Cotton plant architecture is an important agronomic trait affecting yield and quality. In the present study, two F2:3 upland cotton (Gossypium hirsutum L.) populations were developed from Baimian2/TM-1 and Baimian2/CIR12 to map quantitative trait loci (QTL) for cotton plant architecture traits using simple sequence repeat (SSR) markers. A total of 73 QTL (37 significant and 36 suggestive) affecting plant architecture traits were detected in both populations. Four common QTL, qTFN-17 for total fruit nodes, qFBN-17 for fruit branch nodes, qFBL-17 for fruit branch length and qTFB-17a/qTFB-17b (qTFB-17) for total fruit branches, were found across the two populations. These common QTL should have high reliability and could be used for marker-assisted selection (MAS) to facilitate cotton plant architecture. The two common QTL, qTFN-17 and qFBL-17, were especially significant in both populations, and moreover, they explained >0·100 of the phenotypic variation in at least one population. These two QTL should be considered preferentially for MAS. The synergistic alleles and the negative alleles could be utilized in cotton plant architecture breeding programmes according to specific breeding objectives.

The relationships between physiological variables and sugarcane productivity under water deficit conditions were investigated in field studies during 2005 and 2006 in Weslaco, Texas, USA. A total of 78 genotypes and two commercial varieties were studied, one of which was drought-tolerant (TCP93-4245) and the other drought-sensitive (TCP87-3388). All genotypes were subjected to two irrigation regimes: a control well-watered treatment (wet) and a moderate water-deficit stress (dry) treatment for a period of 90 days. Maximum quantum efficiency of photosystem II (Fv/Fm), estimated chlorophyll content (SPAD index), leaf temperature (LT), leaf relative water content (RWC) and productivity were measured. The productivity of all genotypes was, on average, affected negatively; however, certain genotypes did not suffer significant reduction. Under water deficit, the productivity of the genotypes was positively and significantly correlated with Fv/Fm, SPAD index and RWC, while LT had a negative correlation. These findings suggest that genotypes exhibiting traits of high RWC values, high chlorophyll contents and high photosynthetic radiation use efficiency under low moisture availability should be targeted for selection and variety development in programmes aimed at improving sugarcane for drought prone environments.

The potential of ensiled sorghum–soybean as a ruminant feed has already been demonstrated; however, alternatives for soybean should be considered because other legumes such as jack bean (JB; Canavalia ensiformis (L.) DC.) and velvet bean (VB; Mucuna pruriens (L.) DC) might produce better yields under tropical conditions. First, the possibility for a qualitative conservation of these legumes in combination with sorghum was studied using lab-scale silages. Furthermore, the potential of additional molasses as a source of water-soluble carbohydrates (WSC) and a microbial inoculant (BIOPRANAL) to improve silage quality were assessed. As sorghum or legume tannins may influence the silage nutritive value, their importance was assessed through the addition of polyethylene glycol (PEG), which reduces tannin activity. Therefore, 25 treatments per legume–sorghum combination were created according to a central composite design. An acceptable silage quality was reached when at least half of the fresh biomass consisted of sorghum in combination with at least 15 g of additional WSC/kg fresh material. The nutritive value of mixed silages as well as pure sorghum silage in combination with dry jack beans was determined through in vitro digestibility in the rumen and small intestine. Polyethylene glycol increased the in vitro production of short-chain fatty acids, ammonia (NH3) concentrations and effective rumen dry matter and crude protein degradability for both mixed silages, indicating that tannins reduced rumen degradability. The latter action could be positive as the amount of digestible bypass protein increased when no PEG was added in the silo. Propionate and valerate proportions were increased through PEG addition to sorghum–JB and sorghum–VB silages, respectively, suggesting that the nature of the tannins differ between these legumes.

The dynamic model presented in the current paper estimates heat production and heat flow between growing and mature cattle (Bos indicus and Bos taurus) and the surrounding environment. Heat production was calculated using the NRC (2000) and heat flows between the animal and the environment were based largely on existing models and physical principles. Heat flows among the body core, the skin, the coat and the environment were calculated. Heat flows from and to the environment included solar radiation, long wave radiation, convection and evaporative heat loss. Physiological responses of cattle (sweating, panting and vasodilation) were modelled through mechanistic equations. The model required weather (radiation, temperature, wind and vapour pressure), animal (body-core weight and genotype-specific parameters) and dietary inputs (dry matter intake rates and diet composition) and estimated heat balance and the physiological responses of the animal to within-day weather variation. The current paper has focused on heat stress, although the model was designed to run under both hot and cold climatic conditions. The model developed in the current paper provides researchers and livestock producers with the ability to predict heat stress and to evaluate mitigating procedures.

Mineralized soil nitrogen (N) is an important source of N for grassland production. Some soils can supply large quantities of plant-available N through mineralization of soil organic matter. Grass grown on such soils require less fertilizer N applications per unit yield. A reliable, accurate and user-friendly method to account for soil N supply potential across a large diversity of soils and growing conditions is needed to improve N management and N recommendations over time. In the current study, the effectiveness of chemical N tests and soil properties to predict soil N supply for grass uptake across 30 Irish soil types varying in N supply potential was investigated under controlled environmental conditions. The Illinois soil N test (ISNT) combined with soil C : N ratio provided a good estimate of soil N supply in soils with low residual mineral N. Total oxidized N (TON) had the largest impact on grass dry matter (DM) yield and N uptake across the 30 soil types, declining in its influence in later growth periods. This reflected the high initial mineral N levels in these soils, which declined over time. In the current study, a model with ISNT-N, C : N and TON (log TON) best explained variability in grass DM yield and N uptake. All three rapid chemical soil tests could be performed routinely on field samples to provide an estimate of soil N supply prior to making N fertilizer application decisions. It can be concluded that these soil tests, through their assessment of soil N supply potential, can be effective tools for N management on grassland; however, field studies are needed to evaluate this under more diverse growing conditions.

Mixed grazing is an alternative pasture management which can be used to increase ruminant performance and reduce gastro-intestinal nematodes. A meta-analysis was performed on the results of previous studies from the literature to quantify the benefit of mixed grazing with sheep and cattle and identify determinants and enhancing factors. The analysis focused on papers reporting measurements of average daily weight gain (ADG) of sheep and cattle, carried out simultaneously in mixed grazing and in mono-grazing. The meta-analysis used 179 observations from 16 experiments published in nine carefully selected papers. The benefits of mixed grazing were appraised using the individual ADG of sheep (ADGisheep), cattle (ADGicattle) and the overall performances (ADG per hectare (ADGha), g/day/ha). A theoretical production value (ADGha-Theo) was calculated from the ADGha in mixed and mono-grazing at similar stocking rates (SRs)/ha. The ADGisheep was greater in mixed treatments compared with control treatments (+14·5 g/day, P<0·001) and varied with physiological status and SR (P<0·001). The ADGicattle was similar in mixed and mono-grazing. At a global level ADGha in mixed treatments (ADGha-Mtrt) was higher by 28·6% compared with sheep alone and by 25·1% compared with cattle alone; also, ADGha-Mtrt was 8% higher than ADGha-Theo. The ADGha varied with physiological status and decreased with the proportion of sheep in association (P<0·01). The contribution of ADGha in mixed grazing due to each species was lower compared with gains for each species reared alone (1438 v. 2423 g/day/ha for sheep and 1687 v. 2491 g/day/ha for cattle, P<0·001).

Although the effect of saponins or saponin-containing plants on rumen microorganisms and rumen fermentation has been intensively investigated, this issue still requires special attention. Many of the phenomena occurring in the rumen related to dietary saponin supplementation are still not fully understood. Saponaria officinalis is a triterpenoid saponin-containing plant; thus, the aim of the present study was to evaluate the effect of S. officinalis L. powdered root, methanolic extract of the S. officinalis root (SOR) and the effect of the separated fractions (polysaccharides, saponins and phenolics) of S. officinalis on rumen methanogenesis, microbial population and rumen fermentation characteristics in an in vitro batch culture fermentation system. The powdered root (raw plant material) and S. officinalis extract (SOE) decreased in vitro methane production and consequently reduced the microbial population in a dose-dependent manner. The inhibition of methanogenesis was accompanied by changes in the volatile fatty acids profile. In vitro dry matter digestibility was not affected by any of the secondary compounds applied. The highest applied doses of SOE caused a higher reduction in methanogenesis (33·5 v. 14·4%) than the highest doses of powdered root form. Such results suggest that the basic components of the SOR could interact with phytochemicals or that the phytochemicals became physically less available for microbiota, resulting in a decreased antimethanogenic activity of the powdered root v. the extract. Among all the fractions selected, the saponin fraction exerted the greatest impact on ruminal fermentation. In conclusion, saponins decreased methane production by 29% in comparison with the control. This decrease was related to the reduction in protozoa and methanogen counts. It is proposed that S. officinalis has the potential to inhibit rumen methanogenesis without affecting rumen fermentation adversely.

Parameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

Livestock production is a significant source of methane (CH4) and nitrous oxide (N2O) emissions globally. In any sheep-producing nation, an effective agricultural greenhouse gas (GHG) mitigation strategy must include sheep-targeted interventions. The most prominent interventions suited to sheep systems are reviewed in the current paper, with a focus on farm-level enteric CH4 and soil N2O emissions. A small number of currently available interventions emerge which have broad consensus on their mitigation potential. These include breeding to increase lambing percentages and diet formulation to minimize nitrogen excretion. The majority of interventions still require significant research and development before deployment. Research into the efficacy of interventions such as incorporation of biochar is in its infancy, while for others such as dietary supplements, successes in isolated studies now need to be replicated in long-term field trials under a range of conditions. Enhancing understanding of underlying biological processes will allow capitalization of interventions such as vaccination against rumen methanogenesis and pasture drainage. Many interventions cannot be recommended at a regional or national scale because, either, their mitigation potential is inextricably linked to soil and weather conditions in the locality of use, or their use is restricted to more intensive, closely managed systems. Distilling the long list of interventions to produce an effective farm-level mitigation strategy must involve: accounting for all GHG fluxes and interactions, identifying complimentary sets of additive interventions, and accounting for baseline emissions and current practice. Tools such as whole farm GHG models and marginal abatement cost curves are crucial in the development of tailored, practical sheep farm GHG mitigation strategies.

Feed makes up c. 0·7 of total variable costs on Irish beef farms. A period of reduced growth (caused by nutritional restriction) followed by a period of accelerated growth (compensatory growth) can be used to take advantage of lower cost feedstuffs (grazed grass) during the grazing season. The Grange Dairy Beef Systems Model (GDBSM) was modified to capture more accurately the implications of compensatory growth and, thus, the energy demand of beef cattle was partitioned into energy required for maintenance and energy required for growth. For the current study, three production systems were evaluated where the male progeny of dairy cows were finished as steers at 24 (S24), 28 (S28) and 30 (S30) months of age. Three different live weight gains (RESLWG; 0·4, 0·6 and 0·8 kg/day), reflecting different levels of nutritional restriction, were simulated during the first winter feeding period (November–February) for S24 and during the second winter feeding period for S28 and S30. This allowed the effect of different live weight gains during a nutritional restriction period on farm profitability to be determined. Results indicated that for S24 the most profitable RESLWG was 0·6 kg/day. However, for S28 and S30 the most profitable systems were RESLWG of 0·4 kg/day. Financial performance of all systems was very sensitive to variation in beef carcass and calf prices but less sensitive to concentrate and fertilizer price variation. Furthermore, sensitivity analysis showed that the level of maintenance energy reduction and the duration of this reduction had a modest impact on results. The GDBSM is demonstrated as a quantitative framework for simulating compensatory growth and determining its effects on the profitability of dairy calf-to-beef production systems.

The current research was carried out to evaluate the use of crude protein and fibre components in faeces for estimating intake and digestibility in sheep fed with pearl millet (Pennisetum americanum (L.) Leeke). The equations were developed from four trials in metabolism cages with 16 sheep in each trial. Each animal received a different quantity of millet leaves in the diet: 0·015, 0·020 and 0·025 dry matter (DM) as a proportion of live weight (LW) and ad libitum with at least 0·2 of daily feed refusals. Organic matter intake (OMI, g/day) was measured, through the difference between offer and refusals; total faeces were collected for 5 days, which was used to determine faecal crude protein (CPf, g/day and g/kg of organic matter (OM)), faecal neutral detergent fibre (NDFf, g/day and g/kg OM), faecal acid detergent fibre (ADFf, g/day and g/kg OM) and OM digestibility (OMD). Linear regression equations were calculated to determine the relationship between OMI and CPf (P<0·001, R2=0·90, relative prediction error (RPE=14·02%). A multiple linear equation was generated for OMI including CPf and NDFf (P<0·001, R2=0·94; RPE=9·25%). Hyperbolic (single and multiple) and exponential models were tested to estimate OMD, where the hyperbolic multiple model including CPf and NDFf showed lower RPE (3·90%). These equations for estimating OMI and OMD were evaluated on sheep grazing P. americanum fertilized with increasing levels of nitrogen (N) (50, 100, 200 and 400 kg N/ha), comparing measured and estimated OMI. The intake estimated by multiple regression (CP and NDFf) showed a higher R2 (0·98) and lower RPE (5·25%) than the simple (CPf only) linear equation (R2=0·94; RPE=20·45%). The results demonstrated the feasibility of using the faecal index generated in metabolism cages for estimating intake and digestibility in sheep grazing P. americanum.

The current situation of volatile milk prices and rising costs of, e.g. grain and labour, suggests that it is worth studying productivity and efficiency in dairy farming. The objective of the current whole-system study, carried out in lowland Central Switzerland from 2007 to 2010, was to compare the performance, efficiency, land productivity and profitability of indoor-feeding (IF) dairy production with that of pasture-based feeding (PF) dairy production. An IF herd consisting of 11 Holstein–Friesian (HF) and 13 Brown Swiss (BS) cows was kept in a free-stall barn and fed a part-mixed ration (PMR) of maize silage, grass silage and protein concentrate. The cows were allocated 15·8 ha of agricultural land (AL). In the PMR, an average per lactation of 443 kg protein concentrate and 651 kg compound feed was fed by a concentrate dispenser according to the requirements of each cow. The PF herd comprised 14 Swiss Fleckvieh (SF) and 14 BS cows, which were kept in a free-stall barn throughout the winter; barn-ventilated hay was offered ad libitum during the lactation period. This herd was allocated 15·7 ha of AL. After calving in spring, the PF cows grazed on semi-continuous pastures; they consumed an average of 285 kg of concentrate per lactation. The IF cows of the BS breed produced significantly more energy-corrected milk (ECM) per standard lactation compared with PF cows (8750 v. 5610 kg), more milk fat (350 v. 213 kg) and more milk protein (306 v. 203 kg). However, the milk of PF cows had higher levels of conjugated linoleic acid (CLA) (1·9 v. 0·6 g/100 g fat) and ω−3 fatty acids (1·7 v. 0·9 g/100 g fat) than the milk of the IF cows. The calving interval (378 v. 405 days) and the empty time (87 v. 118 days) of the BS breed were significantly shorter in the PF in comparison with that of the IF production system. The IF herd yielded significantly higher ECM/ha AL and year (12 716 v. 10 307 kg), and showed a higher feed efficiency (1·3 v. 1·1 kg ECM/kg of total dry matter intake (DMI)). The productivity per hour was roughly similar in the two systems (IF: 76 v. PF: 73 kg milk/h). The PF system resulted in higher labour income compared with the IF system (20·7 v. 13·4 €/h), but the difference was not significant. In conclusion, land productivity and efficiency were higher with the IF herd than the PF herd due to the higher energy intake per kg feed. However, within the given conditions, the more interesting case, economically, might be the reduced costs and improved milk quality of the PF system rather than the increased milk yield of the IF cows.

The classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed and grazed only fields. To classify land usage, the data were clustered and visualized in a hierarchical fashion. This was done by using a novel method for the analysis and classification of data based on growing hierarchical self-organizing maps (GHSOM). Self-organizing maps (SOM) have been shown to be successful for the analysis of highly dimensional input data in data mining applications as well as for data visualization. Moreover, the hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, capturing inherent hierarchical relationships among them. Experimental results show the utility of this approach.

An 8-year-long field experiment (1998–2006) was established in Sweden with the aim of evaluating the effects of applying organic wastes in combination with mineral nitrogen (N) to agricultural soil. Sewage sludge (SS), biogas residues (BR) and municipal compost (CO) were applied annually at rates corresponding to 50 kg N/ha and supplementary mineral N fertilizer also applied at rates corresponding to 50 kg N/ha. The effects were evaluated by analysing crop yield and soil chemical and microbiological properties. The results showed that none of the fertilizers produced significantly higher yield of barley over the 8-year period compared to any other. Biogas residue proved to be particularly beneficial for the substrate-induced respiration (SIR) in soil and increased the proportion of active to dormant micro-organisms. Treatment with SS increased plant-available phosphorus (P-AL) and N mineralization (N-min), whereas CO increased the basal respiration (B-resp). Changes in the microbial community structure were assayed by terminal restriction fragment length polymorphism (T-RFLP); the T-RFLP signatures of the soil bacterial community were largely unaffected by the addition of organic waste. Of the chemical properties assayed, the largest increases were seen in P-AL, where SS produced the highest value. Treatments with the organic wastes showed no negative effects other than a slight decrease in B-resp induced by SS and BR. In conclusion, the microbiological activity in the soil responded more rapidly than the changes in the community structure and the chemical properties to changes in the soil environment.

In order to describe the lactation curves of milk yield (MY) and composition, six non-linear mathematical equations (Wood, Dhanoa, Sikka, Nelder, Hayashi and Dijkstra) were used. Data were 5 535 995 test-day records for MY, fat (FC) and protein (PC) contents and somatic cell score (SCS) from the first three lactations of Iranian Holstein cows that were collected on 2547 dairy herds in the period from 2000 to 2011 by the Animal Breeding Center of Iran. Each model was fitted to monthly production records of dairy cows using the NLIN and MODEL procedures in SAS and the parameters were estimated. The models were tested for goodness of fit using root-mean-square error (RMSE), Durbin–Watson statistic (DW) and Akaike's information criterion (AIC). The Wood and Dhanoa models provided the best fit of the lactation curve for MY in the first and second parities due to the lower values of RMSE and AIC than other models; but the Dijkstra model showed the best fit of milk lactation curve for third-parity dairy cows, FC, PC and SCS in the first three parities because of the lowest values of RMSE and AIC. Also, In general, the Sikka model did not fit the production data as well as the other equations. The results showed that the Dijkstra equation was able to estimate the time to the peak and peak MY more accurately than the other equations. However, the Wood equation provided more accurate predictions of peak MY at second- and third parities than the other equations. For first lactation FC, the Dijkstra equation was able to estimate the minimum FC and for second- and third-parity FC, the Wood equation provided more accurate predictions of minimum FC. For first- and second-lactation PC, the Dijkstra equation was able to estimate the minimum PC but for third parity, the minimum value of PC was predicted more accurately by the Wood model. The Dhanoa and Dijkstra equations for first lactation SCS and the Dhanoa equation for second- and third- lactation SCS were able to estimate the minimum SCS more accurately than the other equations. Overall, evaluation of different equations used in the current study indicated the potential of the non-linear functions for fitting monthly productive records of Holstein cows.

Biogenic nitrogen (N2) and nitrous oxide (N2O) accumulations were measured in groundwater, streams and the vadose zone of small agricultural watersheds in the Mid-Atlantic USA. In general, N2 and N2O in excess of atmospheric equilibrium were found in groundwater virtually everywhere that was sampled. Excess N2 in groundwater ranged from undetectable to 616 μmol N2-N/l, the latter representing c. 50% of background N2. The N2O-N concentrations varied from undetectable to 75 μm, and usually greatly exceeded values at atmospheric equilibrium (25–30 nM); however, N2O was generally 1–10% of excess N2. Intermediate levels of deficit and excess N2 in flowing streams (−65 to +250 μmol N2-N/L) resulting from both abiotic and biotic processes were also measured. In vadose zone gases, multiple N2/Ar gas profiles were measured which exhibited seasonal variations with below atmospheric values when the soil was warming in spring/summer and above atmospheric values when groundwater was cooling in fall/winter. Both abiotic and biotic processes contributed to the excess N2 and N2O that was observed. The current data indicate that large concentrations of excess N gases can accumulate within soil, groundwater, and streams of agriculturally dominated watersheds. When excess N gases are exchanged with the atmosphere, the net fluxes to the atmosphere may represent an important loss term for watershed N budgets.

The Thompson model (Thompson et al., in press), a heat balance model for cattle, was evaluated for Bos indicus and B. taurus under different climate conditions through the use of two local and one global sensitivity analyses and tested against independent datasets. The local analyses, which evaluate the individual effects of parameters on model output, showed that the vasodilation/vasoconstriction parameter and reference body temperature (Tbref) strongly affected body temperature. The global analysis, which evaluates the overall effect of parameters on model output, showed that 6 out of 24 parameters account for 0·79–0·89 of the model variation. The high proportion of variation accounted for by the parameters demonstrates that the model is linear in its parameters, with little interaction between the parameters.

The Thompson model was tested against four independent datasets which included both B. indicus and B. taurus animals. The prediction of the relationship between skin and body temperature from the model aligned closely with the relationship in the datasets (R2 ranged from 0·55 to 0·87, mean bias ranged from 0·32 to 1·49). The prediction of sweating and respiration rates from the model aligned closely with the rates measured in the datasets (R2 ranged from 0·80 to 0·98 and 0·79 to 0·93, respectively). The delay in the diurnal body temperature variation, relative to air temperature, was more accurately predicted for cattle in the sun than for cattle in climate chambers. Given the limited datasets for construction and parameterization (both of which are described in Thompson et al., in press), the model evaluated in the current study performed relatively well compared to the literature and known biology.

Studying and understanding the sources of variation in early life traits in farmed deer are fundamental for management and/or breeding purposes. Data from a captive white-tailed deer population were analysed to identify non-genetic and genetic factors affecting the birth weight (BW) of fawns. The year, type of birth and sex were included in a fixed linear model to examine their significance. All of the examined non-genetic factors had a highly significant effect on BW (P<0·001). The examined years showed variation attributed to food availability affecting the gestational conditions of does. Male fawns were 193 g heavier than female fawns at birth (P<0·001), and singleton births were associated with a higher BW (2·97±0·043 kg) compared with twin (−0·261 g) and triplet (−0·642 g) fawning (P<0·001). The best-fitting animal model was selected by comparing reduced and complete models. Based on the selected animal model, which included direct genetic and common maternal effects, genetic components and parameters were estimated. The direct heritability was found to be 0·28±0·126, and a small but important contribution of common maternal environmental effects was identified (c2=0·15±0·062). The results support the importance of certain environmental factors affecting BW and indicate the relevance of direct genetic and maternal environmental influences to sustained genetic changes in BW and positively correlated traits in farmed white-tailed deer populations.

A set of independent assays were conducted to assess the effects of either pH or glucose concentration on forage degradation, bacterial adherence and on fibrolytic enzyme activity in vitro. For measuring degradation and bacterial adherence, ryegrass (Lolium multiflorum) and bermudagrass (Cynodon dactylon) samples were incubated in vitro for 24 h in the medium at different pH (5·5, 6·0, 6·5 or 7·0) or with different initial glucose concentrations (0, 1000, 2000 and 3000 mg/l). For fibrolytic enzyme activity evaluation, forage samples were incubated in situ and the extracted enzymes were incubated in vitro under the different pH and glucose treatment conditions. The amount of bacteria adhering to samples and the degradability of dry matter (DM) and neutral detergent fibre (NDF) were higher for ryegrass than for bermudagrass, were not affected by glucose concentration and were linearly and positively affected by increased pH. On average, carboxymethylcellulase (CMCase) and xylanase activities were higher for ryegrass than for bermudagrass in the pH assay, whereas the differences between forages were not significant in the glucose assay. For both forage species, the quadratic effect of pH or glucose concentration on CMCase and xylanase activities was significant. Maximum activity of both enzymes was observed at pH 6·0 or at glucose concentration of 2000 mg/l. In conclusion, forage degradation was affected negatively by decreased ruminal pH due to reduced bacterial adherence. In turn, the pH or glucose effect on fibrolytic enzyme activity was not related to their effects on bacterial adherence or forage degradation, indicating that forage degradation is more dependent on the degree of microbial colonization than on the specific activity of bacterial enzymes.

The objective of the current study was to assess the use of nonlinear mixed model methodology to fit the growth curves (weight v. time) of two dairy goat genotypes (Alpine, +A and Saanen, +S). The nonlinear functions evaluated included Brody, Von Bertalanffy, Richards, Logistic and Gompertz. The growth curve adjustment was performed using two steps. First, random effects u1, u2 and u3 were linked to the asymptotic body weight (β1), constant of integration (β2) and rate constant of growth (β3) parameters, respectively. In addition to a traditional fixed-effects model, four combinations of models were evaluated using random variables: all parameters associated with random effects (u1, u2 and u3), only β1 and β2 (u1 and u2), only β1 and β3 (u1 and u3) and only β1 (u1). Second, the fit of the best adjusted model was refined by using the power variance and modelling the error structure. Residual variance ($\sigma _e^2 $) and the Akaike information criterion were used to evaluate the models. After the best fitting model was chosen, the genotype curve parameters were compared. The residual variance was reduced in all scenarios for which random effects were considered. The Richards (u1 and u3) function had the best fit to the data. This model was reparameterized using two isotropic error structures for unequally spaced data, and the structure known in the literature as SP(MATERN) proved to be a better fit. The growth curve parameters differed between the two genotypes, with the exception of the constant that determines the proportion of the final size at which the inflection point occurs (β4). The nonlinear mixed model methodology is an efficient tool for evaluating growth curve features, and it is advisable to assign biologically significant parameters with random effects. Moreover, evaluating error structure modelling is recommended to account for possible correlated errors that may be present even when using random effects. Different Richard growth curve parameters should be used for the predominantly Alpine and Saanen genotypes because there are differences in their growth patterns.