Plant nitrogen (N) links with many physiological progresses of crop growth and yield formation. Accurate simulation is key to predict crop growth and yield correctly. The aim of the current study was to improve the estimation of N uptake and translocation processes in the whole rice plant as well as within plant organs in the RiceGrow model by using plant and organ maximum, critical and minimum N dilution curves. The maximum and critical N (Nc) demand (obtained from the maximum and critical curves) of shoot and root and Nc demand of organs (leaf, stem and panicle) are calculated by N concentration and biomass. Nitrogen distribution among organs is computed differently pre- and post-anthesis. Pre-anthesis distribution is determined by maximum N demand with no priority among organs. In post-anthesis distribution, panicle demands are met first and then the remaining N is allocated to other organs without priority. The amount of plant N uptake depends on plant N demand and N supplied by the soil. Calibration and validation of the established model were performed on field experiments conducted in China and the Philippines with varied N rates and N split applications; results showed that this improved model can simulate the processes of N uptake and translocation well.

Agricultural water use accounts for more than 95% of the total water consumption in the extreme arid region of the Tarim River Basin. Understanding the variation of agricultural water demand (AWD) and its attributions is therefore vital for irrigation management and water resource allocation affecting the economy and natural ecosystems in this high water-deficit region. Here spatial–temporal variations of AWD based on weighted crop water requirement (ETc) were estimated using the Penman–Monteith equation and the crop coefficient approach. Then the contributions of meteorological factors and planting structure (i.e. proportions of crop acreages) to AWD variations were quantified based on traditional methods and numerical experiment (i.e. a series calculation of AWD based on different input data). Results indicated that AWD decreased during 1960–1988 at a rate of 2.76 mm/year and then started to increase at a high rate of 9.47 mm/year during 1989–2015. For the first period (1960–1988), wind speed (uz), maximum humidity (RHmax) and sunshine duration (n) were the most important factors leading to decreased AWD, while for the second period the evolution of planting structure was the most significant factor resulting in the rapid increase of AWD, followed by the minimum temperature (Tmin), uz and RHmax. The evolution of planting structure alone would lead to an increase rate for AWD of 7.1 mm/year while the climatic factor would result in an increase rate of 1.9 mm/year during 1989–2015.

The response of soil microbial communities to soil quality changes is a sensitive indicator of soil ecosystem health. The current work investigated soil microbial communities under different fertilization treatments in a 31-year experiment using the phospholipid fatty acid (PLFA) profile method. The experiment consisted of five fertilization treatments: without fertilizer input (CK), chemical fertilizer alone (MF), rice (Oryza sativa L.) straw residue and chemical fertilizer (RF), low manure rate and chemical fertilizer (LOM), and high manure rate and chemical fertilizer (HOM). Soil samples were collected from the plough layer and results indicated that the content of PLFAs were increased in all fertilization treatments compared with the control. The iC15:0 fatty acids increased significantly in MF treatment but decreased in RF, LOM and HOM, while aC15:0 fatty acids increased in these three treatments. Principal component (PC) analysis was conducted to determine factors defining soil microbial community structure using the 21 PLFAs detected in all treatments: the first and second PCs explained 89.8% of the total variance. All unsaturated and cyclopropyl PLFAs except C12:0 and C15:0 were highly weighted on the first PC. The first and second PC also explained 87.1% of the total variance among all fertilization treatments. There was no difference in the first and second PC between RF and HOM treatments. The results indicated that long-term combined application of straw residue or organic manure with chemical fertilizer practices improved soil microbial community structure more than the mineral fertilizer treatment in double-cropped paddy fields in Southern China.

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.

The current study evaluated the uncertainty of beef cattle supply for slaughter due to the variable climate of Rio Grande do Sul, Brazil. The data included the numbers of cattle slaughtered, local live cattle prices, price and amount of exported beef, and the prices of the Brazilian beef futures market. Data were collected on the beef supply from January 1997 to March 2014. Climate data included El Niño (EN), La Niña (LN), South Atlantic Sub-tropical Dipole (SASD), Negative and Positive Atlantic Dipole (−AD and +AD), Tropical South and North Atlantic indices. Statistical analysis was performed by a multivariate regression of time series. It was observed that EN and SASD climatic variables increased the numbers of beef cattle slaughtered, with a 1 and 4-month lag, respectively. On the other hand, LN and -AD decreased the number of animals slaughtered, with 4 and 0 months’ lag, respectively, meaning that there was an immediate response to −AD, while there was a 4-month delay for LN. The amount of exported beef and live beef cattle prices were explained by the number of animals slaughtered in the state. Data suggested that the beef cattle market in RS was more strongly influenced by the occurrence of climate phenomena with LN and −AD than by economic variables such as the price paid to the producer for beef and the amount exported. The climate changes evaluated in the current study affect the livestock production system and consequently the beef market industry in Southern Brazil.

Though the number of climate-change-related agro-phenological investigations are growing rapidly, the attention paid to spring crops has been much less than to winter ones. The objective of the current study was to investigate long-term temporal and spatial trends of spring barley phenology and to project changes in the timing and duration of different phenological phases during the current century. Higher temperatures significantly affected the potential scheduling of agricultural practices, accelerating the occurrence of sowing and emergence dates. Historical trends in harvest dates of spring barley showed a slight delay. These changes resulted in the extension of the total vegetative period of spring barley by >12 days over the period investigated (1961–2015). Since Lithuania is situated on the Baltic Sea, an increase in temperature along with an increase in distance from the sea was characteristic over the last 55 years. Projected changes in the occurrence of phenological phases of spring barley differ significantly from analysed historical changes and advancement of all phenological phases have been projected according to both Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 climate change scenarios. Shortening of the total vegetative period by 5 days is foreseen for the far (2071–2100) future according to the pessimistic (RCP 8.5) climate change scenario.

Grain production potential is mainly influenced by agroclimate and land use. In the present study, substantial regional differences associated with the impact of climate change were found (i.e. the degree of climate-related impacts varied among regions). Currently, there is an urgent need for effective responses and adaptations to different agricultural districts and agricultural production modes. Therefore, the aim was to examine ecotones and explore trends and influential factors associated with grain production potential change. Using the Global Agro-ecological Zone model, the grain production potential of West Jilin, China under different conditions during various years were estimated, considering meteorological, soil, topographic, land use and other data. The results showed that total grain production potential (TGrPP) of West Jilin increased continuously from 1976 to 2013. The average annual increase in TGrPP was higher in period 1 (1976–2000) than period 2 (2000–2013). In period 1, grain production potential was influenced mainly by irrigation percentage changes, followed by land use change. The conversion of grassland to farmland was the most important land use change factor that was associated with increased grain production potential. Climate change affected grain production potential in period 1 negatively. In period 2, climate change had the largest impact and land use imparted the smallest effect on grain production potential. Finally, the decrease in irrigation percentage resulted in reduced grain production potential.

Phenological models for predicting the grapevine flowering were tested using phenological data of 15 grape varieties collected between 1990 and 2014 in Vinhos Verdes and Lisbon Portuguese wine regions. Three models were tested: Spring Warming (Growing Degree Days – GDD model), Spring Warming modified using a triangular function – GDD triangular and UniFORC model, which considers an exponential response curve to temperature. Model estimation was performed using data on two grape varieties (Loureiro and Fernão Pires), present in both regions. Three dates were tested for the beginning of heat unit accumulation (t0 date): budburst, 1 January and 1 September. The best overall date was budburst. Furthermore, for each model parameter, an intermediate range of values common for the studied regions was estimated and further optimized to obtain one model that could be used for a diverse range of grape varieties in both wine regions. External validation was performed using an independent data set from 13 grape varieties (seven red and six white), different from the two used in the estimation step. The results showed a high coefficient of determination (R2: 0.59–0.89), low Root Mean Square Error (RMSE: 3–7 days) and Mean Absolute Deviation (MAD: 2–6 days) between predicted and observed values. The UniFORC model overall performed slightly better than the two GDD models, presenting higher R2 (0.75) and lower RMSE (4.55) and MAD (3.60). The developed phenological models presented good accuracy when applied to several varieties in different regions and can be used as a predictor tool of flowering date in Portugal.

Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

Pig manure management systems in Taiwan differ from the model representing the Asian region developed by the Intergovernmental Panel on Climate Change (IPCC). The current study was undertaken to update greenhouse gas (GHG) emission factors of anaerobically treated piggery waste water by operating the conventional three-step piggery waste-water treatment system from selected pig farms located in northern, central and southern Taiwan. Biogas mass flow meters were installed to the outlet of anaerobic basins prior to the biogas pressure stabilizers for direct and reliable biogas measurement. The analytic results showed that average GHG emissions were 0.088, 0.128 and 0.066 m3/head/day in the northern, central and southern pig farms, respectively. Thus, the average emission levels of methane and nitrous oxide were 14.38 and 0.055 kg/head/year, respectively, from anaerobic digestion of piggery waste water for the three pig farms. The average removal efficiency of chemical oxygen demand, biochemical oxygen demand and suspended solids by anaerobic digestion process from the three pig farms was about 77, 93 and 70%, respectively.

Analysis of the potato yield gap and the corresponding nutrient gap can help in devising strategies and measurements to increase productivity for closing the gaps through improved practices. On-farm experiments conducted in the main potato production areas of northwest China were used to determine attainable yield. Official statistical data were used to determine the actual on-farm yield. Yield gap was the difference between attainable yield and actual on-farm yield. Nutrient gap was calculated by dividing the size of yield gap by partial factor productivity. Results indicated that nitrogen (N), phosphorus (P) and potassium (K) fertilization increased potato yield by an average of 1169–7625, 2937–5336 and 2331–7338 kg/ha, respectively. The maximum attainable yields (the 90th percentile yields) were 50 145, 37 855, 30 261 and 56 616 kg/ha and the average actual on-farm yield were 14 179, 16 732, 10 271 and 19 990 kg/ha in the Inner Mongolia Autonomous Region (IMAR), Gansu, Ningxia and Qinghai provinces, respectively. In the above four regions, yield would need to increase by 165, 70, 112 and 121% from actual yield to reach 75% of attainable yield. Compared with recent 3-year average NPK rates by farmers, the total NPK rates need to increase by 90.1–134.3% for IMAR, 42.9–69.2% for Gansu, 68.1–111.2% for Ningxia and 48.1–83.8% for Qinghai to improve productivity to near the 75% attainable yield. In conclusion, the high yield responses to fertilizer application provide opportunities to close the large yield gaps through balanced nutrition.

Some biological and chemical properties of a Gleysol were examined after 24 years of soil tillage (chisel plough – CP, mouldboard plough – MP, no-till – NT) and that of ploughing the 24-yr NT (P-NT) once, in two cropping systems (conventional – CONV, organic – ORG) applied over 4 years (2007–2010) of a long-term experiment (autumn 1987–autumn 2011) at La Pocatière, Québec, Canada. The 0–10, 10–20 and 20–30 cm soil depths were sampled in autumn 2011 after a maize trial. Tillage affected light fraction organic carbon (LFOC), light fraction organic nitrogen (LFON) and mineralizable N (Nmin) in soil, with the lowest LFOC, LFON and Nmin values in the MP treatment. No-till had lower soil pH than the other tillage systems in the 10–20 and 20–30 cm soil depths. Tillage affected the amounts of nitrate-N in 0–10 and 10–20 cm soil depths, with the lowest amounts for MP (4.3 kg nitrate-N/ha) compared with NT (7.2 or 8.5 kg nitrate-N/ha) or CP (7.7 kg nitrate-N/ha). The P-NT had no negative impact on organic C and N, or available nutrients in soil. Cropping system had no effect on soil organic C and N, available nutrients or pH. Findings suggest that long-term NT or CP may result in greater storage of organic C and N in soil and improve available nutrients compared with MP. Ploughing 25-year-old NT plots redistributed available nutrients in the profile but had no negative effect on soil organic C or N.

A field experiment with the 35–1 fractional factorial design and five factors (k = 5) at three levels (s = 3) was performed in 2007–2010 at the Agricultural Experiment Station in Bałcyny, north-eastern (NE) Poland. The results of the experiment carried out under the agro-ecological conditions of NE Poland confirmed the high yield potential of common wheat and satisfactory yield potential of spelt and durum wheat. On average, durum wheat and spelt yields were 2.14 and 2.55 t/ha lower, respectively, than common wheat yields. Sowing date was not correlated with the yields of analysed Triticum species. Seed rate (350, 450 and 550 seeds/m2) had no significant influence on the grain yield of winter cultivars of common wheat, durum wheat and spelt. Common wheat cv. Oliwin and durum wheat cv. Komnata were characterized by the highest yields in response to nitrogen (N) fertilizer rates calculated based on the Nmin content of soil. An increase in the spring fertilizer rate by 40 kg N/ha in excess of the balanced N rate was not justified because it did not induce a further increase in the grain yield of common wheat and durum wheat. The grain yield of spelt cv. Schwabenkorn continued to increase in response to the highest rate of N fertilizer in spring (40 kg N/ha higher than the optimal rate). Intensified fungicide treatments improved grain yield in all Triticum species.

Atmospheric emissions of nitrogen (N) from New Zealand dairy farms are significant but have the potential to be affected by manure management prior to land application. The current work examined whether reducing cattle manure dry matter (DM) from 0.16 high DM (HDM) to 0.06 low DM (LDM), to enhance infiltration and reduce ammonia (NH3) emissions when applied to grassland, would affect nitrous oxide (N2O) emissions. Pasture was cut, simulating grazing, and either amended with HDM (173 kg N/ha) or LDM manure (48 kg N/ha) or left unamended. Ammonia emissions from HDM manure were higher than from LDM manure, as a flux or as a percentage of total ammoniacal nitrogen (TAN, i.e. NH3 + NH4+) applied, due to more TAN being retained near the soil surface and the higher soil surface pH under HDM manure treatment. Cumulative N2O emissions over 37 days from HDM plots were higher than from the control but not from the LDM plots. After 5 days, the daily N2O emission rate was larger from HDM plots than from LDM and control plots. The N2O fluxes from LDM and HDM treatments did not differ, either as a proportion of TAN applied or as a proportion of total-N applied. Increasing DM contributed to reductions in both oxygen (O2) availability and relative gas diffusivity, and thus potentially N2O production. Under the conditions of the current study, lower manure DM content reduced NH3 emissions but did not increase cumulative losses of N2O.

Simulation of the water balance in cropping systems is an essential tool, not only to monitor water status and determine drought but also to find ways in which soil water and irrigation water can be used more efficiently. However, besides the requirement that models are physically correct, the spatial representativeness of input data and, in particular, accurate precipitation data remain a challenge. In recent years, satellite-based soil moisture products have become an important data source for soil wetness information at various spatial-temporal scales. Four different study areas in the Czech Republic and Austria were selected representing Central European soil and climatic conditions. The performance of soil water content outputs from two different crop-water balance models and the Metop Advanced SCATterometer (ASCAT) soil moisture product was tested with field measurements from 2007 to 2011. The model output for soil water content shows that the crop model Decision Support System for Agrotechnology Transfer performs well during dry periods (<30% plant available soil moisture (ASM), whereas the soil water-balance model SoilClim presents the best results in humid months (>60% ASM). Moreover, the model performance is best in the early growing season and decreases later in the season due to biases in simulated crop-related above-ground biomass compared with the relatively stable grass canopy of the measurement sites. The Metop ASCAT soil moisture product, which presents a spatial average of soil surface moisture, shows the best performance under medium soil wetness conditions (30–50% ASM), which is related to low variation in precipitation frequency and under conditions of low-surface biomass (early vegetation season).