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.

Early-maturing provitamin A (PVA) quality protein maize (QPM) hybrids with combined drought and low soil nitrogen (low-N) tolerance are needed to address malnutrition and food security problems in sub-Saharan Africa (SSA). The current study's objectives were to (i) examine combining ability of selected early maturing PVA-QPM inbreds for grain yield and other agronomic traits under drought, low-N, optimal environments and across environments, (ii) determine gene action conditioning PVA accumulation under optimal environments, (iii) classify inbreds into heterotic groups and identify testers and (iv) assess yield and stability of hybrids across environments. Ninety-six hybrids generated from 24 inbred lines using the North Carolina Design II together with four commercial hybrid controls were evaluated under drought, low-N and optimal environments in Nigeria in 2016 and 2017. Fifty-four selected hybrids were assayed for PVA carotenoid and tryptophan content. Additive genetic effects were greater than non-additive effects for grain yield and most agronomic traits under each and across environments. The gene action conditioning accumulation of PVA carotenoids under optimal growing conditions followed a pattern similar to that of grain yield and other yield-related traits. The inbred lines were categorized into four heterotic groups consistent with the pedigree records and with TZEIORQ 29 identified as the best male and female tester for heterotic group IV. No tester was found for the other groups. Hybrid TZEIORQ 24 × TZEIORQ 41 was the highest yielding and most stable across environments and should be further tested for consistent performance for commercialization in SSA.

In Central America, population and food demands are rising rapidly, while yields of staple crops, maize and beans, remain low. To identify the main factors limiting production, field trials were established in six maize- and bean-producing regions in Guatemala, Honduras and El Salvador, representing about three-quarters of the maize-producing area. Potential yield-limiting factors were evaluated in 2017 and included: water stress, nutrient deficiency, pest and disease pressure, and/or inter-plant competition. When considering all sites, improved fertilization and pest and disease control significantly improved yields in maize by 11 and 16%, respectively but did not have a significant effect in beans. Irrigation had no effect due to good rainfall distribution over the growing season. Optimized planting arrangement resulted in an average 18% increase in maize yield, making it the most promising factor evaluated. The treatment and site combinations that increased both crop productivity and net profit included management changes that improved resource use efficiency. However, the contribution of each limiting factor to yield gaps varied across sites and no treatment was effective at increasing yield consistently across sites. Production constraints are highly dependent on local management practices and agroecological location. Therefore, public and private development efforts that seek to increase production should conduct multi-year, participatory experiments to identify limitations pertinent to the area in question. The next step is then to evaluate sustainable and profitable practices, to address those limitations and provide sound recommendations to farmers while decreasing the environmental and economic costs.

Noy-Meir's simple but insightful model of grazing-system dynamics was used to draw broader inferences from empirical data generated by a 17-year field trial with beef cattle grazing a Mediterranean grassland in northern Israel. After calibration of its parameters against the field results, the model predictions were tested against an independent set of data obtained from the study site; they were within acceptable deviations from the inherently noisy field data. The calibrated model was used to analyse the effects of changes to two key grazing-management factors – stocking density and early-season grazing deferment – on biomass dynamics and forage consumption. The simulated results were used to calculate forage deficits and supplementary feed requirements for optimum herd performance during the growth (‘green’) season and throughout the year. The results revealed a critical stocking density of 0.7 Animal Units (AU)/ha, above which early-season deferment reduced the amount of supplementary feed required to maintain the optimum production of the herd. Optimum stocking is higher when the grassland is used mainly in the highly nutritious green season. Responses of the strongly seasonal Mediterranean grassland to the interaction between stocking density and early-season grazing deferment were expressed by a calibrated model, in terms that determine the efficiency of forage supplementation of the herd during the green season and throughout the year.

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 probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

Measurement of water consumption and urinary nitrogen (UN) excretion of individual grazing ruminants is difficult, time-consuming and expensive. Therefore, prediction and modelling are critical for research to improve N and water use efficiency. The objective of the current work was to use a mechanistic model of a grazing ruminant, MINDY, to represent drinking and urination diurnal patterns, and the resulting pattern of UN excretion. This work is primarily an integration of existing knowledge of basic urination physiology and water dynamics in ruminants. MINDY reproduces observed patterns of urination achieving the correct temporal occurrence, relative volumes and nitrogen (N) concentration of individual urination events for a grazing dairy cow, comparable with those reported in the literature. The model simulates daily water imbibed and UN realistically, as well as ingestion rates for herbages with different protein content and contrasting grazing managements. Results of a cross-validation indicate that the root mean square prediction error and mean absolute error as % of the observed mean, respectively, were 26 and 23% for daily water imbibed, 26 and 27% for urination volume, and 25 and 19% for the frequency of urination. Although further parameterization and validation are needed, for a new development in an exploratory model like MINDY, these numbers are encouraging and reflect that the concepts encoded capture many of the underlying biological mechanisms that drive the diurnal pattern and daily UN excretion, as well as thirst, acceptable.

A probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006–2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006–2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.

Maize (Zea mays L.) is a major staple food and cash crop in sub-Saharan Africa (SSA). However, its production and productivity are severely constrained by drought. A total of 120 single-cross hybrids and an open-pollinated control variety were evaluated for 2 years at two locations under managed drought and rain-fed conditions in Nigeria. The objective of the present study was to assess their performance, classify them into distinct heterotic groups and identify promising hybrids for commercialization in the West and Central Africa sub-region. General combining ability and specific combining ability mean squares were highly significant for grain yield and other traits under the research environments. However, there was a preponderance of additive gene action over non-additive. Only six out of 39 inbreds were classified into distinct heterotic groups by the testers. The highest-yielding drought-tolerant hybrid, TZEEI 102 × TZEEI 95, out-yielded the open-pollinated control variety by 43·70%. The average yield reduction under drought was 54·90% of the yield under rain-fed conditions. The hybrids TZEEI 81 × TZEE1 79, TZEEI 100 × TZEEI 63 and TZEEI 64 × TZEEI 79 were the highest-yielding and most stable across environments. These outstanding drought-tolerant hybrids, which are also resistant to Striga, have the potential to contribute to food security and increased incomes in SSA and should be tested extensively on-farm and commercialized.

The goal of the present study was to improve the CERES-wheat model simulation of grain protein concentration (GPC) for winter durum wheat and to use the model as a basis for the development of a GPC Simplified Forecasting Index (SFIpro). The performances of CERES-wheat, which is one of the most widespread crop simulation models, with (i) its standard GPC routine and (ii) a novel equation developed to improve the model GPC simulation for durum wheat, were assessed through comparison with field data. Subsequently, CERES-wheat was run for a 56-year period in order to identify the most important status and forcing variables affecting GPC simulation. The number of dry days during the early growth stages and the leaf area index (LAI; green leaf area per unit ground surface area) at heading stage (LAI5) were identified as the main variables positively correlated with CERES-wheat predicted GPC, and so included in the SFIpro. At validation against observed data SFIpro was found to perform differently on the basis of observed plant LAI. In fact, SFIpro was able to forecast GPC variability for intermediate values of LAI5 ranging from 1 to 2, while it totally failed when LAI5 was outside this range (LAI5 < 1 or LAI5 > 2). The results suggest that the relationship between LAI and GPC is not linear and that the model assumptions for GPC simulation in CERES-wheat are only partially confirmed, being valid for an intermediate range of LAI.