Climate change has an impact on soybean production in the USA, necessitating thorough impact studies across broad geographic areas and extended periods to develop appropriate coping strategies. This study investigates the simulated response of soybean in ten major soybean-growing states of the USA under Climate Model Intercomparison Project Phase 5 based on multiple global climate models, two representative concentration pathways [RCP8.5 and RCP4.5] under rainfed and irrigated conditions for 2013–2039, 2043–2069, 2063–2099. The future climate series was developed using Agricultural Model Intercomparison and Improvement Project protocol by applying mean and variability, and CROPGRO-soybean model was explored for soybean simulation under 400 ppm CO2 level and a set of management. Under future climate, intense changes in temperature, precipitation amount and variability are anticipated under RCP8.5 than RCP4.5. As a result, a shorter life cycle, more evapotranspiration, lower grain production, higher water consumption and water productivity were expected under RCP8.5 than RCP4.5 scenarios. A higher reduction in grain yield and water productivity is expected under rainfed than irrigated conditions and intensity increases with advancement towards end of the century. Irrigation tends to decrease adverse climate change effects; however, the marginal economy for irrigation water must be assessed. Since the northern states under study are likely to experience increased grain yields or lower negative impacts, these areas could be the major production zones for soybean production in the future if only climate change is taken into account. Before reaching a convincing conclusion, different adaptation strategies must be thoroughly investigated.

The land cover of the Sanjiang Plain has changed dramatically since the 1950s. Although previous studies have analysed its spatiotemporal dynamics at long time intervals, a near real-time and accurate representation of the interannual evolution of cropping patterns in this region is of far-reaching importance for rationally allocating agricultural resources and ensuring food security. Based on the 30 m and 10 m land cover datasets in 2015 and 2017–2019, the current study used Landsat-8 satellite data in 2014, 2016 and 2020 to identify paddy rice and dryland crops using a decision tree classification approach and constructed the annual cropland datasets of the Sanjiang Plain from 2014 to 2020. The results show that the overall classification accuracies of crop datasets exceeded 95%, and the Kappa coefficients were higher than 0.92. The average annual accuracies of users and producers were 93% and 94% for rice fields and 97% and 95% for dryland crops, respectively. During the 7 years, the total area of paddy fields and dryland crops decreased by 5% and 8%. However, with minor positive and negative variation between years. 24.2% of paddy rice and 42% of dryland crops has been cultivated under 4 years. The centres of gravity for both crops mainly aggregated in the central counties with the migration direction and magnitude varying interannually. The current study emphasizes the importance of establishing annual high-resolution crop datasets to track the detailed spatio-temporal trajectories of cropping patterns that are essential to support sustainable cropland management and agricultural development.

Long-term intensive tea cultivation is suspected of deteriorating soil quality status and degrading land sustainability. This study aimed to determine the soil quality index of soils in a micro-catchment in Rize Province, Turkey, used for long-term intensive tea cultivation, by means of spatial multi-criteria analysis (SMCA) and standard scoring function (SSF) integrated with geographical information system (GIS) and geostatistics, considering bio-physical-chemical properties of a detailed soil dataset. Soil samples (102) were collected from the surface layer (0–20 cm). In the soil quality index for tea-cultivated soils (TSQI), soil indicators were weighted by an analytical hierarchy. Various indicator units were normalized with the SSF. The TSQI model was divided into five main criteria: (i) physical properties, (ii) chemical properties, (iii) fertility, (iv) biological indicators and (v) soil erosion susceptibility parameters. Principal components analysis (PCA) was applied and minimum dataset (MDS) created to determine the most effective indicators. The spatial distribution pattern of the tea total dataset soil quality index (TSQITDS) and tea minimum dataset soil quality index (TSQIMDS) values were statistically similar. TSQITDS low and very low-class areas accounted for 34.1% of the total area, while TSQIMDS low and very low-class areas constituted 33.6%. These areas, especially those with low soil quality properties, were in the northern and north-western parts of the micro-catchment. TSQITDS very high and high-class areas accounted for 56.2% of the total area, while TSQIMDS very high and high-class areas were found in 55.3% of the total area. These areas are located in the south of the micro-catchment.

Chemical defoliants are widely used in cotton (Gossypium L.) to accelerate leaf abscission and boll maturation, as well as, to facilitate mechanical harvesting. The current study was conducted to determine the interactive effect of cotton cultivars and spraying time of defoliant on defoliation, boll opening, fibre yield and quality. An experiment was performed with four cultivars and three defoliant spraying time during 2019 and 2020 in split plot design with three replications. At harvest, the defoliation and boll opening rate of all treatments after spraying defoliant was 94.6 and 85.4%, while the blank control (water) was 73.9 and 79.1%, respectively. After spraying defoliant, the effects of defoliation rate, boll opening rate, fibre yield and quality were different among cultivars, indicating that different cultivars had different responses to defoliant. Among them, L7619 was the most sensitive to defoliant, with the average defoliation rate of 95.6% and a seed cotton yield reduction of 882.9 kg/ha. Among the different time of applications, late spraying (17 September, B3) of defoliant recorded the highest defoliation rate (97.3%), boll opening rate (89.8%), seed cotton yield (3991 kg/ha) and steadily increased the fibre strength by 0.59 cN/tex compared with the control. Late spraying of defoliant had little or even no adverse effect on the remaining fibre quality traits (length, uniformity, micronaire and elongation). In general, these results suggested that the appropriate time for spraying defoliant can be determined based on the sensitivity of the cotton cultivar, the weather conditions at the field and the harvest time.

Understanding the photosynthetic behaviour, radiation use efficiency (RUE) and water use efficiency (WUE) of potatoes in response to diurnal and seasonal fluctuations in weather, is important for optimizing growth and production. The net ecosystem exchange (NEE) of carbon dioxide (CO2), is a parameter used to measure the balance between gross CO2 assimilation (also referred to as gross primary production (GPP)) and ecosystem respiration (Reco). The objectives of this study were (i) to quantify the NEE of potato grown in two contrasting agro-ecologies and partition NEE into Reco and GPP; (ii) to describe seasonal patterns in daily photosynthesis, RUE and WUE in potato; and (iii) to analyse the variability in photosynthesis rate, RUE and WUE as affected by weather and crop growth stage. We measured CO2 and water vapour fluxes using eddy covariance techniques in potato fields in two production regions of South Africa. The winter crop had higher mean RUE and WUE values, whereas the summer crop absorbed more CO2. RUE had a negative relationship with incident radiation. To optimize dry matter production, WUE and RUE, potato crops must be well established and develop a full canopy early during the season. Maintaining the crop for a longer period in the field has yield benefits, although they appear to decline over time due to reduced efficiencies as the crop matures. The RUE observed over the growing period in the present study was high, relative to findings by other field studies, despite relatively warm growing conditions that were likely sub-optimal for potato.

This study sought to compare the efficiency of different nitrogen (N) fertilizer forms applied to perennial ryegrass swards that were rotationally grazed by dairy cows or sheep under Irish conditions for two or three years. A 3 × 2 factorial random complete block design plot arrangement was used to compare calcium ammonium nitrate (CAN), urea and urea + N-(n-butyl) thiophosphoric triamide (urea + NBPT) at 150 and 250 kg N/ha per year. Zero nitrogen plots were also added to allow for N efficiency to be calculated. The study was conducted at four sites, giving three years of data collection at two sites and two years at the other two sites. All four sites observed similar responses to N fertilizer type and N fertilizer rate. Significant differences were observed between the 150 kg N/ha and 250 kg N/ha treatments for pre-grazing herbage yield (1346 and 1588 kg DM/ha, respectively; P < 0.001) and total herbage production (12 290 and 14 448 kg DM/ha, respectively; P < 0.001). There was no difference but a tendency for pre-grazing herbage yield to be higher for CAN and urea + NBPT than urea (1485, 1480 and 1436 kg DM/ha, for CAN, urea + NBPT and urea, respectively; P = 0.091). Total herbage production was significantly higher for CAN and urea + NBPT than urea (13 478, 13 542 and 13 087 kg DM/ha, respectively; P = 0.004). In conclusion, there was an overall benefit detected over the 10 site-years from using urea protected with NBPT v. using urea.

Understanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

This study addresses the assessment of crop water stress index (CWSI) of bell pepper (Capsicum annuum L.) and net income generated under regulated deficit irrigation (RDI), conventional deficit irrigation and partial root-zone drying (PRD) and full irrigation (I100) using surface- and subsurface-drip systems (DI and SDI) during 2016 and 2017 in the Mediterranean region. The experimental design was split-plots with four replications. RDI was supplied with 50% of I100 during vegetative stage until flowering, then received 100% of crop water requirement. PRD50 received 50% of I100, but from alternative laterals each watering. The results revealed that CWSI was correlated significantly (P < 0.01) and negatively with yield, yield per plant, total soluble solid, ETa, fruit weight and plant height indicating that yield of bell pepper declined with increasing CWSI values (P < 0.01). Bell pepper should be irrigated at mean CWSI value of 0.20 without any yield reduction. CWSI in the RDI and I75 treatments were slightly greater than 0.20. Irrigation treatments had significant effect on yield and quality traits. The highest total soluble solutes were found in PRD50 and I50. The DI I100 treatment generated the highest net income followed by the SDI I100 and RDI. In conclusion, RDI and I75 appear to be good alternatives to I100 for sustainable bell pepper production in the Mediterranean region.

Crop rotation in agriculture can lead to increased crop productivity in the rotation and improved soil fertility as a result of residue incorporation. Unfortunately, residue incorporation is not a common practice in crop production systems under the Mediterranean regime, largely due to the lack of information on the effect of the follow-up crops. Therefore, this study was conducted with six biannual rotations (bread wheat–canola, bread wheat–bean, durum wheat–canola, durum wheat–bean, corn–canola and corn–bean) using four residue incorporation levels (0, 50, 100 and 200%) in an Andisol in south central Chile. Grain yield and residue production were evaluated in the canola and bean crops 5 years after initiating crop rotation. The previous crop affected canola grain yield and residue production, which were both higher after corn. Meanwhile, the residue incorporation level had a slight effect on residue production after bread wheat. Only bean grain yield was affected by the previous crop, which was higher after the durum wheat and corn crops. The residue incorporation has marginally affected residue production after bread wheat. Finally, residue incorporation at different levels under the conditions of the present experiment had a minimal effect on bean and canola residue production following the bread wheat crop. The best crop rotation for canola grain yield was corn–canola, which produced 0.54 Mg/ha (18.3%) higher yield than the average of the other two rotations. The best crop rotation for achieving higher bean grain yield was corn–bean, which yielded 0.52 Mg/ha (12.3%) higher than the average of the other two rotations.

Soybean production contributes to ca. 60% of global plant-based protein used for food and feed. Brazil is the largest soybean producer and exporter, with 60% from tropical and 40% from subtropical environments. Nitrogen (N) can play an essential role in the storage of proteins in seeds; thus, it could be a key factor in increasing the quantity and quality of seeds in high-yielding soybean crops. Unlike in temperate environments, there is a gap of knowledge on whether soybean grown under tropical and subtropical climates are limited by N-fertilization to sustain the seed yield increase without detriments in seed protein concentration. This study aimed to evaluate the effect of N-fertilization on soybean seed yield, protein and oil concentrations in tropical and subtropical environments in Brazil, thus contributing to agricultural intensification procedures and food security studies. Two levels of N-fertilization (0 and 1000 ka/ha) were tested across 11 tropical or subtropical environments. The range of latitudes explored here was from 12°S to 29°S, representing the major soybean-producing regions in Brazil either under rainfed or irrigated conditions. We found that seed yield responses to N-fertilization were significant (in some environments under rainfed with an average increase of 7%) or not significant (in irrigated). Seed protein increases due to improved N-fertilization (on average 4% for irrigated and 12% for rainfed conditions) were much higher than previous reports from temperate environments. Regardless of N supply and water deficit, there was a trend of seed protein and oil concentration increasing toward lower latitudes.

The milk production is strongly influenced by the dairy cow welfare related to a good nutrition and the analysis of the digestibility of feeds allows us to evaluate the health status of the animals. Through faeces’ visual examination it is possible to estimate the quality of diet fed in terms of lacking in fibre or too high in non-structural carbohydrates. The study was carried out in 2021, on four dairy farms in central Italy. The purpose of this work is the classification and evaluation of dairy cow faeces using RGB image analysis through an artificial intelligence (AI) (convolutional neural network (CNN)) algorithm. The main features to analyse are pH, colour and consistency. For the latter two RGB imaging was combined with deep learning and AI to reach objectivity in samples’ evaluation. The images have been captured with several smartphones and cameras, under various light conditions, collecting a data set of 441 images. Images acquired by RGB cameras are then analysed through CNN technology that extracts features and data previously standardized by a faecal score index assigned after a visual analysis and based on five classes. The results achieved with different training strategies show a training accuracy of 90% and a validation accuracy of 78% of the model which allow us to identify problems in bovine digestion and to intervene promptly in feed variation. The method used in this study eliminates subjectivity in field analysis and allows future improvement of increasing the data set to strengthen the model.

The current study investigated the effects of pre-grazing herbage mass (PGHM, 1500 or 2500 kg dry matter (DM)/ha) and post-grazing sward height (PGSH, 4 or 6 cm) on herbage production and its nutritive value and DM intake, grazing behaviour and growth of Charolais steers (n = 96; 12 months of age; 396 ± 19.0 kg) during a 222-day grazing season, and the subsequent effect of an indoor finishing diet (grass silage alone or supplemented with concentrates) for 146 days, on performance and carcass traits. Steers were assigned to one of 12 grazing groups and group was assigned to a 2 (PGHM) × 2 (PGSH) factorial arrangement of treatments. At the end of the grazing season, live-weight was 16 kg heavier for PGHM-1500 than PGHM-2500 and 34 kg heavier for PGSH-6 than PGSH-4. After indoor finishing, there was no difference in carcass weight between PGHM treatments, but PGSH-6 had a 19 kg heavier carcass than PGSH-4. Herbage production was 881 and 517 kg DM/ha greater for PGHM-2500 than PGHM-1500 and for PGSH-4 than PGSH-6, respectively. Grazing stocking rate did not differ between PGHM treatments but PGSH-4 carried 1.35 more steers/ha than PGSH-6. Supplementing concentrates during the indoor period increased carcass weight (42 kg) and fat score (2.10 units). In conclusion, grazing to 6 rather than 4 cm, increased individual carcass weight but not carcass weight gain/ha. Compared to PGHM-2500, grazing PGHM-1500 increased steer live-weight gain at pasture, but did not affect carcass weight following indoor finishing.