The temporal change in soil organic carbon (SOC) was analysed over an 80-year period based on climate change predictions of four regional circulation models under the International Panel on Climate Change (IPCC) A1B emission scenario in the 21st century. A 20-year (1991–2010) set of observed climate data was used to form the baseline, and generate synthetic data for future scenario analyses. With increasing carbon dioxide (CO2) levels, and under continuous winter wheat production with conventional tillage at different nitrogen (N) input rates, three crop-soil models were used to study the temporal changes of SOC. Results indicated that soil carbon (C) generally decreased over the simulation period. In addition, increased N losses through leaching and denitrification were estimated. Decline in soil C under continuous mono-cropping systems indicated increased focus on N fertilization strategies. The results also suggested significant interactive effect of N input rate and climate variables on soil C and denitrification in response to climate change. The uncertainty was addressed by including the crop-soil models in a mixed-effect analysis so that the contribution of the models to the total variance of random variation was quantified. Statistical analysis showed that the crop-soil models are the main source for uncertainty in analysing soil C and N responses to climate change.

Little is known about the quantitative relationships between wheat powdery mildew (Blumeria graminis f.sp. tritici) epidemics and climatic variables at the provincial scale in China, particularly under climate change. The present study assesses the actual disease process and corresponding impact on wheat yield and addresses climatic-driven variables that affect a powdery mildew epidemic. Powdery mildew increased in frequency from 1981 to 2010, and wheat yield decreased in most regions. It was clear that differences in disease and yield loss occurred temporally and spatially. Although particular weather variables were positively or negatively related to the disease, multiple stepwise regression analysis indicated that mostly fewer than three variables affected prevalence and severity of powdery mildew in each province. In most cases, some combination of higher temperature, humidity, rainfall and wind led to higher disease severity. These weather factors had different effects on disease development. The influence of climatic variables on powdery mildew tended to decrease from 1981 to 2010, whereas the effect of non-climatic factors increased and was attributed mainly to the use of fungicides and resistant cultivars. Therefore, the results of the current study suggest that wheat powdery mildew in China will not increase consistently in the future. In addition, the quantitative assessment method used in the current study can generally provide a good way to identify disease epidemics under climate change.

Spodoptera frugiperda, or the fall armyworm (FAW) (Lepidoptera: Noctuidae), is an endemic and important agricultural pest in America. Several outbreaks have occurred with losses estimated at millions of dollars. Insects are affected by climate factors, and climate change may affect geographical range, growth rate, abundance, survival, mortality, number of generations per year and other characteristics. These effects are difficult to project due to the complex interactions among insects, hosts and predators. The aim of the current research is to project the impact of climate change on future suitability for the expansion and final range of FAW as well as highlight the risk of damage due to the pest under current and future conditions. The modelling was carried out using two general circulation models (GCMs), CSIRO Mk3.0 and MIROC-H, for 2050 and 2100 under the A2 Special Report on Emissions Scenarios (SRES), using the known distribution of the species and the CliMond meteorological database. The possible number of generations was estimated to exceed five in the south-eastern USA by 2100. A unique modelling approach linking environmental suitability and number of generations was developed to project the risks of FAW damage. The results show changes in suitability and risk across America, with an increase in the northern hemisphere and decreases or extinction in the southern hemisphere, except for southern Brazil, Uruguay, Paraguay and northern Argentina, which indicate high future levels of risk. The current study highlights the possible extinction of a tropical pest in areas near the Equator. The two GCMs both projected increases in the low-risk category of 40% by 2050 and 23% by 2100, with the medium- and high-risk categories decreasing by >50% by 2050 and >39% by 2100, compared with the current risk. In general, agricultural pest management may become more challenging under future climate change and variation, and thus, understanding and quantifying the possible impacts of FAW under future climate conditions is essential for the future economic production of crops.

Date palm (Phoenix dactylifera L.) is an important cash crop in many countries, including Saudi Arabia. Understanding the likely potential distribution of this crop under current and future climate scenarios will enable environmental managers to prepare appropriate strategies to manage the changes. In the current study, the simulation model CLIMEX was used to develop a niche model to estimate the impacts of climate change on the current and future potential distribution of date palm. Two global climate models (GCMs), CSIRO-Mk3·0 and MIROC-H under the A2 emission scenario for 2050 and 2100, were used to assess the impacts of climate change. A sensitivity analysis was conducted to identify which model parameters had the most effect on date palm distribution. Further refinements of the potential distributions were performed through the integration of six non-climatic parameters in a geographic information system. Areas containing suitable soil taxonomy, soil texture, soil salinity, land use, landform and slopes of <7° for date palm were selected as suitable refining variables in order to achieve more realistic models. The results from both GCMs exhibited a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100. Climate sensitivity analysis indicates that the lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the most effect on sensitivity, while other parameters were moderately sensitive or insensitive to change. The study also demonstrated that the inclusion of non-climatic parameters with CLIMEX outputs increased the explanatory power of the models. Such models can provide early warning scenarios for how environmental managers should respond to changes in the distribution of the date palm in Saudi Arabia.

Studies on climate impacts and related adaptation strategies are becoming increasingly important to counteract the negative impacts of climate change. In Ethiopia, climate change is likely to affect crop yields negatively and therefore food security. However, quantitative evidence is lacking about the ability of farm-level adaptation options to offset the negative impacts of climate change and to improve food security. The MarkSim Global Climate Model weather generator was used to generate projected daily rainfall and temperature data originally taken from the ECHAM5 general circulation model and ensemble mean of six models under high (A2) and low (B1) emission scenarios. The FAO AquaCrop model was validated and subsequently used to predict maize yields and explore three adaptation options: supplemental irrigation (SI), increasing plant density and changing sowing date. The maximum level of maize yield was obtained when the second level of supplemental irrigation (SI2), which is the application of irrigation water when the soil water depletion reached 75% of the total available water in the root zone, is combined with 30 000 plants/ha plant density. It was also found that SI has a marginal effect in good rainfall years but using 94–111 mm of SI can avoid total crop failure in drought years. Hence, SI is a promising option to bridge dry spells and improve food security in the Rift Valley dry lands of Ethiopia. Expected longer dry spells during the shorter rainy season (Belg) in the future are likely to further reduce maize yield. This predicted lower maize production is only partly compensated by the expected increase in CO2 concentration. However, shifting the sowing period of maize from the current Belg season (mostly April or May) to the first month of the longer rainy season (Kiremt) (June) can offset the predicted yield reduction. In general, the present study showed that climate change will occur and, without adaptation, will have negative effects. Use of SI and shifting sowing dates are viable options for adapting to the changes, stabilizing or increasing yield and therefore improving food security for the future.

Palm oil (PO) is a very important commodity used as food, in pharmaceuticals, for cooking and as biodiesel: PO is a major contributor to the economies of many countries, especially Indonesia and Malaysia. Novel tropical regions are being explored increasingly to grow oil palm as current land decreases, whilst recent published modelling studies by the current authors for Malaysia and Indonesia indicate that the climate will become less suitable. Countries that grow the crop commercially include those in Latin America, Africa and Asia. How will climate change (CC) affect the ability to grow oil palm in these countries? Worldwide projections for apt climate were made using Climex software in the present paper and the global area with unsuitable climate was assessed to increase by 6%, whilst highly suitable climate (HSC) decreased by 22% by 2050. The suitability decreases are dramatic by 2100 suggesting regions totally unsuitable for growing OP, which are currently appropriate: the global area with unsuitable climate increased from 154 to 169 million km2 and HSC decreased from 17 to 4 million km2. This second assessment of Indonesia and Malaysia confirmed the original findings by the current authors of large decreases in suitability. Many parts of Latin America and Africa were dramatically decreased: reductions in HSC for Brazil, Columbia and Nigeria are projected to be 119 000, 35 and 1 from 5 000 000, 219 and 69 km2, respectively. However, increases in aptness were observed in 2050 for Paraguay and Madagascar (HSC increases were 90 and 41%, respectively), which were maintained until 2100 (95 and 45%, respectively). Lesser or transient increases were seen for a few other countries. Hot, dry and cold climate stresses upon oil palm for all regions are also provided. These results have negative implications for growing oil palm in countries as: (a) alternatives to Malaysia and Indonesia or (b) economic resources per se. The inability to grow oil palm may assist in amelioration of CC, although the situation is complex. Data suggest a moderate movement of apposite climate towards the poles as previously predicted.

The effects of soil variability on regional crop yield under projected climate change are largely unknown. In Southeastern Norway, increased temperature and precipitation are projected for the mid-21st century. Crop simulation models in combination with scaling techniques can be used to determine the regional pattern of crop yield. In the present paper, the CSM-CROPSIM-CERES-Wheat model was applied to simulate regional spring wheat yield for Akershus and Østfold counties in Southeastern Norway. Prior to the simulations, parameters in the CSM-CROPSIM-CERES-Wheat model were calibrated for the spring wheat cvars Zebra, Demonstrant and Bjarne, using cultivar trial data from Southeastern Norway and site-specific weather and soil information. Weather input data for regional yield simulations represented the climate in 1961–1990 and projections of the climate in 2046–2065. The latter were based on four Global Climate Models and greenhouse gas emission scenario A1B in the IPCC 4th Assessment Report. Data on regional soil particle size distribution, water-holding characteristics and organic matter data were obtained from a database. To determine the simulated grain yield sensitivity to soil input, the number of soil profiles used to describe the soilscape in the region varied from 76 to 16, 5 and 1. The soils in the different descriptions were selected by arranging them into groups according to similarities in physical characteristics and taking the soil in each group occupying the largest area in the region to represent other soils in that group. The simulated grain yields were higher under all four projected future climate scenarios than the corresponding average yields in the baseline conditions. On average across the region, there were mostly non-significant differences in grain yield between the soil extrapolations for all cultivars and climate projections. However, for sub-regions grain yield varied by up to 20% between soil extrapolations. These results indicate how projected climate change could affect spring wheat yield given the assumed simulated conditions for a region with similar climate and soil conditions to many other cereal production regions in Northern Europe. The results also provide useful information about how soil input data could be handled in regional crop yield determinations under these conditions.

Drought risk is considered to be among the main limiting factors for maize (Zea mays L.) production in the Northeast Farming Region of China (NFR). Maize yield data from 44 stations over the period 1961–2010 were combined with data from weather stations to evaluate the effects of climatic factors, drought risk and irrigation requirement on rain-fed maize yield in specific maize growth phases. The maize growing season was divided into four growth phases comprising seeding, vegetative, flowering and maturity based on observations of phenological data from 1981 to 2010. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, solar radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961–2010, mean temperature increased significantly in all growth phases in NFR, while solar radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases, reducing water deficit over the period, whereas decreasing effective rainfall over time in the flowering and maturity phases enhanced water deficit. An increase in days with drought stress was concentrated in western NFR, with larger volumes of irrigation needed to compensate for increased dryness. The present results indicate that higher mean temperature in the seeding and maturity phases was beneficial for maize yield, whereas excessive rainfall would damage maize yield, in particular in the seeding and flowering phases. Drought stress in any growth stage was found to reduce maize yield and water deficit was slightly better than other indicators of drought stress for explaining yield variability. The effect of drought stress was particularly strong in the seeding and flowering phases, indicating that these periods should be given priority for irrigation. The yield-reducing effects of both drought and intense rainfall illustrate the importance of further development of irrigation and drainage systems for ensuring the stability of maize production in NFR.

Climate change is expected to affect optimum agricultural management practices for autumn-sown wheat, especially those related to sowing date and nitrogen (N) fertilization. To assess the direction and quantity of these changes for an important production region in eastern Austria, the agricultural production systems simulator was parameterized, evaluated and subsequently used to predict yield production and grain protein content under current and future conditions. Besides a baseline climate (BL, 1981–2010), climate change scenarios for the period 2035–65 were derived from three Global Circulation Models (GCMs), namely CGMR, IPCM4 and MPEH5, with two emission scenarios, A1B and B1. Crop management scenarios included a combination of three sowing dates (20 September, 20 October, 20 November) with four N fertilizer application rates (60, 120, 160, 200 kg/ha). Each management scenario was run for 100 years of stochastically generated daily weather data. The model satisfactorily simulated productivity as well as water and N use of autumn- and spring-sown wheat crops grown under different N supply levels in the 2010/11 and 2011/12 experimental seasons. Simulated wheat yields under climate change scenarios varied substantially among the three GCMs. While wheat yields for the CGMR model increased slightly above the BL scenario, under IPCM4 projections they were reduced by 29 and 32% with low or high emissions, respectively. Wheat protein appears to increase with highest increments in the climate scenarios causing the largest reductions in grain yield (IPCM4 and MPEH-A1B). Under future climatic conditions, maximum wheat yields were predicted for early sowing (September 20) with 160 kg N/ha applied at earlier dates than the current practice.

A warmer and potentially drier future climate is likely to influence the production of forage crops on dairy farms in the southeast dairy regions of Australia. Biophysical modelling was undertaken to explore the resilience of forage production of individual forage crops to scalar increases in temperature, atmospheric carbon dioxide (CO2) concentration and changes in daily rainfall. The model APSIM was adapted to reflect species specific responses to growth under elevated atmospheric CO2 concentrations. It was then used to simulate 40 years of production of forage wheat, oats, annual ryegrass, maize grown for silage, forage sorghum, forage rape and alfalfa grown at three locations in southeast Australia with increased temperature scenarios (1, 2, 3 and 4 °C of warming) and atmospheric CO2 concentration (435, 535, 640 and 750 ppm) and decreasing rainfall scenarios (10, 20 or 30% less rainfall). At all locations positive increases in DM yield compared with the baseline climate scenario were predicted for lucerne (2·6–93·2% increase), wheat (8·9–37·4% increase), oats (6·1–35·9% increase) and annual ryegrass (9·7–66·7% increase) under all future climate scenarios. The response of forage rape and forage sorghum varied between location and climate change scenario. At all locations, maize was predicted to have a minimal change in yield under all future climates (between a 2·6% increase and a 6·8% decrease). The future climate scenarios altered the seasonal pattern of forage supply for wheat, oats and lucerne with an increase in forage produced during winter. The resilience of forage crops to climate change indicates that they will continue to be an important component of dairy forage production in southeastern Australia.

Increasing recognition of the extent to which nitrous oxide (N2O) contributes to climate change has resulted in greater demand to improve quantification of N2O emissions, identify emission sources and suggest mitigation options. Agriculture is by far the largest source and grasslands, occupying c. 0·22 of European agricultural land, are a major land-use within this sector. The application of mineral fertilizers to optimize pasture yields is a major source of N2O and with increasing pressure to increase agricultural productivity, options to quantify and reduce emissions whilst maintaining sufficient grassland for a given intensity of production are required. Identification of the source and extent of emissions will help to improve reporting in national inventories, with the most common approach using the IPCC emission factor (EF) default, where 0·01 of added nitrogen fertilizer is assumed to be emitted directly as N2O. The current experiment aimed to establish the suitability of applying this EF to fertilized Scottish grasslands and to identify variation in the EF depending on the application rate of ammonium nitrate (AN). Mitigation options to reduce N2O emissions were also investigated, including the use of urea fertilizer in place of AN, addition of a nitrification inhibitor dicyandiamide (DCD) and application of AN in smaller, more frequent doses. Nitrous oxide emissions were measured from a cut grassland in south-west Scotland from March 2011 to March 2012. Grass yield was also measured to establish the impact of mitigation options on grass production, along with soil and environmental variables to improve understanding of the controls on N2O emissions. A monotonic increase in annual cumulative N2O emissions was observed with increasing AN application rate. Emission factors ranging from 1·06–1·34% were measured for AN application rates between 80 and 320 kg N/ha, with a mean of 1·19%. A lack of any significant difference between these EFs indicates that use of a uniform EF is suitable over these application rates. The mean EF of 1·19% exceeds the IPCC default 1%, suggesting that use of the default value may underestimate emissions of AN-fertilizer-induced N2O loss from Scottish grasslands. The increase in emissions beyond an application rate of 320 kg N/ha produced an EF of 1·74%, significantly different to that from lower application rates and much greater than the 1% default. An EF of 0·89% for urea fertilizer and 0·59% for urea with DCD suggests that N2O quantification using the IPCC default EF will overestimate emissions for grasslands where these fertilizers are applied. Large rainfall shortly after fertilizer application appears to be the main trigger for N2O emissions, thus applicability of the 1% EF could vary and depend on the weather conditions at the time of fertilizer application.

Grazing season length (GSL) on grassland farms with ruminant production systems can influence farm economics, livestock disease transmission, environmental impact, milk and meat quality, and consumer choice. Bioclimatic variables are biologically meaningful climate variables that may enable predictions of the impact of future climate change on GSL on European farms. The present study investigated the spatial relationship between current GSL (months) measured by EUROSTAT on dairy, beef and sheep farms in 706, 774 and 878 regions, respectively, and bioclimatic variables. A stepwise multiple regression model revealed a highly significant association between observed GSL and bioclimatic variables across Europe. Mean GSL was positively associated with the mean temperature of the coldest quarter and isothermality, and negatively associated with precipitation in the wettest month. Extrapolating these relationships to future climate change scenarios, most European countries were predicted to have a net increase in GSL with the increase being largest (up to 2·5 months) in the north-east of Europe. However, there were also predictions of increased variability between regions and decreases in GSL of up to 1·5 months in some areas such as the west of France, the south-west of Norway and the west coast of Britain. The study quantified and mapped the potential impact of climate change on GSL for dairy, beef and sheep farms across Europe.

Phenological models are considered key tools for the short-term planning of viticultural activities and long-term impact assessment of climate change. In the present study, statistical phenological models were developed for budburst (BUD), flowering (FLO) and veraison (VER) of 16 grapevine varieties (autochthonous and international) from the Portuguese wine-making regions of Douro, Lisbon and Vinhos Verdes. For model calibration, monthly averages of daily minimum (Tmin), maximum (Tmax) and mean (Tmean) temperatures were selected as potential regressors by a stepwise methodology. Significant predictors included Tmin in January–February–March for BUD, Tmax in March–April for FLO, and Tmin, Tmax and Tmean in March–July for VER. Developed models showed a high degree of accuracy after validation, representing 0·71 of total variance for BUD, 0·83 for FLO and 0·78 for VER. Model errors were in most cases < 5 days, outperforming classic growing degree-day models, including models based on optimized temperature thresholds for each variety. Applied to the future scenarios RCP4·5/8·5, projections indicate earlier phenophase onset and shorter interphases for all varieties. These changes may bring significant challenges to the Portuguese wine-making sector, highlighting the need for suitable adaptation/mitigation strategies, to ensure its future sustainability.

Analyses of the geographic patterns of wild species abundance have been carried out in studies ranging from those interested in hot-spots for biodiversity and conservation to basic ecological analyses. Based on the methodological approaches used, the present study searched for areas of higher yield among native races of maize in Mexico, its centre of origin and diversification. Ecological theory suggests that population fitness, and thus abundance, is maximal at the centroid of the multi-dimensional ecological niche of a particular species, and decreases progressively as distance from it increases. In the present study, yield was used instead of abundance, assuming it to be higher under optimal environmental conditions. It was assessed whether nine Mexican maize races exhibited higher yields in areas that are ecologically closer to their niche centroid (NC), or a niche optimum (NO) that did not always coincide with the geometric centroid. Environmental and geographical clusters for each race were also created to identify additional NOs in widely distributed races. All races showed significant correlations between yield and distance, both to the NC or NOs, but in only six of them was the chosen model better than the null model. Three races and two sub-groups were selected for projection under climate change conditions: Celaya (R2 = 0·288), Celaya 3 (R2 = 0·774), Vandeño (R2 = 0·277) and Vandeño 2 (R2 = 0·466) and Tepecintle (R2 = 0·537). The Celaya race improved with environmental clustering, while Vandeño improved under geographical clustering. Finally, Tepecintle was projected at a race level. Projections under climate change suggested some potential areas for high yields in the future. It was concluded that the approach used in the present paper, considering the distance to NC/NO as a predictor of maize landrace yield, may contribute important information to agro-ecological projects.

The present study applies refined and improved scenarios for climate change to quantify the effects of potential alterations in climatic factors on localities for wheat and cotton production, which are two crops important to Australia's economy. The future distributions of Gossypium (cotton) and Triticum aestivum L. (wheat) were modelled using CLIMEX software with the A2 emission scenario generated by CSIRO-Mk3·0 and MIROC-H global climate models. The results were correlated to identify areas suitable for these economically important crops for the years 2030, 2050, 2070 and 2100 in Australia. The analysis shows that the areas where wheat and cotton can be grown in Australia will diminish from 2030 to 2050 and 2070 through to 2100. While cotton can be grown over extensive areas of the country until 2070, the area grown to wheat will decrease significantly over the period.

Chamber sampling is a common method for measuring nitrous oxide (N2O) emissions from agricultural soils. However, for grazed pastures, the patchy nature of urine deposition results in very high levels of spatial variability in N2O emissions. In the present study, the behaviour of the sample mean was examined by simulating a large number (9999) of random N2O chamber samples under different assumptions regarding the underlying N2O distribution. Using sample sizes of up to 100 chambers, the Central Limit Theorem did not apply. The distribution of the sample mean was always right-skewed with a standard deviation varying between 12·5 and 135% of the true mean. However, the arithmetic mean was an unbiased estimator and the mean of the sample mean distribution was close to the true mean of the simulated N2O distribution. The properties of the sample mean distribution (variance, skewness) were affected significantly by the assumed distribution of the emission factor, but not by distribution of the urine patch concentration. The geometric mean was also investigated as a potential alternative estimator. However, although its distribution had lower variance, it was also biased. Two methods for bias correcting the mean were investigated. These methods reduced the bias, but at the cost of increasing the variance. Neither of the bias-corrected estimators were consistently better than the arithmetic mean in terms of skewness and variance. To improve the estimation of N2O emissions from a grazed pasture using chambers, techniques need to be developed to identify urine patch and non-urine patch areas before sampling.

The historical temperature (1981–2007) and phenological (1986–2011) data were analysed for the region of Sremski Karlovci, one of the oldest grapevine growing areas in Europe, with the aim of detecting trends of changes in the data, evaluating the sensitivity of grapevine phenology to temperature and revealing diversity among cultivars in their response to observed changes in temperature. The onset dates of four major phenological stages (budburst, flowering, veraison and harvest), along with the corresponding growth intervals between them, were examined for 20 wine grape cultivars. A number of climatically important parameters for viticulture were calculated for the calendar year, growing season and different grapevine growth periods. Significant increases were detected in average and heat-related extreme temperature indices. The greatest rate of change in temperature variables across the growing season was observed during the period from the beginning of flowering to the beginning of veraison and the smallest during the ripening period. Linear trends indicated that all phenological stages, except budburst, have advanced significantly. Averaged across all cultivars, detected trends were –0·4, –0·7 and –0·6 days/year for the beginning of flowering, the beginning of veraison and harvest date, respectively. Observed warming and change in the timing of phenological events did not significantly affect the duration of the growth intervals, which can be explained by significant inter-correlation between the phenological stages’ onset. Ripening was occurring under warmer conditions due to earlier flowering and veraison, rather than because of considerably higher temperatures preceding harvest or shortening of the ripening period. Most of the variation in phenology timing (74–90%) can be explained by a linear relationship between the onset date of phenological stage and temperature, with mean and maximum temperatures being more important than minimum temperatures. According to the current results, a 1 °C increase in the most influential temperature variable during the most relevant periods for the onset of phenological stages led to an advancement in the beginning of budburst, the beginning of flowering, the beginning of veraison and harvest by 3·6, 3·1, 5·2 and 7·4 days, respectively, on average for all cultivars. Among the cultivars studied, Pinot Noir displayed the greatest phenology advancement in response to increased temperature.

Brazil is one of the most important soybean producers in the world. Soybean is a very important crop for the country as it is used for several purposes, from food to biodiesel production. The levels of soybean yield in the different growing regions of the country vary substantially, which results in yield gaps of considerable magnitude. The present study aimed to investigate the soybean yield gaps in Brazil, their magnitude and causes, as well as possible solutions for a more sustainable production. The concepts of yield gaps were reviewed and their values for the soybean crop determined in 15 locations across Brazil. Yield gaps were determined using potential and attainable yields, estimated by a crop simulation model for the main maturity groups of each region, as well as the average actual famers’ yield, obtained from national surveys provided by the Brazilian Government for a period of 32 years (1980–2011). The results showed that the main part of the yield gap was caused by water deficit, followed by sub-optimal crop management. The highest yield gaps caused by water deficit were observed mainly in the south of Brazil, with gaps higher than 1600 kg/ha, whereas the lowest were observed in Tapurah, Jataí, Santana do Araguaia and Uberaba, between 500 and 1050 kg/ha. The yield gaps caused by crop management were mainly concentrated in South-central Brazil. In the soybean locations in the mid-west, north and north-east regions, the yield gap caused by crop management was <500 kg/ha. When evaluating the integrated effects of water deficit and crop management on soybean yield gaps, special attention should be given to Southern Brazil, which has total yield gaps >2000 kg/ha. For reducing the present soybean yield gaps observed in Brazil, several solutions should be adopted by growers, which can be summarized as irrigation, crop rotation and precision agriculture. Improved dissemination of agricultural knowledge and the use of crop simulation models as a tool for improving crop management could further contribute to reduce the Brazilian soybean yield gap.

The high environmental costs of raising livestock are now widely appreciated, yet consumption of animal-based food items continues and is expanding throughout the world. Consumers' ability to distinguish among, and rank, various interchangeable animal-based items is crucial to reducing environmental costs of diets. However, the individual environmental burdens exerted by the five dominant livestock categories – beef, dairy, poultry, pork and eggs – are not fully known. Quantifying those burdens requires splitting livestock‘s relatively well-known total environmental costs (e.g. land and fertilizer use for feed production) into partial categorical costs. Because such partitioning quantifies the relative environmental desirability of various animal-based food items, it is essential for environmental impact minimization efforts to be made. Yet to date, no such partitioning method exists. The present paper presents such a partitioning method for feed production-related environmental burdens. This approach treated each of the main feed classes individually – concentrates (grain, soy, by-products; supporting production of all livestock), processed roughage (mostly hay and silage) and pasture – which is key given these classes' widely disparate environmental costs. It was found that for the current US food system and national diet, concentrates are partitioned as follows: beef 0·21±0·112, poultry 0·27±0·046, dairy 0·24±0·041, pork 0·23±0·093 and eggs 0·04±0·018. Pasture and processed roughage, consumed only by cattle, are 0·92±0·034 and 0·87±0·031 due to beef, with the remainder due to dairy. In a follow-up paper, the devised methodology will be employed to partition total land, irrigated water, greenhouse gases and reactive nitrogen burdens incurred by feed production among the five edible livestock categories.

Assessing carbon footprint (CF) of crop production in a whole crop life-cycle could provide insights into the contribution of crop production to climate change and help to identify possible greenhouse gas (GHG) mitigation options. In the current study, data for the major crops of China were collected from the national statistical archive on cultivation area, yield, application rates of fertilizer, pesticide, diesel, plastic film, irrigated water, etc. The CF of direct and indirect carbon emissions associated with or caused by these agricultural inputs was quantified with published emission factors. In general, paddy rice, wheat, maize and soybean of China had mean CFs of 2472, 794, 781 and 222 kg carbon equivalent (CE)/ha, and 0·37, 0·14, 0·12 and 0·10 kg CE/kg product, respectively. For dry crops (i.e. those grown without flooding the fields: wheat, maize and soybean), 0·78 of the total CFs was contributed by nitrogen (N) fertilizer use, including both direct soil nitrous oxide (N2O) emission and indirect emissions from N fertilizer manufacture. Meanwhile, direct methane (CH4) emissions contributed 0·69 on average to the total CFs of flooded paddy rice. Moreover, the difference in N fertilizer application rates explained 0·86–0·93 of the provincial variations of dry crop CFs while that in CH4 emissions could explain 0·85 of the provincial variation of paddy rice CFs. When a 30% reduction in N fertilization was considered, a potential reduction in GHGs of 60 megatonne (Mt) carbon dioxide equivalent from production of these crops was projected. The current work highlights opportunities to gain GHG emission reduction in production of crops associated with good management practices in China.