Prediction of enteric methane production from dairy cows

Abstract

The main objective of this study was to elucidate the potential for prediction of enteric methane (CH₄) emissions from dairy cows by using predicted rumen plus hindgut digested (fermented nutrients) and total tract digested nutrients (by using NorFor) as input variables. Twenty-one experiments (78 dietary treatments) were collected. The data-set was used to develop prediction models and to test their and extant models ability to predict enteric CH₄ emissions. Models were compared based on mean squared prediction error and concordance correlation coefficient (CCC) analysis. Fermented nutrients did not predict enteric CH₄ emissions adequately (CCC < 0.420). Including total digested (td) nutrients in the model [CH₄ (MJ/d) = ?2.13 + 1.64 tdOM (kg/d) ?9.74 tdFat (kg/d) + 1.64 tdNDF (kg/d)] predicted enteric CH₄ emissions more precisely (CCC = 0.733), and showed an improvement in the prediction of enteric CH₄ emissions over the extant models tested.     

Climate Variability and Oceanographic Settings Associated with Interannual Variability in the Initiation of Dinophysis acuminata Blooms

In 2012, there were exceptional blooms of D. acuminata in early spring in what appeared to be a mesoscale event affecting Western Iberia and the Bay of Biscay. The objective of this work was to identify common climatic patterns to explain the observed anomalies in two important aquaculture sites, the Galician Rías Baixas (NW Spain) and Arcachon Bay (SW France). Here, we examine climate variability through physical-biological couplings, Sea Surface Temperature (SST) anomalies and time of initiation of the upwelling season and its intensity over several decades. In 2012, the mesoscale features common to the two sites were positive anomalies in SST and unusual wind patterns. These led to an atypical predominance of upwelling in winter in the Galician Rías, and increased haline stratification associated with a southward advection of the Gironde plume in Arcachon Bay. Both scenarios promoted an early phytoplankton growth season and increased stability that enhanced D. acuminata growth. Therefore, a common climate anomaly caused exceptional blooms of D. acuminata in two distant regions through different triggering mechanisms. These results increase our capability to predict intense diarrhetic shellfish poisoning outbreaks in the early spring from observations in the preceding winter.     

Mixed-effect non-linear modelling for diameter estimation along the stem of Tectona grandis in mid-western Brazil

This study evaluated the efficiency of taper functions and the application of mixed-effect modelling for diameter estimation along the stems of Tectona grandis. We sampled 266 trees of Tectona grandis, measuring the diameter at relative heights for volume determination, grouping the data according to three form-factor classes. Six taper functions were fitted, selecting the function with better fit performance. Six taper functions were fitted, selecting the function with better fit performance. The selected function was fitted in its basic formulation, and with the mixed non-linear modelling technique in different scenarios, and for the stem stratified in three portions of the total height. The precision and selection of the adjusted models were evaluated regarding the coefficient of determination, standard error of estimate, the Akaike information criterion, bias, quadratic error and absolute bias. According to the statistical criteria used, the model of Kozak was selected for the adjustments. For diameter estimation, the scenario with two coefficients as random effects provided an accuracy increase of 11.91%, and the mixed non-linear modelling better estimated the stem diameter for the stratified stems. In conclusion, the model of Kozak can be used to describe the stem shape of Tectona grandis, and the mixed-effect non-linear model approach was the best technique to estimate diameter along the stem of Tectona grandis.     

Combining rhizosphere and soil-based P management decreases the P fertilizer demand of China by more than half based on LePA model simulations

Phosphorus (P) is a finite natural resource and is increasingly considered to be a challenge for global sustainability. Agriculture in China plays a key role in global sustainable P management. Rhizosphere and soil-based P management are necessary for improving P-use efficiency and crop productivity in intensive agriculture in China. A previous study has shown that the future demand for phosphate fertilizer by China estimated by the LePA model (legacy phosphorus assessment model) can be greatly reduced by soil-based P management (the building-up and maintenance approach). The present study used the LePA model to predict the phosphate demand by China through combined rhizosphere and soil-based P management at county scale under four P fertilizer scenarios: (1) same P application rate as in 2012; (2) rate maintained same as 2012 in low-P counties or no P fertilizer applied in high-P counties until targeted soil Olsen-P (TP_Olsen) level is reached, and then rate was the same as P-removed at harvest; (3) rate in each county decreased to 1–7 kg ha^–1 yr^–1 after TP_Olsen is reached in low-P counties, then increased by 0.1–9 kg ha^–1 yr^–1 until equal to P-removal; (4) rate maintained same as 2012 in low-P counties until TP_Olsen is reached and then equaled to P-removal, while the rate in high-P counties is decreased to 1–7 kg ha^–1 yr^–1 until TP_Olsen is reached and then increased by 0.1–9 kg ha^–1 yr^–1 until equal to P-removal. Our predictions showed that the total demand for P fertilizer by whole China was 693 Mt P₂O₅ and according to scenario 4, P fertilizer could be reduced by 57.5% compared with farmer current practice, during the period 2013–2080. The model showed that rhizosphere P management led to a further 8.0% decrease in P fertilizer use compared with soil-based P management. The average soil Olsen-P level in China only needs to be maintained at 17 mg kg^–1 to achieve high crop yields. Our results provide a firm basis for government to issue-relevant policies for sustainable P management in China.     

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Soya bean yield gap can be caused by different factors resulting in uncertainties when the objective is to use such information for farm decision‐making and reference yield determination. Thus, this study aimed to quantify the soya bean yield gap for four sites, located in Southern and Midwestern Brazil, as well as the uncertainties of that related to cultivars, sowing dates, soil types and reference yields. The crop simulation model DSSAT‐CSM‐CROPGRO‐Soybean was calibrated for cultivars with similar maturity groups, based on the data obtained from the best farmers at the county level. The yield gap by water deficit (YG_WD) was obtained through the difference between potential and attainable yields, and that one caused by sub‐optimum crop management (YG_CM) by subtracting actual yield of each county, obtained from official statistics between 1989/90 and 2014/15 growing seasons, from the estimated attainable yield. The yield was simulated using four sowing dates, three soil types and two soya bean maturity groups by county. The reference yield uncertainty was quantified using yield reference from crop model and regional winners of the soya bean yield context, conducted by CESB (Brazilian Soybean Strategic Committee), for the growing seasons from 2013/14 to 2015/16. The crop model showed a good agreement between measured and simulated crop development and growth using calibration by maturity group, with low root mean square error (347 kg/ha). Southern sites had a mean YG_WD of 1,047 kg/ha, while in the Midwest, it was lower than 100 kg/ha. The YG_CM was 1,067, 528, 984 and 848 kg/ha, respectively, for Castro, PR, Mamborê, PR, Montividiu, GO and Primavera do Leste, MT, representing the opportunity for yield gain when having the best farmers as reference. The maturity groups, sowing dates and soil types showed to be an important source of uncertainty for yield gap determination, being recommended to investigate the farms in detail for an appropriate quantification. The reference yield showed expressive uncertainties, with some farmers presenting conditions to increase their soya bean yields by more than 3,000 kg/ha, when considering as reference the yields obtained by the winners’ farmers. These results show that uncertainties must be reduced when assessing farm yield gaps, in order to ensure that expected rate of soya bean yield growth could be reached by adopting the same technologies from CESB winners and best farmers in the county as a reference.     

Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

Estimating species’ potential distribution is one of the main objectives of macroecology, especially when sampling biases can affect knowledge on how environmental variables affect species distribution. Ecological niche models estimate species’ environmental niches from different variables and their occurrences. Using the presence-only data from eight Amazonian fish species, which inhabit rivers and streams, we aimed to (a) explore the effect of different sets variables on the spatial distributions of target species and (b) evaluate the predictive responses of MaxEnt to sets of variables with different degrees of complexity. MaxEnt has high flexibility in relation to the input data and its performance is influenced by a moderate number of adjustable parameters, allowing for high precision results when balancing underestimation and overestimation errors. We used environmental predictors in MaxEnt the principal components of climatic, topographic and edaphic variables as inputs. The combination of topographic and edaphic variables produced more precise and spatially restricted distribution ranges for all species when compared to those generated with climatic variables. All models reached high AUC values, especially for stream species. Modelled range sizes were broader for the river species, suggesting different tolerance thresholds and habitat preferences when compared to stream species. The complexity of the different variables sets did not affect MaxEnt's prediction capacity. However, for stream species, MaxEnt showed a greater predictive power. This work increases the knowledge with regards to the influence of different environmental predictors on the spatial patterns of the distribution of Amazonian fish.     

A multicentric retrospective study of serum/plasma urea and creatinine concentrations in dogs using univariate and multivariate decision rules to evaluate diagnostic efficiency

BACKGROUND: Urea and creatinine are the most frequently used indirect markers in plasma and serum of glomerular filtration rate in dogs. Both have been shown to lack sensitivity but their diagnostic efficiency for the diagnosis of kidney disease has been minimally investigated. OBJECTIVE: The purpose of this retrospective study was to investigate the influence of possible factors of variation on both analytes and to determine whether specific decision rules should be drawn up for subpopulations of dogs. METHODS: The results of urea and creatinine measurements, breed, sex, age, and health status (healthy, renal disease, or nonrenal disease) of 3822 dogs were collected from the archives of 5 veterinary clinics. Data were analyzed with univariate and multivariate decision rules with and without adjustment. RESULTS: There were significant effects and interactions of almost all of the sources of variation. Slight improvements in diagnostic efficiency were obtained by adjusting the decision rules to these sources of variations. Univariate decision rules gave approximately the same diagnostic efficiency for urea and creatinine concentrations, with sensitivity and specificity in the range of 70% and 90%, respectively, using the upper limit of the reference interval as the threshold value. Multivariate decision rules provided only minor improvements in diagnostic efficiency. CONCLUSION: Simultaneous measurement of both urea and creatinine is of limited diagnostic value over the analysis of a single variable. Creatinine is the preferred analyte as it is affected by fewer extrarenal factors of variation.     

Different susceptibility to fatty liver-haemorrhagic syndrome in young and older layers and the interaction on blood LDL-C levels between oestradiols and high energy-low protein diets

1. The objective of the study was to investigate the susceptibility of young and older laying hens to fatty liver-haemorrhagic syndrome (FLHS) and to evaluate the reliability of different blood lipid fractions for predicting or diagnosing FLHS.

2. Forty young hens and 40 older hens were caged individually. Each group of hens was randomly allotted to four treatments for 21 days: either a control, an oestradiol group, a high energy-low protein diet (HELPD) group or a HELPD + oestradiol group. Blood levels of oestradiol, triglyceride (TG), cholesterol (CHOL), high density lipoprotein-cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C), liver total lipids, hepatic haemorrhagic scores and productive performance were assessed.

3. In older hens, β-oestradiol increased (P < 0.05) liver total lipids, hepatic haemorrhagic scores and the incidence of FLHS but reduced (P < 0.05) productive performance; however, such changes were not observed in young hens.

4. In two groups of hens, serum TG, CHOL and HDL-C levels were increased (P < 0.001) by β-oestradiol. Hens with FLHS had higher serum TG, CHOL and HDL-C (P < 0.001) than non-FLHS birds in the older layer group of hens.

5. An interaction (β-oestradiol × HELPD) (P < 0.05) for LDL-C levels was observed in both groups of hens. In young hens, β-oestradiol induced a decrease (P = 0.004) in serum LDL-C levels but the effect was attenuated by HELPD. In older hens, HELPD caused an increase (P = 0.02) in serum LDL-C although the effect depended on the presence of β-oestradiol.

6. In conclusion, older layers were more susceptible to FLHS than young layers after oestradiol treatment. Blood TG, CHOL and HDL-C rather than LDL-C levels can be used as a prediction tool for the overall susceptibility to FLHS in older rather than young layers. There were interactions between oestradiol and HELPD on blood LDL-C levels in laying hens.     

Modelling cumulative egg production in laying hens and parent stocks of broiler chickens using classical growth functions

ABSTRACT

1. The objective of the present study is to introduce fresh insight into modelling of egg production by applying classical growth functions to egg production records reported by Aviagen Management Guide to laying hens and the parent stock of broiler chickens.

2. The functions (monomolecular, logistic, Gompertz, Richards and Morgan) were fitted using nonlinear regression procedures of SAS software, and their performance was assessed using goodness-of-fit statistics (coefficient of determination, residual mean squares, Akaike information criterion and Bayesian information criterion).

3. Overall, except for the logistic and Gompertz, the growth functions evaluated gave an acceptable fit to the cumulative egg production curves, with the Morgan equation ranking first followed by the Richards equation. The Morgan and Richards equations provided satisfactory predictions of weekly egg yield at different egg production stages, from early to late production, whereas the least accurate estimates were obtained with the logistic equation.

4. In conclusion, classical growth functions proved feasible alternatives to fit cumulative egg production curves of laying hens and parent stock of broiler chickens, resulting in suitable statistical performance and accurate estimates of production.