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.

Spreading of pruning waste over the soil surface may increase soil organic carbon, thus improving soil physical properties and serving as a source of nutrients and energy for microbial populations. The aim of this study was to test the effect of the environmental conditions and the biochemical composition of pruning waste from avocado, cherimoya, mango and gardens on their decomposition process in a Mediterranean subtropical climate. Bagged pruning and garden waste were placed on the ground at a distance of 1 m around the trunk of the three trees from each crop. The concentrations in C, N, lignin, cellulose, hemicellulose, other extracts and ash were determined at the beginning of the experiment (T0), after six (T6) and 24 (T24) months in the field. Initially, significant differences were detected for all types of waste, especially in lignin, hemicellulose, cellulose and other extracts. No significant differences were found in the N content and the C content in mango pruning waste was significantly lower than that in avocado. The greatest weight loss recorded at T24 (63.2%) was related to the lower content in lignin, cellulose and other extracts. Weight losses and C concentrations showed negative correlations with lignin content. Despite the intense decomposition of all the waste, between 55 and 36.8% of the original weights were recorded at the end of the experiment. Recalcitrant C could be the result of the lignin concentrating in the case of the garden waste applied to the different crops.

The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

The characteristic traits of maize (Zea mays L.) leaves affect light interception and photosynthesis. Measurement or estimation of individual leaf area has been described using discontinuous equations or bell-shaped functions. However, new maize hybrids show different canopy architecture, such as leaf angle in modern maize which is more upright and ear leaf and adjacent leaves which are longer than older hybrids. The original equations and their parameters, which have been used for older maize hybrids and grown at low plant densities, will not accurately represent modern hybrids. Therefore, the aim of this paper was to develop a new empirical equation that captures vertical leaf distribution. To characterize the vertical leaf profile, we conducted a field experiment in Jilin province, Northeast China from 2015 to 2018. Our new equation for the vertical distribution of leaf profile describes leaf length, width or leaf area as a function of leaf rank, using parameters for the maximum value for leaf length, width or area, the leaf rank at which the maximum value is obtained, and the width of the curve. It thus involves one parameter less than the previously used equations. By analysing the characteristics of this new equation, we identified four key leaf ranks (4, 8, 14 and 20) for which leaf parameter values need to be quantified in order to have a good estimation of leaf length, width and area. Together, the method of leaf area estimation proposed here adds versatility for use in modern maize hybrids and simplifies the field measurements by using the four key leaf ranks to estimate vertical leaf distribution in maize canopy instead of all leaf ranks.

One of the major challenges in agriculture is how climate change influences crop production, for different environmental (soil type, topography, groundwater depth, etc.) and agronomic management conditions. Through systems modelling, this study aims to quantify the impact of future climate on yield risk of winter wheat for two common soil types of Eastern Denmark. The agro-ecosystem model DAISY was used to simulate arable, conventional cropping systems (CSs) and the study focused on the three main management factors: cropping sequence, usage of catch crops and cereal straw management. For the case region of Eastern Denmark, the future yield risk of wheat does not necessarily increase under climate change mainly due to lower water stress in the projections; rather, it depends on appropriate management and each CS design. Major management factors affecting the yield risk of wheat were N supply and the amount of organic material added during rotations. If a CS is characterized by straw removal and no catch crop within the rotation, an increased wheat yield risk must be expected in the future. In contrast, more favourable CSs, including catch crops and straw incorporation, maintain their capacity and result in a decreasing yield risk over time. Higher soil organic matter content, higher net nitrogen mineralization rate and higher soil organic nitrogen content were the main underlying causes for these positive effects. Furthermore, the simulation results showed better N recycling and reduced nitrate leaching for the more favourable CSs, which provide benefits for environment-friendly and sustainable crop production.

The paper presents results from a study examining the relationship between large-scale modes of climate variability with the fluctuations in the yield of barley, durum wheat, olives and sunflower crops in Tuscany, Italy. In particular, the blocking circulation over the growing season, with associated hot and dry conditions, decreased yield for olive crops, barley and durum wheat. The teleconnections analysed in this study are the winter North Atlantic Oscillation (NAO) and the Summer North Atlantic Oscillation (SNAO); the West African Monsoon (WAM) and the Intertropical Front (ITF); and although NAO, SNAO, ITF and WAM are not strictly related to each other, the values of these indices are strongly related to the atmospheric circulation regimes and related weather types. Thus, they have an impact on precipitation and temperature patterns in Italy and on yields of important crops in Tuscany. Results show that the large-scale temperate and tropical variability directly influences the crop yield through three main circulation regimes. These patterns illustrate the importance of the large-scale modes, which, together with the associated weather types, have an impact directly on Tuscan crop yields; both barley and olive yields decline significantly when the ITF is further north with warmer and drier conditions in Italy.

Durum wheat culture requires a high level of N fertilization to achieve ideal protein concentration for semolina and pasta quality, contributing to N losses. Optimizing plant N use efficiency could improve agro-environmental balance. In the current paper, we studied the impact of the marine (DPI4913) and fungal (AF086) extracts (biostimulants) applied on leaves on growth, N absorption and N fluxes in durum wheat in field and greenhouse experiments. In the field, 15NO3− and 15NH4+ were injected into the soil; in the greenhouse, N of the flag-leaf was labelled with 15NH4+. Flag-leaf senescence was studied by estimating leaf chlorophyll concentration. In greenhouse, biostimulants increased grain yield, total N in plant and the proportion of plant N in ears. When water was limited in greenhouse experiment, neither biostimulants had any effect. In the field, DPI4913 increased soil fertilizer-derived 15N accumulated in grains. In the greenhouse, biostimulants increased the proportion of 15N applied to the flag-leaf recovered in grains and accelerated leaf senescence. For plants treated with biostimulants, flag-leaf N resorption increased. Biostimulants had a larger positive impact on mineral N root uptake than on N remobilization. In conclusion, our study has shown that DPI4913 and AF086 can promote plant growth and grain yield, N uptake and remobilization. Thus, these biostimulants could be used to optimize durum wheat N fertilization and contribute to reduced N losses.

The development of resilient cropping systems with high yield stability is becoming increasingly important due to future climatic and agronomic challenges. Consequently, it is essential to compare the effects of different agronomic management practices, such as cropping sequences and nutrient supply, on the stability of crop yields. Long-term experiments are a valuable resource for investigating these effects, as they provide enough time to accurately estimate stability parameters. The objective of the current study was to compare the effects of different cropping sequencing (#1: continuous v. rotational), fertilization (#2: mineral v. organic) and straw management techniques (in the case of continuous wheat; #3: removal v. incorporation) on the yield stability of winter wheat; yield risk (the probability of yield falling below a threshold yield level) and inter-annual yield variability were used as stability indicators of the effects. Long-term yield data from the Broadbalk Wheat Experiment (Rothamsted, UK) were analysed using a mixed model. Overall, the results showed that rotational cropping combined with sufficient mineral N fertilizer, with or without organic manure, ensured stable wheat yields while reducing yield risk. In contrast, higher yield risks and inter-annual yield variabilities were found in continuous wheat sections with less mineral N fertilizer or with organic manure only.

Protein content (PC) and oil content (OC) are important breeding traits of soybean [Glycine max (L.) Merr.]. Quantitative trait locus (QTL) mapping for PC and OC is important for molecular breeding in soybean; however, the negative correlation between PC and OC influences the accuracy of QTL mapping. In the current study, a four-way recombinant inbred lines (FW-RILs) population comprising 160 lines derived from the cross (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19) was planted in eight different environments and PC and OC measured. Conditional and unconditional QTL analyses were carried out by interval mapping (IM) and inclusive complete IM based on linkage maps of 275 simple sequences repeat markers in a FW-RILs population. This analysis revealed 59 unconditional QTLs and 52 conditional QTLs among the FW-RILs. An analysis of additive effects indicated that the effects of 13 protein QTLs were not related to OC, whereas OC affected the expression of 13 and eight QTLs either partially or completely, respectively. Eight QTLs affecting OC were not influenced by PC, whereas six and 26 QTLs were partially and fully affected by PC, respectively. Among the QTLs detected in the current study, two protein QTLs and five oil QTLs had not been previously reported. These findings will facilitate marker-assisted selection and molecular breeding of soybean.