In the Mediterranea area, major effects of climate change are a modification in rainfall patterns, an increase in temperature with an intensify in tropical nights, and an increase in incoming radiations, especially UV-Bs. Despite the various adaptation strategies, grapevines are sensitive to altered climatic conditions. This paper aims to assess the benefits of applying a new sustainable product to the soil that can implement farmers’ resources to adapt to this changing situation. Zeowine was realized by combining the properties of zeolite, which has excellent potential in many sectors such as in agriculture, with the organic substance of a compost obtained on a company scale from the reuse of waste processing grapes, pomace and stalks. The effects of two different soil management (Z – Zeowine, 30 t/ha dose and C – Compost, 20 t/ha dose) on vine physiology and berry compositions in Sanforte grapevines (new plantation) were studied during the 2019–2020–2021 growing seasons in the San Miniato area, Italy. The following physiological parameters of grapevines were measured: leaf gas exchange, leaf temperature, stem water potential and chlorophyll fluorescence. The results showed that Z increased single leaf photosynthesis, reduced leaf temperature and water stress. In addition, phenolic and technological parameters were studied. The Z-treated vines had higher sugar content and total and extractable anthocyanin content as well as berry weight. These results suggested that the application of zeolites added to compost in the vineyard to the soil can be a valid tool to mitigate the effects of climate change.

Tossa (Corchorus olitorius L.) is a significant cash crop, cultivated commercially in the lower flood plain of Bangladesh. The climatic regimes in Bangladesh are changing as well as the world does. However, this species is threatened by climate change. Occurrences of data on threatened and endangered species are frequently sparse which makes it difficult to analyse the species suitable habitat distribution using various modelling approaches. The current paper used maximum entropy (Maxent) and educational global climate model (EdGCM) modelling to predict and conserve the suitable habitat distributions for Tossa species in Bangladesh to the year 2100. Nine environmental variables, 239 occurrence data and two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were used for the Maxent modelling to project the impact of climate change on the Tossa distributions. Furthermore, the EdGCM was used to study the climatic space suitability for the Tossa species in the context of Bangladesh. Both of the climatic scenarios were used for the prediction to the year 2100. The Maxent model performed better than random for the Tossa species with a high AUC value of 0.86. Under the RCP scenarios, the Maxent model predicted habitat reduction for RCP4.5 is 2%, RCP8.5 is 9% and EdGCM is 10.2% from the current localities. The predictive modelling approach presented here is promising and can be applied to other important species for conservation planning, monitoring and management, especially those under the threat of extinction due to climate change.

Accurate estimation of tuber size distribution (TSD) parameters in discretely categorized potato (Solanum tuberosum L) yield samples is desirable for estimating modal tuber sizes, which is fundamental to yield prediction. In the current work, systematic yield digs were conducted on five commercial fields (N = 119) to compare the Weibull, Gamma and Gaussian distribution functions for relative-likelihood-based goodness-of-fit to the observed discrete distributions. Parameters were estimated using maximum likelihood estimation (MLE) for the three distributions but were also derived using the percentiles approach for the Weibull distribution to compare accuracy against the MLE approaches. The relationship between TSD and soil nutrient variability was examined using the best-fitting model's parameters. The percentiles approach had lower overall relative likelihood than the MLE approaches across five locations, but had consistently lower Root Mean Square Error in the marketable tuber size range. Negative relationships were observed between the percentile-based shape parameter and the concentrations of Phosphorus and Nitrogen, with significant (non-zero-overlapping 95% confidence interval) regression coefficients for P (−0.74 ± 0.33 for distribution of proportional tuber numbers and −1.3 ± 0.62 for tuber weights). Stem density was negatively associated with the scale and mode of tuber number (regression coefficients −0.98 ± 0.63 and −1.08 ± 0.78 respectively) and tuber weight (regression coefficients −0.99 ± 0.78 and −1.04 ± 0.69 respectively) distributions. Phosphorus is negatively related to the scale of the tuber-number-based distribution while positively associating with the tuber weight distribution. The results suggest that excess P application was associated with the increase in small tubers that did not contribute significant weight to the final yield.

Little information is available on the phenotypic performance of perennial ryegrass varieties when exposed to grazing conditions on commercial grassland farms. Grass varieties are classically evaluated in mechanically defoliated plot systems which, although designed to mimic grazing conditions, do not fully capture the range of stresses or interactions that a sward is subjected to under commercial settings or over any period longer than 4 years. The evolution of technology in the form of PastureBase Ireland has led to agronomic data of individual paddocks being made available for analysis over multiple years. Data used in the current study consisted of dry matter (DM) production and ground score data across a 7-year period from ten perennial ryegrass varieties grown as monocultures in 559 paddocks on 98 commercial farms. The results demonstrated how perennial ryegrass variety is associated with a range of agronomic performance traits on commercial farms; including total and seasonal DM production, grazing DM production and number of grazing events. Varieties with the highest total DM production also had the highest spring and mid-season DM production; autumn DM production was associated with the interaction between variety and year. The highest producing variety in the study, AberGain, produced 1342 kg DM/ha/year more than the mean of all other varieties. Variety differences manifested themselves as swards aged, with some varieties increasing in total DM production while others reduced in total DM production. The current work provides a basis for the consideration of on-farm variety assessment in the composition of future variety evaluation protocols.

The effects of three seeding rates (250, 350 and 450 seeds/m2), six applied N rates (40, 70, 100, 130, 160 and 190 kg N/ha) and the application of plant growth regulator on the grain quality of two spring oat cultivars (Husky and Keely) were evaluated at four spring-sown sites over a 3-year period (2016–2018). The same cultivars, seeding rates and five applied N rates (80, 110, 140, 170 and 200 kg N/ha) were evaluated at three winter-sown sites, in 2017–2019. Keely had a significantly higher kernel content (KC) in spring (0.7%) and winter-sown (0.6%) oats. Specific weight (SW) decreased as N rate increased in spring-sown crops (1 kg/hl) with no effect observed in winter-sown oats. Hullability (HB) increased (0.92%) in spring-sown crops as N rate increased with a reduction (0.9%) observed when winter-sown. Increases in grain protein percentage (GPP) were observed in spring (1.8%) and winter (1.2%) sowings in response to N rate. The application of plant growth regulator reduced SW (0.4 kg/hl), GPP (0.3%), KC (0.6%) with no effect on HB. Kernel size was highly responsive too applied N rate, with reductions in grains >2.5 mm and increases in grains of smaller size observed as N rate increased. HB had a marked relationship to kernel size with GP inconsistently linked to SW. The results of this study suggest that cultivar, N rate and PGR application are of importance to milling quality and that changes in grain size in response to agronomic practices contribute to changes in specific weight and hullability.

The combination of advances in knowledge, technology, changes in consumer preference and low cost of manufacturing is accelerating the next technology revolution in crop, livestock and fish production systems. This will have major implications for how, where and by whom food will be produced in the future. This next technology revolution could benefit the producer through substantial improvements in resource use and profitability, but also the environment through reduced externalities. The consumer will ultimately benefit through more nutritious, safe and affordable food diversity, which in turn will also contribute to the acceleration of the next technology. It will create new opportunities in achieving progress towards many of the Sustainable Development Goals, but it will require early recognition of trends and impact, public research and policy guidance to avoid negative trade-offs. Unfortunately, the quantitative predictability of future impacts will remain low and uncertain, while new chocks with unexpected consequences will continue to interrupt current and future outcomes. However, there is a continuing need for improving the predictability of shocks to future food systems especially for ex-ante assessment for policy and planning.

Participatory plant breeding (PPB), commonly applied in the Global South to address the needs of underserved farmers, refers to the active collaboration between researchers, farmers and other actors throughout the breeding process. In spite of significant public and private investments in crop variety improvement in the Global North, PPB is increasingly utilized as an approach to address cropping system needs. The current study conducted a state-of-the-art review, including a comprehensive inventory of projects and five case studies, to explore the emergence of PPB in the Global North and inform future PPB efforts. Case studies included maize (Zea mays), tomato (Solanum lycopersicum), Brassica crops (Brassica oleracea), wheat (Triticum aestivum) and potato (Solanum tuberosum). The review identified 47 projects across the United States, Canada and Europe including 22 crop species representing diverse crop biology. Improved adaptation to organic farming systems and addressing principles and values of organic agriculture emerged as consistent themes. While projects presented evidence that PPB has expanded crop diversity and farmer's access to improved varieties, obstacles to PPB also emerged including challenges in sustained funding as well as addressing regulatory barriers to the commercial distribution of PPB varieties. Agronomic improvements were only one lens motivating PPB, with many projects identifying goals of conservation of crop genetic diversity, farmers' seed sovereignty and avoidance of certain breeding techniques. The authors conclude that a multidisciplinary approach is needed to fully understand the social, political and agroecological influences driving the emergence of projects in the Global North and factors impacting success.

If current food consumption patterns continue, the agriculture sector must provide significantly more food in the coming years from the available land area. Some livestock systems engage in feed–food competition as arable land is used for livestock feed rather than as crops for food; reducing the global supply of food. There is a growing argument that to meet future-food demands sustainably, feed–food competition must be minimized. To this end, we evaluated the effectiveness of two refined metrics to quantify feed–food competition in three livestock systems; dairy and its beef, suckler beef and pig production in Ireland. The metrics are edible protein conversion ratio (EPCR) and the land-use ratio (LUR). The EPCR compares the amount of human digestible protein (HDP) in livestock feed against the amount of HDP the livestock produced, calculating how efficiently it produces HDP. However, the LUR compares the potential HDP from a crop system on the land used to produce the livestock's feed against the HDP the livestock system produced. In both metrics, a value <1 demonstrates an efficient system. The EPCR values for dairy beef (0.22) and suckler beef (0.29) systems consider them efficient producers, whereas pig production (1.51) is inefficient. The LUR values designate that only the dairy beef (0.58) is a net positive producer of HDP from the land used for its feed, with crop production producing more HDP than suckler beef (1.34) and pig production (1.73). Consequently, the LUR can be deemed to be more suitable to represent feed–food competition in livestock production.

Soybean yield within the Southern Africa falls below its potential despite similar climatic conditions across some agroecologies, replicable agronomic management practices and introduced improved varieties. Understanding physiological processes and water-use efficiency (WUE) of soybean offer information on bridging this yield gap. A field study was conducted in 2017 and 2018 seasons in two agroecologies (Angonia and Ruace) in Mozambique to evaluate the effects of Bradyrhizobium diazoefficiens strain USDA110 formerly known as Bradyrhizobium japonicum inoculant, nitrogen and phosphorus on nodulation, physiology and yield of non-promiscuous (Safari) and promiscuous (TGx 1740-2F) soybean varieties. Data on transpiration, photosynthesis, leaf area index, radiation interception and WUE from the beginning of flowering to maturity were collected. Transpiration rate varied considerably with interaction between locations, growth stages, varieties and treatments. At podding, phosphorus-treated soybean at Angonia transpired less (6.3 mmol/m2/s) than check plants (6.6 mmol/m2/s). Photosynthesis rate and WUE were distinct with variety, growth stages and inputs within agroecologies. For instance, in Angonia 2018 season, phosphorus fertilized TGx 1740-2F photosynthesized more at flowering (25.3 μmol/m2/s) while the lowest was phosphorus-treated Safari at podding with 17.2 μmol/m2/s. At the same site in 2017, inoculated soybean photosynthesized more at 22.8 μmol/m2/s leading to better WUE of 3.6 that corresponded to 2894 kg/ha yield. Overall, soybean WUE was higher when inoculated than N-treated, while P application yielded better. Results from this study will complement breeders’ effort in developing phosphorus efficient varieties suited for a wide range of changing climatical conditions.

During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders.

To exploit the potential of ecological intensification during sunflower cropping, it is crucial to understand the potential synergies between crop management and ecosystem services. We therefore examined the effect of pollination intensification on sunflower yield and productivity under various levels of soil fertilization over two seasons in the eastern Free State, South Africa. We manipulated soil fertility with fertilizer applications and pollination with exclusion bags. We found a synergetic effect between pollination and soil fertilization whereby increasing pollination intensity led to a far higher impact on sunflower yield when the soil had been fertilized. Specifically, the intensification of insect pollination increased seed yield by approximately 0.4 ton/ha on nutrient poor soil and by approximately 1.7 ton/ha on moderately fertilized soil. Our findings suggest that sunflower crops on adequate balanced soil fertility will receive abundant insect pollination and may gain more from both synergies than crops grown in areas with degraded soil fertility.

For ensuring food demand of the fast growing population in developing countries, quantification of crop yield gaps and exploring production constraints are very crucial. Sorghum is one of the most important climate change resilient crops in the rainfed farming systems of semi-arid tropics. However, there is little information about yield gaps and production constraints. This study aimed at analysing existing yield gaps and exploring major constraints of sorghum production in Southwest Ethiopia. A crop simulation model approach using AquaCrop and DSSAT was used to estimate potential yield and analyse the yield gaps. Model calibration and evaluation was performed using data from field experiments conducted in 2018 and 2019. Sorghum production constraints were assessed using a survey. The actual and water-limited yield of sorghum ranged from 0.58 to 2.51 and 3.6 to 6.47 t/ha, respectively for the period 2003–17. The regional yield gaps of sorghum for the targeted period were 3.02–3.95 t/ha with a mean value of 3.51 t/ha. Majority of respondent farmers considered seasonal rainfall risk (98%), poor soil fertility (86%), lack of improved varieties (78%) and inadequate weed management (56%) as major factors responsible for the existing yield gaps. The mean exploitable yield gap (2.5 t/ha) between water-limited and actual yield showed the level of existing opportunity for improvement in the actual productivity of sorghum. The gaps also call for introduction of proper interventions such as adoption of improved varieties, planting date adjustment, conservation tillage, fertilizer application and on time weed management.

The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.

Soil acidity and poor nutrient use efficiency are major limiting factors as regards output potential on heavy soils, soils which are dominated by high proportions of clay and organic matter, with impeded drainage, high buffering capacity and located in high rainfall areas. Lime is applied in order to counteract these limiting factors and in turn improve agricultural output and productivity. The current study investigates the effects of two commonly used lime products at three comparable treatment rates, ground lime (7.5, 5 and 2.5 tonne/ha) and granulated lime (7.5, 2.5 and 1.5 tonne/ha), applied across three distinct sites. The ability of each lime product and treatment rate to counteract soil acidity, increase nutrient availability and influence soil physical structure was assessed over time. On average across sites, 1 tonne/ha of each lime product increased soil pH by 0.15 and 0.21 pH units between ground and granulated lime, respectively. Site 3 experienced the greatest increase change in soil pH in comparison to the other two sites, largely due to lower clay content and cation exchange capacity. Granulated lime was 5.7 times more expensive than ground lime in its ability to reduce soil acidity. The high treatment rate showed the greatest reduction in soil acidity, aluminium and iron concentration as a mean across all sites. Morgan's soil test phosphorus concentration increased across all sites, with treatment rates having no effect on the rate of increase. There was evidence of reduced soil compaction and lime application showed no negative implication on soil physical structure.

Factorial experiments were carried out on spring- and winter-sown oat crops in Ireland in 2016–2019 to identify plant responses in yield formation and grain filling procedures. Two cultivars (Husky and Keely), three seed rates (250, 350 and 450 seeds/m2), six applied nitrogen (N) rates (40, 70, 100, 130, 160 and 190 kg N/ha) with a plant growth regulator (PGR) were examined in spring and winter sowings, using the same cultivars at five rates of applied N (80, 110, 140, 170 and 200 kg N/ha). The applied N rate and seed rate significantly (P < 0.05) modified the yield components determining grains/m2 in spring- and winter-sown crops with increases in panicle number of key importance. Increases were also observed in the proportion of primary grain by weight (0.9–1.6%) and number (0.7–1.4%) ratios in spring-sown crops in response to applied N rate, with winter-sown crops exhibiting a more consistent pattern of grain fill. Seed rate and PGR application had minor effects on yield components and panicle conformation. Grain fill procedures played an important role in the maximization of grain yield under seasonal conditions. Significant positive correlations were observed between the number of aborted grain sites and yield under drought conditions (0.22), with negative associations observed in near-optimal conditions (−0.22 to −0.41). Agronomic effects on grain site abortion were minimal in comparison with seasonal effects. In seasons characterized by cool, consistent conditions, grain yield was maximized by the utilization of all available grain sites. Where assimilate shortages were encountered during grain fill the abortion of grains sites was positively associated with grain yield.

The objective of this project is to integrate a domestic photocatalytic desulphurization facility with a biogas upgrading module and try to develop a system for biogas desulphurization and upgrading under ambient conditions. Four photocatalytic desulphurization reactors (PDRs) and one activated carbon reactor (ACR) were applied for biogas desulphurization and filtration under ambient conditions. Moreover, a hollow fibre carbon dioxide (CO2) adsorption module was applied for biogas upgrading. The operation pressure of the PDR and ACR was under ambient pressure. Results showed that hydrogen sulphide removal efficiency of the photocatalytic desulphurizer was about 0.99–1.00 (v/v) under the inlet biogas flow less than 5 litres/min and the concentration of inlet hydrogen sulphide was lower than 5600 mg/m3. For desulphurized biogas upgrading, the removal efficiency of CO2 was higher than 0.90 (v/v) under the outlet biogas flow was 1 litre/min (i.e. inlet biogas flow was about 2 litres/min). However, the ratio of methane in the upgrading biogas was lower than 0.90 (v/v). Thus, nitrogen gas removal cartridges will be integrated with the biogas upgrading module to promote methane concentration in the upgraded biogas.

Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Crop production is at the core of a ‘perfect storm’ encompassing the grand challenges of achieving food and nutrition security for all, in the face of climate change, while avoiding further conversion of natural habitats for agriculture and loss of biodiversity. Here, we explore current trends in crop modelling related to these grand challenges by reflecting on research presented at the Second International Crop Modelling Symposium (iCropM2020). A keyword search in the book of abstracts of the symposium revealed a strong focus on ‘climate change’, ‘adaptation’ and ‘impact assessment’ and much less on ‘food security’ or ‘policy’. Most research focused on field-level investigations and far fewer on farm(ing) systems levels – the levels at which management decisions are made by farmers. Experimentation is key to development and testing of crop models, yet the term ‘simulation’ outweighed by far the terms ‘experiments’ and ‘trials’, and few contributions dealt with model improvement. Cereals are intensively researched, whereas roots, tubers and tropical perennials are under-researched. Little attention is paid to nutrient limitations apart from nitrogen or to pests and diseases. The aforementioned aspects represent opportunities for future research where crop models can help in devising hypotheses and driving new experimentation. We must also ensure that crop models are fit for their intended purposes, especially if they are to provide advice to policymakers. The latter, together with cross-scale and interdisciplinary efforts with direct engagement of stakeholders are needed to address the grand challenges faced by food and agricultural systems in the next century.

The Global Yield Gap Atlas (GYGA) is an international project that addresses global food production capacity in the form of yield gaps (Yg). The GYGA project is unique in employing its original Climate Zonation Scheme (CZS) composed of three indexed factors, i.e. Growing Degree Days (GDD) related to temperature, Aridity Index (AI) related to available water and Temperature Seasonality (TS) related to annual temperature range, creating 300 Climate Zones (CZs) theoretically across the globe. In the present study, the GYGA CZs were identified for Japan on a municipality basis and analysis of variance (ANOVA) was performed on irrigated rice yield data sets, equating to actual yields (Ya) in the GYGA context, from long-term government statistics. The ANOVA was conducted for the data sets over two decades between 1994 and 2016 by assigning the GDD score of 6 levels and the TS score of 2 levels as fixed factors. Significant interactions with respect to Ya were observed between GDD score and TS score for 13 years out of 21 years implying the existence of favourable combinations of the GDD score and the TS score for rice cultivation. The implication was also supported by the observation with Yg. The lower values of coefficient of variance obtained from the CZs characterized by medium GDD scores indicated the stability over time of rice yields in these areas. These findings suggest a possibility that the GYGA-CZS can be recognized as a tool suitable to identify favourable CZs for growing crops.