From 1999 to 2006, 36 field experiments were conducted in five sugar beet growing areas in Greece (Larissa, Plati, Serres, Xanthi and Orestiada) to monitor yield. Locations differed significantly regarding thermal variables during the growing season with Xanthi having the most favourable thermal conditions (Tmax, average daily maximum temperature; Tmean, average daily mean temperature; GDD, growing degree days) for sugar beet growth. From early June to the end of the harvesting campaign, successive harvests were conducted. Over the years, fresh root weight and sugar yield at the last harvest of the season (FRWLH, SYLH) did not differ significantly among locations. Also, there were no significant differences among locations regarding GDD for maximum FRW and SY (GDDMFRW, GDDMSY), with the means over location estimated at 2639·9 and 2792·5 °C, respectively. Days after seeding (DAS) necessary for maximum yield (DASMFRW, DASMSY, respectively) differed among locations, with the longest period (DASMFRW 206·4 days, DASMSY: 204·5 days) occurring in the northernmost location (Orestiada). Means for DASMFRW and DASMSY at the five locations were estimated at 190·4 and 188·9·days, respectively. Excluding Xanthi and combining the remaining locations, FRWLH and SYLH were negatively correlated with the average temperatures (Tmean, Tmax and Tmin, daily minimum temperature) over the growing season. The opposite was evident for Xanthi where sugar beet was grown under sub-optimal temperatures. The optimum mean Tmax of the five locations was estimated at 25·5 and 25·1 °C for FRWLH and SYLH, respectively. Elongation of the growing season, by means of early sowing, would increase yield by decreasing average temperatures (Tmean, Tmax) over the growing season in locations with the highest recorded temperatures (Larissa, Plati, Serres and Orestiada). In Xanthi, the projected temperature increase, as a result of climate change, is expected to have a positive effect on yields.

The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0·82 to 0·96 for the calibration database (the median coefficient of determination (R2) was 0·71), while IA for verification varied from 0·62 to 0·93 (median R2 was 0·78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0·76 and 0·72 for calibration and verification, respectively, and analogous medians of R2 were 0·50 and 0·49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0·49 to 0·74 for calibration and from 0·43 to 0·68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Němčice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0–0·3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.

Predicted future climate changes in northern Europe include increased air temperature and altered precipitation patterns. There is a lack of knowledge about potential climate change effects on the biomass yield and security of agricultural crops. The present study determined the potential impact of future climate change on the yield and harvest security of timothy (Phleum pratense L.). Harvest security was assessed using data on accumulated precipitation and the length of dry spell period within the 7 days after cutting. Timothy production as a function of weather, soil and management practices was simulated using the LINGRA model for the periods 1961–90, 2046–65 and 2080–99, and the locations Apelsvoll, Ås, Sola, Tromsø and Værnes in Norway and harvest systems with 600 and 800 °C days between cuts. One hundred years of daily weather data were generated with the LARS-WG tool, using future daily weather data sets based on 12 Global Climate Models. Total seasonal biomass yield varied between 690 g dry matter (DM)/m2 for the 800 °C days harvesting regime in the period 1961–90 at Tromsø and 1548 g DM/m2 for the same harvesting regime in the period 2046–65 at Sola. In general, the biomass was higher in the two future periods than in 1961–90 across locations and harvesting regimes, mainly owing to more cuts per season. Accumulated precipitation after cutting varied between 12·2 mm after the first cut for the 600 °C days harvesting regime in the period 1961–90 at Værnes and 42·5 mm after the fourth cut in the 800 °C days harvesting regime in the period 2080–99 at Sola. The longest duration of dry spell 7 days after pre-planned harvest varied between 1·8 days after the fourth cut at Sola in the 600 °C days harvesting regime for the period 2080–99, and 3·9 days after the first cut at Ås in the 800 °C days harvesting regime for the period 2046–65. Potential consequences of these results are discussed.

Combustion of biogas containing hydrogen sulphide (H2S) yields highly corrosive sulphur oxides, thereby limiting the use of biogas for heat and power generation. The current study developed and tested a pilot-scale biogas bio-filter system (BBS) to replace conventional water scrubbing systems at a commercial pig farm in Miaoli County, Taiwan. A 62·8-litre pilot-scale BBS, packed with dried aerial roots from the common tree fern (Sphaeropteris lepifera) and a mixture of dried aerial roots and plastic rings as bio-carriers and inoculated with sulphur oxidizing bacteria, was installed at a commercial pig farm. H2S concentration was 5600 mg/m3 and the biogas flow rate was 4 litres/min. The pilot-scale BBS could remove 26·9 g H2S/m3/h using a bio-filter that operated at a 7% (v/v) O2 level continuously for >200 days. It was found that most H2S was oxidized to elemental sulphur (S0) and accumulated on the surface of the dried aerial roots (0·084 g S0/g aerial root) in the BBS. The dried aerial roots and plastic Raschig ring mixture was more efficent at sulphur dioxide removal than the dried aerial roots on their own. The pH and sulphate (SO42−) concentrations of water dropping from the BBS were 1·6 ± 1·0 and 28 680 ± 8253 mg/l, respectively. However, sulphur is the dominant product of sulphur oxidizers at the 7% O2 level. Periodical flushing of the bio-filter with the effluent from the wastewater treatment system on the farm can maintain high efficiency of H2S removal.

The present study investigated the historical occurrence of the European corn borer (Ostrinia nubilalis), the European grape vine moth (Lobesia botrana) and the Codling moth (Cydia pomonella) in southern Moravia and northern Austria from 1803–2008 by using climate and pest models. The pest model used, CLIMEX, indicates areas that are climatically favourable for the pest's development and long-term survival, considering the climatic parameters, especially daily air temperature, as determining factors for pest development. For model input parameters, two sets of meteorological data were prepared: (i) a generated meteorological series for 1803–2008 and (ii) a measured reference meteorological series for 1976–2008. In addition to estimating the historical climatic suitability for the persistence of a given pest, a second aim of the present study was to specify the core of the climatic niche during the continued presence of the pest and evaluate the applicability of the meteorological data generated for climate, based on pest mapping. This evaluation resulted in a partial overestimation of pest occurrence for L. botrana when using the generated meteorological data set. This species, native to warmer areas, has proved to be a sensitive indicator of increased temperatures.

A field experiment was conducted to investigate the effects of powdery mildew (Blumeria graminis f. sp. Tritici, Bgt) on wheat grain at varying levels of disease severity and at different growth stages. Methods used to determine these effects included Kjeldahl determination, unidimensional polyacrylamide gel electrophoresis, dielectrophoresis combined with mass spectrometric analysis. The specific influences explored were those on prolamins and protein composition at the molecular level. Concentrations of both grain protein and prolamin in wheat increased as disease indices (DIs) of powdery mildew rose from 20 days after anthesis (DAA) to maturity. Globulin concentrations changed dynamically and significantly, especially at 25 DAA when DI was the highest. This was verified by proteomic analysis which showed globulins (such as globulin 3, globulin 3B, globulin 3C, gliadin/avenin-like protein and triticin) being up-regulated significantly under powdery mildew stress. It was proposed that powdery mildew might indirectly affect protein accumulation in grain by influencing the regulative enzymes (including peptidylprolyl isomerase, cyclophilin A-2 and GTPase ObgE) and metabolic processes. It was speculated that the indirect increase caused by yield reduction was not the only factor causing the increase in prolamin concentration. Another factor may be the rise of expression level of molecular chaperones and enzymes relating to protein synthesis, which led to the rise in protein synthesis.

Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the conventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.

Sunflower (Helianthus annuus L.) and rapeseed (Brassica napus L.) are considered as the most suitable crops for biodiesel production in the Mediterranean basin. Soybean (Glycine max L.) could also be used, under certain conditions. In Greece, the farming practice adopted in each region varies significantly, leading to significant differences in the levels of emitted greenhouse gases (GHG). Greenhouse gas emissions were estimated during the cultivation phase as grams of carbon dioxide equivalents (g CO2e) per megajoule (MJ), followed by emission savings (%) estimation when fossil fuels are replaced by biodiesel. Crop and region comparisons provided important information towards promoting sustainability. Overall, sunflower demonstrated the lowest average emissions, 53·8 g CO2e/MJ, followed by rapeseed and soybean. Furthermore, rapeseed achieved the lowest emission saving level required by European legislation in most cases studied, with an average value of 37%. Irrigation and nitrogen fertilization were the operations mostly contributing to the total quantity of GHG emissions. More specifically, the highest GHG emissions were found for soybean irrigation (34%) and rapeseed nitrogen fertilization (68%).

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.

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.

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.

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.

Sainfoin is a temperate legume that contains condensed tannins (CT), i.e. polyphenols that are able to bind proteins and thus reduce protein degradation in the rumen. A reduction in protein degradation in the rumen can lead to a subsequent increase in amino acid flow to the small intestine. The effects of CT in the rumen and the intestine differ according to the amount and structure of CT and the nature of the protein molecular structure. The objective of the present study was to investigate the degradability in the rumen of three CT-containing sainfoin varieties and CT-free lucerne in relation to CT content and structure (mean degree of polymerization, proportion of prodelphinidins and cis-flavanol units) and protein structure (amide I and II bands, ratio of amide I-to-amide II, α-helix, β-sheet, ratio of α-helix-to-β-sheet). Protein molecular structures were identified using Fourier transform/infrared-attenuated total reflectance (FT/IR-ATR) spectroscopy. The in situ degradability of three sainfoin varieties (Ambra, Esparcette and Villahoz) was studied in 2008, during the first growth cycle at two harvest dates (P1 and P2, i.e. 5 May and 2 June, respectively) and at one date (P3) during the second growth cycle (2 June) and these were compared with a tannin-free legume, lucerne (Aubigny). Loss of dry matter (DMDeg) and nitrogen (NDeg) in polyester bags suspended in the rumen was measured using rumen-fistulated cows. The NDeg of lucerne compared with sainfoin was 0·80 v. 0·77 at P1, 0·78 v. 0·65 at P2 and 0·79 v. 0·70 at P3, respectively. NDeg was related to the rapidly disappearing fraction (‘a’) fraction (r=0·76), the rate of degradation (‘c’) (r=0·92), to the content (r=−0·81) and structure of CT. However, the relationship between NDeg and the slowly disappearing fraction (‘b’) was weak. There was a significant effect of date and species×date, for NDeg and ‘a’ fraction. The secondary protein structure varied with harvest date (species×date) and was correlated with the fraction ‘b’. Both tannin and protein structures influenced the NDeg degradation. CT content and structure were correlated to the ‘a’ fraction and to the ‘c’. Features of the protein molecular secondary structure were correlated to the ‘b’ fraction.