partitioning, linear regressions resulted in well‐correlated models between TDM and total nutrient accumulation (R 2 = 0.35–0.88) for measured nutrients. Results from the current study provide critical data on nutrient accumulation as well as regression models for two‐row barley under high‐yielding conditions. This information can be used to improve harvest decisions as well as more accurately predict nutrient cycling in barley cropping systems.
Background: The recent discovery of accessory proteins that boost cellulose hydrolysis has increased the economical and technical efficiency of processing cellulose to bioethanol. Oxidative enzymes (e.g. GH61) present in new commercial enzyme preparations have shown to increase cellulose conversion yields. When using pure cellulose substrates it has been determined that both oxidized and unoxidized cellodextrin products are formed. We report the effect of oxidative activity in a commercial enzyme mix (Cellic CTec2) upon overall hydrolysis, formation of oxidized products and impact on beta-glucosidase activity. The experiments were done at high solids loadings using a lignocellulosic substrate simulating commercially relevant conditions. Results: The Cellic CTec2 contained oxidative enzymes which produce gluconic acid from lignocellulose. Both gluconic and cellobionic acid were produced during hydrolysis of pretreated wheat straw at 30% WIS. Up to 4% of released glucose was oxidized into gluconic acid using Cellic CTec2, whereas no oxidized products were detected when using an earlier cellulase preparation Celluclast/Novozym188. However, the cellulose conversion yield was 25% lower using Celluclast/Novozym188 compared to Cellic CTec2. Despite the advantage of the oxidative enzymes, it was shown that aldonic acids could be problematic to the hydrolytic enzymes. Hydrolysis experiments revealed that cellobionic acid was hydrolyzed by beta-glucosidase at a rate almost 10-fold lower than for cellobiose, and the formed gluconic acid was an inhibitor of the beta-glucosidase. Interestingly, the level of gluconic acid varied significantly with temperature. At 50 degrees C (SHF conditions) 35% less gluconic acid was produced compared to at 33 degrees C (SSF conditions). We also found that in the presence of lignin, no reducing agent was needed for the function of the oxidative enzymes. Conclusions: The presence of oxidative enzymes in Cellic CTec2 led to the formation of cellobionic and gluconic acid during hydrolysis of pretreated wheat straw and filter paper. Gluconic acid was a stronger inhibitor of beta-glucosidase than glucose. The formation of oxidized products decreased as the hydrolysis temperature was increased from 33 degrees to 50 degrees C. Despite end-product inhibition, the oxidative cleavage of the cellulose chains has a synergistic effect upon the overall hydrolysis of cellulose as the sugar yield increased compared to using an enzyme preparation without oxidative activity.
The storage of wood chips is important for the biomass supply chain as it compensates for temporal differences in production and consumption. Typical storage-related problems are dry matter and energy losses due to microbial activity. In extensive field trials, we investigated the storage of spruce wood chips from forest residues (FRC) and from energy roundwood (ERC) with and without rain protection under Central European conditions. Additionally, we examined the storage of unchipped piles. The results indicate that the investigated factors, i. e. storage duration, season, assortment and rain protection, have a statistically significant influence on moisture content and dry matter loss of wood chips. During five months of storage, the highest decline in moisture content was 22.6 %-points, the highest dry matter loss 11.1 %. In winter, energy losses reached up to 11.3 %. In summer, energy contents did not change or even increased slightly (max. 4.7 %). Pile temperature and dry matter losses were significantly positively correlated in FRC. Formation of different layers within the piles could be detected. Storage performance was better in unchipped than in chipped energy roundwood. Storage of unchipped forest residues was not beneficial concerning energy content, but fuel quality increased due to reduced ash and fine particle content. Clear best practice recommendations could be drawn regarding wood chip storage under Central European conditions. During winter, FRC should be stored with rain protection or as short as possible while during a dry and warm summer, wood chips can be stored with only few restrictions.
Cheese curd dry matter determines functional properties and process parameters during cheese manufacture. Dry matter is affected by many internal (milk composition and pre-treatment) and external (cheese process parameters) factors that are not considered in the most common models. The purpose of this study was to consider a large number of multiple linear regression models that use these internal and external factors as predictor variables, and select the most suitable of these models in order to predict the cheese curd dry matter during curd treatment. Dry matter ( ) was experimentally determined to create a native data set ( = 1013) for fitting the regression model. Dry matter was affected by curd treatment time ( ), curd treatment temperature ( ), pH-value ( ), curd grain size ( ), fat level ( ) and degree of microfiltration ). A large number of empirical regression models, organized into three different groups, depending on the predictors used, were developed on basis of . A Monte Carlo approach was used to select the optimal model, taking into account the value of Akaike's information criterion (AICc) and the coefficient of determination (R ) of each model. The best models were further analyzed to check for potential bias and to verify that the model assumptions were met. We considered one model of group G2 with 11 terms to most closely fit the aforementioned criteria (native data set; R = 95.55). This model was successfully validated by an independent validation data set ( = 120; R = 91.95).