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Nitrogen fertilisation contributes significantly to the atmospheric increase of nitrous oxide (N2O). Application of nitrification inhibitors (NIs) is a promising strategy to mitigate N2O emissions and improve N-use efficiency in agricultural systems. This study investigated the effect of NI, 3,4-dimethylpyrazol phosphate (DMPP) on N2O mitigation from spring barley and spring oilseed rape. Manual and automatic chamber methodologies were used to capture spatial and temporal variability in N2O emissions. In a second experiment, we study the effect of N fertiliser levels without NI (0 %, 50 %, 100 %, 150 % and 200 % of recommended amount of N fertiliser), as well as 100 % of N with NI on N2O emissions in spring barley. The automated chamber measurements showed dynamics of N2O changes throughout the season, including positive and negative peaks that were unobservable with manual chambers due to low temporal resolution. Although not significant, application of NI tended to reduce N2O emissions. The reduction was on average 16 % in spring barley and 58 % in spring oilseed rape in manual chamber measurements. However, N2O reduction was 108 % in continuous automatic chamber measurements in spring barley. The N2O EFs for the growing season were very low (0.025 % to 0.148 %), with a greater reduction in EF in spring oilseed rape (76 %) than in spring barley (32 %) with NI application. A positive correlation (R = 80 %) was observed between N fertiliser levels and N2O emissions. Crop yield and crop N uptake were not significantly affected by the use of NI. This study highlighted that NI can reduce N2O emissions, but the reduction effects are plot, crop and microclimate specific. Long-term experiments with continuous plot-scale measurements are needed to capture and optimise N2O mitigation effect of NIs across wide variability in soils and microcl

Appropriate soil amendments may increase plant available water and crop yields on coarse sandy soils under drought conditions. In this study, we applied straw ash or straw biochar from gasification to a Danish coarse sandy subsoil to assess the effects on soil water retention, evapotranspiration and crop yields. Spring barley (2016, 2017) and winter wheat (2018) were grown over three years in columns containing 25cm of organic matter-rich topsoil, 80 cm of amended coarse sandy soil (1.5%, 3%, 6% wt. ash or 1% wt. biochar or control soil) and 45 cm of un-amended subsoil. Precipitation, evaporative demands and soil moisture were recorded across the growth seasons, with 2018 having severe drought conditions. This year evapotranspiration levels increased with increasing ash and biochar content (by 54% and 33% for the 6% ash- and 1% biochar-amended soils, respectively), and plant dry matter increased by 18% in both the 1% biochar- and 6% ash-treated soils compared to the untreated control. A linear relationship was established between in situ field capacity and ash dosage (R2=.96), showing an increase of 2.2% per percentage (wt.) of ash added, while the 1% biochar treatment increased the capacity by 3.5%, indicating a higher efficiency than for ash. However, we did not find significant positive effects on grain yields. The results show that ash and biochar have the potential to significantly increase soil water retention, evapotranspiration and total dry matter yield in drought conditions, but that this may not correspond to an increase in grain yield.

This deliverable reports on the work conducted within WP3, based on two existing crop growth models, APSIM and DAISY. The objective of deliverable 3.1 is to identify the key traits and mechanisms underpinning beneficial plant associations, by calibrating, validating and running APSIM and DAISY. For each model, this report presents in detail i) the data used for model calibration and validation, and the rationale for their choice; ii) the calibration and validation process; iii) the results of simulation runs and comparison with field trial data across pedoclimatic conditions; and iv) a discussion of the key aspects driving the performance of each model and the key plant traits defining the output, with particular reference to intercropped systems. In addition, the report also presents an evaluation of resource use efficiencies in support of the modelling work. On the basis of the calibration and validation results, the two models are also contrasted. APSIM and DAISY showed some promising results for the simulation of spring wheat-faba bean and spring barley-field pea systems, towards the identification of the key traits and mechanisms driving the interaction of cereals and legumes in field conditions and across different pedoclimatic regions. Further steps are discussed towards the improvement of the model capabilities, in particular pertaining intercropped systems, also exploiting some additional experimental results relative to plant nutrient use efficiency. (PDF) Summary report on mechanisms underpinning beneficial plant associations based on APSIM and DAISY. Available from: https://www.researchgate.net/publication/342410836_Summary_report_on_mechanisms_underpinning_beneficial_plant_associations_based_on_APSIM_and_DAISY?channel=doi&linkId=5ef3009ea6fdcc158d2605eb&showFulltext=true [accessed Oct 22 2021]., This deliverable reports on the work conducted within WP3, based on two existing crop growth models, APSIM and DAISY. The objective of deliverable 3.1 is to identify the key traits and mechanisms underpinning beneficial plant associations, by calibrating, validating and running APSIM and DAISY. For each model, this report presents in detail i) the data used for model calibration and validation, and the rationale for their choice; ii) the calibration and validation process; iii) the results of simulation runs and comparison with field trial data across pedoclimatic conditions; and iv) a discussion of the key aspects driving the performance of each model and the key plant traits defining the output, with particular reference to intercropped systems. In addition, the report also presents an evaluation of resource use efficiencies in support of the modelling work. On the basis of the calibration and validation results, the two models are also contrasted. APSIM and DAISY showed some promising results for the simulation of spring wheat-faba bean and spring barley-field pea systems, towards the identification of the key traits and mechanisms driving the interaction of cereals and legumes in field conditions and across different pedoclimatic regions. Further steps are discussed towards the improvement of the model capabilities, in particular pertaining intercropped systems, also exploiting some additional experimental results relative to plant nutrient use efficiency.

Intercropping of cereal/grass-legume is an ecological intensification measure where several crop components are grown together to facilitate complementarity between crop species for better utilization of available solar radiation, nutrients and water to maximise biomass productivity (Ghaley et al., 2005). Modelling of intercropping systems are a useful measure to assess biomass production in cereal/grass-legume intercrops and their residual effects on the following bioenergy crops like maize and sorghum to predict the productivity. As the current crop-soil process models are mainly built for monoculture, there is a lack of tools to simulate the biomass accumulation under intercropping contexts. In this study, the objective is to demonstrate the capability of Daisy model to simulate barley-pea intercrop biomass production in Denmark.

In the central highlands of Kenya, decreasing farm size due to land fragmentation upon inheritance has the potential to threaten livelihoods and food security. Farmers are forced to prioritise food, fodder, cash crops, and trees to make the best use of limited space. The objective of this study was to investigate whether and how decreasing land size shapes agroforestry practices, as well as the effects of having trees on farms. In order to gain a holistic perspective on agroforestry practices and their impacts in the Othaya area, in Nyeri South District, a combination of social and natural science methods were used. These included field interviews, questionnaires, and focus group discussion, which were triangulated with analyses of soil, microclimate, and species richness. Our findings show an increase in tree density as farm sizes decrease. For instance, farms of <0.5ha have mean density of 187 trees/ha, while those >1ha have mean density of 51 trees/ha. At the same time, 78% of farmers report self sufficiency in fuelwood and timber, showing the subsistence value of trees. Farmers strongly prefer exotics also for timber and fruit for income. Species richness analysis revealed that fast growing exotic trees far outnumber indigenous. The cultural valuation of indigenous trees is simultaneously diminishing, potentially suppressed by economically-driven preferences. In addition, there is limited understanding of the potential ecological benefits of trees in farming systems. Multipurpose species, such as leguminous fodder trees, are underutilised, which may represent a missed opportunity for increased livelihood and farm system diversification. The gap in farmer knowledge, as well as a lack of support for best practices, are key barriers to agroforestry in the Othaya area. With better understanding of trees' effects on crops, livestock, climate, soil, and water, it could be expected that systems would be more productive and resilient, in effect contributing to sel