Agroecological services of cover crops depend mostly on their biomass accumulation, which in turn depends on growing season weather and on nitrogen (N) availability. We hypothesised that cover crop growth and weed control can be increased with an early sowing date and under high residual soil inorganic N left after the previous cash crop harvest. This hypothesis was tested in Northern Italy, with a two-year field experiment in two locations with five cover crops, comparing two cover crop sowing dates (beginning of September, SD1, and mid September, SD2), and evaluating the effects of pre-plant soil mineral N addition (N0 and N1). The cover crops, grown between two maize crops, were terminated in March. Weather conditions in both years were drier than normal, reducing on average cover crop growth. In November, cover crop aboveground biomass and N content ranged between 0.5 and 3.3 t DM ha-1 and between 18 and 70 kg N ha-1, respectively, with significantly lower values for SD2 compared to SD1 for most species. In most cases high pre-plant soil mineral N significantly increased cover crop biomass and N content, suggesting that cover crop growth was N-limited. Weed growth was higher in Egyptian clover and hairy vetch (0.7 t DM ha-1 on average) than in white mustard and black oat (0.2 t DM ha-1 on average), due to limited competitive ability of legumes. Cover crop biomass accumulation and weed control were enhanced by early sowing (in particular for legumes) and in conditions of high mineral N residue in the soil (in particular for non-legumes).

In the agroecosystem, surface crop residues are widely recognized as affecting many processes such as soil water dynamics, crop growth, nitrogen and carbon cycling. For this reason, developing models that simulate the effect of surface residues and their decomposition is crucial, especially while modeling conservation agriculture. To date, even though many cropping systems and C-oriented models differently simulate the evolution of surface residue biomass, a comprehensive approach is still missing. In this study, we developed a new simulation module that explicitly simulates the decomposition of surface residues, by including all the variables and processes that are relevant for agroecosystem's simulation. This module has been later integrated into the ARMOSA cropping system model. To quantify the contribution of each parameter to the simulated outputs (i.e., decomposed biomass), a sensitivity analysis (SA) was conducted, comparing the result with the APSIM model used as a benchmark. The SA was conducted on four different crop residues (maize, rye, soybean and wheat) over three different years. In addition, for each crop residue, we verified whether parameters changed their relevance depending on the considered time period. The most critical parameters of the new module reflected the importance of air temperature, soil water content and residue biomass in the decomposition process. The potential decomposition rate had minor importance, highlighting that, when setting crop-specific values, other environment-related parameters are more relevant for the actual decomposition rate. In the case of APSIM model, the potential decomposition rate and the optimum temperature for this process resulted in the first two ranks. Finally, concordance coefficients were used to compare SA outputs: compared to APSIM, the new model showed higher concordance passing from one crop residue to another, even when comparing the different simulation periods within the same crop. In summary, this work presented a novelty in surface crop residue representation and provided a deep survey of the module behavior and characteristics.

Cover crop cultivation provides several benefits, among which the most relevant are nitrate leaching reduction, weed growth control, increase of soil organic matter, improvement of soil structure and of water infiltration. In temperate climates, the adoption of winterkilled cover crops is increasing due to their benefits in intensive cropping systems based on summer cash crops, specifically in northern Italy where maize is frequently planted early. However, there is still a lack of knowledge about winterkilled cover crops management in this framework. We studied various agronomic effects of winterkilled cover crops in a conservation agriculture cropping system. We evaluated biomass production, nitrogen uptake, weed control and frost damage of two pure winterkilled cover crop species (a cereal and a brassica crop), as well as of their mixture with a legume cover crop. White mustard, black oat and their mixture with purple vetch have demonstrated a good aboveground biomass production potential (2-3 t DM ha-1) and nitrogen uptake (45 kg N ha-1 on average, up to 148 kg N ha-1), particularly when planted early (before the first half of September) in optimal conditions for early growth and development; while their weed species control ability has proven to be consistently high. In conclusion, aboveground biomass productivity, N uptake, and weed control of these winterkilled cover crops are promising, with no negative effects on the following cash crop growth.

Nitrogen (N) budgets at farm level are influenced by N fertilisation recommendations. In this study, we reviewed and analysed the underlying principles and methods of N fertilisation recommendations in 10 West European countries, to identify similarities and differences, and develop suggestions for reconsideration and improvement. An analysis of national official documents on N fertilisation recommendations revealed that there were three main categories of calculation methods: (i) 'N mass balances' (France, Italy, Spain), (ii) 'Corrected standards' (Germany, Netherlands, Switzerland, Luxembourg), and (iii) 'Pre-parameterised calculations', which rely on a soil N supply typology (United Kingdom, Ireland, Belgium). In total 16 variables were identified in the calculation methods. The more complex methods use 10 (Italy, France), while the simplest only rely on 3 (Luxembourg). The most common variables include the availability of N in manure, the N uptake by a crop, and the N released by crop residues. Few countries explicitly consider N losses to ground and surface waters or to the atmosphere in the calculation methods. In some countries, the N fertilisation recommendation has a voluntary status, and in other countries, a legal one (caps on maximum allowable N rates). We compared the N fertiliser recommendations for a wheat crop grown on a farm with livestock, and for a farm with a diverse arable crop rotation without livestock. Across the 10 countries, large differences in the N fertilisation calculation methods and resulting N recommendations existed for the two management scenarios, ranging from almost no fertilisation to 135 kg N ha-1, and from 111 to 210 kg N ha-1, respectively. The differences were not accounted for by the complexity of the equations used, but rather resulted from contrasting reference values for N availability in manure, N uptake by crop and N leaching. However, the study concluded that standardisation of the method to calculate N fertilisation recommendations is likely to be counterproductive as there are no objective reasons to favour one method more than the others. Nonetheless, improvements in N use efficiency are necessary. Farm scale mass balance, combined with parameters such as minimum residual soil mineral N test at harvest, was suggested as being an important consideration.

The Oklahoma State University algorithm (OSU) is the most widespread recommendation system to calculate the optimal nitrogen (N) rate in cereal crops. This system is based on the map of a crop vegetation index and N-rich strips in the field, serving as a reference for crop vigour. Based on these inputs, yield potential and in-season crop response to N fertilisation are predicted. The system does not consider other factors influencing the spatial variability of crop growth (e.g., soil), even if the integration of additional data sources could provide more correct N recommendations by considering simultaneously the main drivers of crop growth and production variability. Therefore, the main aims of this study were to modify the OSU algorithm by merging maps of soil electrical conductivity and crop vegetation index (NDRE) to identify management zones instead of using vegetation index alone and to implement and apply the algorithm to define crop response to nitrogen, specific by management zones using a statistical approach instead of N-rich strips in the field. The proposed algorithm was calibrated for maize in northern Italy on four experimental fields in 2021. A comparison among the proposed, the original algorithm and one its previous modification (at Clemson University) was carried out in terms of spatial accuracy and levels of recommended N rates. The proposed algorithm, thanks to the integrated approach, distinguished more in detail different crop responses to N within each management zone and provided N recommendations that match the within-field variability better than the original algorithm and subsequent similar modifications. The new approach resulted in potential average N savings of about 12% compared to uniform management. We conclude that the potential of merging soil and vegetation indices and the definition of N rates with a statistical approach could improve performances and, at the same time, facilitate the adoption of the OSU algorithm. Field validation is needed to confirm the promising results shown in this work.

Cover crops provide agro-ecological services like erosion control, improvement of soil quality, reduction of nitrate leaching and weed control. Before planting the subsequent cash crop, cover crops need to be terminated with herbicides, mechanically or with the help of frost (winterkill). Winterkill termination is expected to increase its relevance in the next years, especially for organic farming due to limitations in the use of herbicides and for conservation agriculture cropping systems. Termination by frost depends on complex interactions between genotype, development stage and weather conditions. To understand these interactions for management purposes, crop frost damage models, whose review is the purpose of this article, can be very useful. A literature search led to the collection of eight frost damage models, mainly dedicated to winter wheat. Three of these models are described in detail because they appear suited to adaptation to cover crops. Indeed, they explicitly simulate frost tolerance acquisition and loss as influenced by development stage using a crop frost tolerance temperature, whose rate of variation depends on the processes of hardening and dehardening. This tolerance temperature is compared daily with environmental temperature to calculate frost damage to the vegetative organs. The three models, when applied to winter wheat in Canada, Norway and France, have shown good agreement between measured and simulated crop frost tolerance temperature (when declared, the root mean squared error was 2.4°C). To compare the behaviour of these models, we applied them in two locations with different climatic conditions (temperate climate: Sant’Angelo Lodigiano, Italy, and continental climate: Saskaatoon, Canada) with respect to frost tolerance acquisition. This comparison revealed that the three models provide different simulated dates for the frost damage event in the continental site, while they are more similar in the temperate site. In conclusion, we have shown that the reviewed models are potentially suitable for simulating cover crop frost damage. Highlights - Frost termination is very important for cover crops and needs to be simulated with crop models. - Lacking a cover crop frost damage model, we review eight models simulating damage of cash crops, namely cereals. - Three of these models are also applicable to cover crops and are described in more detail. - The simulated crop frost tolerance temperature decreases and increases with hardening and dehardening, respectively. - This tolerance temperature is compared with environmental temperature to calculate frost damage to the crop.

Vegetation indices are used in precision agriculture to estimate crop aboveground biomass (AGB) and, in turn, to quantify crop needs. However, crop species and development stage affect vegetation indices limiting the setup of generalized models for AGB estimation. Some approaches to overcome this issue have combined vegetation indices and structural crop properties such as crop height. However, only a few studies have considered different herbaceous crops like forages and cover crops. A 2-year field experiment was carried out on five winter cover crops with different habits at a high cover fraction (on average 93%) to study if combining vegetation indices, crop height and the fraction of soil covered by the crop could improve AGB estimation. Seven vegetation indices, crop height and cover fraction were derived from UAV-multispectral images. Species-specific and global (including all species) regression models were built and tested through cross-validation (CV). Green-based indices were the best estimators of AGB (RCV2 = 0.56–0.93, normalized root mean square error in CV nRMSECV = 26–38%) of the five species, separately. A global linear model using crop height alone, provided good results (RCV2 = 0.57, nRMSECV = 42%). Also, stepwise multiple regression was used to get a global model with crop height and five vegetation indices (RCV2 = 0.75, nRMSECV = 31%). Finally, a model was proposed where AGB was estimated by a vegetation index until plants covered 97% of soil or its height was shorter than 125 mm and by crop height for vegetation taller than 125 mm. The promising results (RCV2 = 0.65, nRMSECV = 36%) suggested the possibility of increasing AGB estimation by considering both vegetation indices and structural crop properties.

The utilization of winter-killed cover crops is increasing due to their benefits in intensive cropping systems based on summer cash crops. However, there is still a lack of knowledge about their management in temperate climates, where maize is planted early under conservation tillage techniques. For these conditions, here we document for the first time various agronomic effects of winter-killed cover crops under different management options. We evaluated the production, the nitrogen uptake, and the weed control of five pure winter-killed cover crop species, as well as the production of the subsequent maize and its nitrogen recovery, in four different sites. Several management options were compared (cover crop fertilization, sowing technique, and cover crop termination method). Legume cover crops (Trifolium alexandrinum L. and Vicia benghalensis L.) had a small above-ground biomass (on average 0.6 t DM ha−1 in November), while for non-legumes (Avena strigosa Schreb., Sinapis alba L., and Raphanus sativus L.) the production (on average 2.4 t DM ha−1, N uptake 89 kg N ha−1) was higher, as well as weed control and N uptake. The difference in the above-ground biomass between the two groups of cover crops was smaller at the end of winter (0.4 Mg DM ha−1), when in five out of eight site × year combinations, soil mineral N was significantly higher in a cover crop treatment compared to the no cover crop, presumably due to N release from cover crops. Cover crops did not increase maize production, and their residues did not hamper maize sowing and emergence. The recovery of cover crop N by maize was 86% for legumes and − 1% for non-legumes. We conclude that productivity, N uptake, and weed control of winter-killed cover crops (especially non-legume species) are encouraging, with no negative effects on maize yield.

Cover crops are grown in order to provide agro-ecological services and must be terminated before planting the subsequent cash crop. Winterkill termination (by frost damage) depends on the interaction between crop frost hardiness, temperatures and the development stage reached at the time of sub-zero temperature exposure. Remotely sensing intensity, timing and spatial variation of cover crop frost damage can be useful for modeling and planning purposes. Therefore, in this study Sentinel-2 vegetation indices were employed in order to detect frost damage in four white mustard (Sinapis alba L.) fields located in Northern Italy. We estimated the starting date of frost events by means of vegetation indices (EVI, NDRE, NDVI, MMSR, and CCCI); we quantified and mapped frost damage at the sub-field level, using ground-based frost damage measurements carried out during the 2021/2022 season. As to frost damage quantification, MMSR outperformed the other VIs followed by CCCI and EVI (R2 > 0.55). The adopted procedure to detect starting dates of frost events was successful in most cases, with a one-day and a four-day delay in the two best cases (NDRE). Finally, maps of frost damage were consistent with its observed spatial variation. We demonstrated that it is possible to employ vegetation indices in order to detect cover crop frost damage and thus assessing cover crop winterkill termination efficiency in the field. Further research is needed, involving additional field monitoring of white mustard in more diverse conditions, and extension of the calibration, as well as validation.

A scoping review of the relevant literature was carried out to identify the existing N recommendation systems, their temporal and geographical diffusion, and knowledge gaps. In total, 151 studies were identified and categorized. Seventy-six percent of N recommendation systems are empirical and based on spatialized vegetation indices (73% of them); 21% are based on mechanistic crop simulation models with limited use of spatialized data (26% of them); 3% are based on machine learning techniques with integration of spatialized and non-spatialized data. Recommendation systems started to appear worldwide in 2000; often they were applied in the same location where calibration had been carried out. Thirty percent of the studies use advanced recommendation techniques, such as sensor/approach fusion (44%), algorithm add-ons (30%), estimation of environmental benefits (13%), and multi-objective decisions (13%). Some limitations have been identified. Empirical systems need specific calibrations for each site, species and sensor, rarely using soil, vegetation and weather data together, while mechanistic systems need large input data sets, often non-spatialized. We conclude that N recommendation systems can be improved by better data and the integration of algorithms.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Best management practices that could improve sustainability of dairy farming systems in northern Italy include crop rotation, green manure, sprinkler or drip irrigation, incorporation of crop residue, and adoption of a nutrient management plan. Despite the numerous advantages that scientific literature reports for these Best management practices, they are not always adopted by farmers, because other factors – of financial, technical, or social nature – limit their adoption. The theory of planned behaviour, based on the identification of outcomes, referents surrounding the farmers, and control factors, was applied through a detailed questionnaire to study individual farmer beliefs that influence the intention to adopt best practices. More than 50% out of the farms applied incorporation of crop residue, rotation with a grass or a legume meadow, sprinkler or drip irrigation, and adopted a nutrient management plan. Reasons for applying them were mainly related to soil sustainability (improvement of soil organic matter content, soil structure, fertility and yield) or to environmental sustainability (reduction of nitrogen losses, use of fertilizers, herbicides or insecticides). Among the main barriers to their adoption, the most important ones were an increase in direct or indirect costs. The only practice that was not adopted and, despite a limited number of barriers, will not be adopted by farmers, is green manure. Likely, our survey did not capture the real barriers against the adoption of this practice. Across all best management practices, the main difference between adopters and non-adopters was found in referents' opinion on applying them. This means that it is very important, for the adoption of best management practices, that the community of family members, neighbor farmers, and various advisors, are in favour of adoption. This important finding should be used by public authorities to promote the development of focus groups, demonstration days, demonstration farms, and especially good and updated independent farm advisors who could substantially increase the adoption of best management practices by farmers.

AbstractThe plant availability of manure nitrogen (N) is influenced by manure composition in the year of application whereas some studies indicate that the legacy effect in following years is independent of the composition. The plant availability of N in pig and cattle slurries with variable contents of particulate matter was determined in a 3-year field study. We separated cattle and a pig slurry into liquid and solid fractions by centrifugation. Slurry mixtures with varying proportions of solid and liquid fraction were applied to a loamy sand soil at similar NH4+-N rates in the first year. Yields and N offtake of spring barley and undersown perennial ryegrass were compared to plots receiving mineral N fertilizer. The first year N fertilizer replacement value (NFRV) of total N in slurry mixtures decreased with increasing proportion of solid fraction. The second and third season NFRV averaged 6.5% and 3.8% of total N, respectively, for cattle slurries, and 18% and 7.5% for pig slurries and was not related to the proportion of solid fraction. The estimated net N mineralization of residual organic N increased nearly linearly with growing degree days (GDD) with a rate of 0.0058%/GDD for cattle and 0.0116%/GDD for pig slurries at 2000–5000 GDD after application. In conclusion NFRV of slurry decreased with increasing proportion of solid fraction in the first year. In the second year, NFRV of pig slurry N was significantly higher than that of cattle slurry N and unaffected by proportion between solid and liquid fraction.

A number of policies proposed to increase soil organic matter (SOM) content in agricultural land as a carbon sink and to enhance soil fertility. Relations between SOM content and crop yields however remain uncertain. In a recent farm survey across six European countries, farmers reported both their crop yields and their SOM content. For four widely grown crops (wheat, grain maize, sugar beet and potato), correlations were explored between reported crop yields and SOM content (N = 1264). To explain observed variability, climate, soil texture, slope, tillage intensity, fertilisation and irrigation were added as co-variables in a linear regression model. No consistent correlations were observed for any of the crop types. For wheat, a significant positive correlation ( p < 0.05) was observed between SOM and crop yields in the Continental climate, with yields being on average 263 ± 4 (95% CI) kg ha −1 higher on soils with one percentage point more SOM. In the Atlantic climate, a significant negative correlation was observed for wheat, with yields being on average 75 ± 2 (95%CI) kg ha −1 lower on soils with one percentage point more SOM ( p < 0.05). For sugar beet, a significant positive correlation ( p < 0.05) between SOM and crop yields was suggested for all climate zones, but this depended on a number of relatively low yield observations. For potatoes and maize, no significant correlations were observed between SOM content and crop yields. These findings indicate the need for a diversified strategy across soil types, crops and climates when seeking farmers’ support to increase SOM.

Process‐based crop and grassland models estimating carbon (C) and nitrogen (N) dynamics are widely used to investigate best management practices in agriculture. They integrate several processes in a complex structure, but studies where modules corresponding to specific processes extracted from the whole model structure are assessed independently are uncommon. With the support of documented aerobic incubation trials in manure‐amended soils, a sensitivity analysis was performed on the C–N cycling processes of four modules (MOD1–4), corresponding to the models APSIM, EPIC, FASSET and STICS. The results showed that the parameter ‘substrate use efficiency’ had the most effect on the predicted values of net CO2 emissions and net N mineralization, together with the C/N ratio of the soil microbial biomass. They explained 74–75% on average of both output variances, whereas parameters determining manure C and N partitioning and first‐order decomposition constants of manure pools explained, on average, an additional 17–19%. Efforts should be focused on calibrating these parameters for more accurate simulations. The greater sensitivity of both outputs to parameters related to manure pools in more complex modules (MOD2–4) facilitates their adaptation to specific contexts, whereas MOD1 probably requires that parameters related to soil pools are also adapted to specific applications. Parameter interactions were limited, becoming noticeable only in situations of N‐limited soil organic matter decomposition. Models MOD1 and MOD3 allowed the C/N ratio of the soil microbial biomass to vary temporarily; therefore, they were less sensitive to mineral N availability and more easily adapted to a wide range of situations. This study provides essential information to support the development of state‐of‐the‐art biogeochemical models.Highlights We compared four C–N modules embedded in process‐based biogeochemical models. We used sensitivity analysis to assess the simulation of manure decomposition in soil. We identified a few parameters that influenced CO2 emissions and N mineralization. We found that substrate use efficiency explained most of the output variance for all models.

Soil organic matter (SOM) in agricultural soils builds up via – among others the use of organic inputs such as straw, compost, farmyard manure or the cultivation of green manures or cover crops. SOM has benefits for longterm soil fertility and can provide ecosystem services. Farmer behaviour is however known to be motivated by a larger number of factors. Using the theory of planned behaviour, we aimed to disentangle these factors. We addressed the following research question: What are currently the main drivers and barriers for arable farmers in Europe to use organic inputs? Our study focuses on six agro-ecological zones in four European countries (Austria, Flanders [Belgium], Italy and the Netherlands) and four practices (straw incorporation, green manure or cover crops, compost and farmyard manure). In a first step, relevant factors were identified for each practice with farmers using 5 to ten semi-structured interviews per agro-ecological zone. In a second step, the relevance of these factors was quantified and they were classified as either drivers or barriers in a large scale farm survey with 1263 farmers. In the semi-structured interviews, 110 factors that influenced farmer decisions to use an organic input were identified. In the larger farm survey, 60% of the factors included were evaluated as drivers, while 40% were evaluated as barriers for the use of organic inputs. Major drivers to use organic inputs were related to the perceived effects on soil quality (such as improved soil structure or reduced erosion) and the positive influence from social referents (such as fellow farmers or agricultural advisors). Major barriers to use organic inputs were financial (increased costs or foregone income) and perceived effects on crop protection (such as increased weeds, pests and diseases, or increased pesticide use). Our study shows that motivating farmers to use organic inputs requires specific guidance on how to adapt cultivation practices to reduce weeds, pests and diseases for specific soil types, weather conditions, and crops. In addition, more research is needed on the long-term financial consequences of using organic inputs.