Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling

This report provides an overview of how grasslands are represented in six different farmscale  models represented in MACSUR. A survey was conducted, followed by a workshop in  which modellers discussed the results of the survey, and identified research challenges and  knowledge gaps. The workshop was attended by grassland as well as livestock specialists.  The investigated models differed largely with respect to how grasslands were represented,  e.g. as regards weather and management factors accounted for, spatial and temporal  resolution, and output variables. All models had grassland modules that simulate DM yield  and herbage N content (or crude protein (CP) content = N content x 6.25). Many models  also simulate P content, whereas only one simulate K content. About half of the model  simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter  digestibility. Critical input data required from grassland models to simulate ruminant  productivity and GHG emissions at farm scale was identified by the workshop participants.  The different types of input data required were ranked in order of importance as regards  their influence on important system outputs. For simulation of ruminant productivity and  GHG emissions, herbage DM yield was ranked as the most important input variable from  grassland models, followed by CP content together with at least one variable describing  herbage fibre characteristics. These findings suggest that work on improving the ability of  the current grassland models with respect to simulation of fibre/energy should be  prioritized in farm-scale modelling aiming at quantifying livestock production and GHG  emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models

Sustainable agricultural intensification: indicators and metrics for multi-scale modeling.

Agricultural production is expected to provide food security, respect the environment, sustain rural communities and cover an increasing demand for the bioeconomy. In order to simultaneously address these objectives, sustainable agricultural intensification is seen as a promising strategy that could allow satisfying growing demands for agricultural food and non-food products, while reducing environmental impacts and maximizing resource use efficiency. However, the quantification and ex ante evaluation of sustainable intensification options and their associated trade-offs with respect to the various sustainability dimensions remain a challenge.This study aims to address this challenge by presenting a framework for measuring sustainable intensification. First, we reviewed literature on sustainability criteria for agriculture, biomass and bioenergy production, and metrics and frameworks for measuring sustainable intensification. Second, we developed a framework for quantifying sustainable intensification via transparent, clear and relevant indicators that allow the analysis and weighing of trade-offs across sustainable intensification options and scales. Third, we contrasted the metrics of the developed framework to typical outputs of a number of biophysical and economic models of agricultural systems, across different scales including the field, farm, regional, EU and global levels, in order to evaluate typical modeling capabilities to quantify sustainable intensification.This talk will present the findings of this exercise, demonstrate the operationalization of the framework for the assessment of the dual production of food and non-food products, and propose an approach for further improving the presented sustainable intensification metrics via stakeholder involvement

Online maps of Yield Gaps of cereals across Europe

The yield gap and water productivity analysis of key cereal crops in Europe is completed  and results are available through www.yieldgap.or

Needs on model improvement

The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models

Modelling nitrous oxide emissions of high input maize crop systems

Arable soils are a large source of nitrous oxide (N2O) emissions and several factors may affect the processes responsible of its production (nitrification and denitrification). In particular, forage crop systems for dairy farming are among the cropping systems with highest N input, mainly because they are based on high yielding forage grasses such as maize. A number of options have been explored to decrease the emissions but they remain site specific and are related to climatic, soil and local availability of management options. Moreover, guidelines for estimating N2O emission from agricultural soils does not take into account different crops, soils, climate and management, all of which are known to affect nitrification-denitrification and N2O production and emission.Process-based models represent a promising route to capture the spatial and temporal variability of N2O emissions, along with the effects of crop management. Nevertheless, the testing and comparison of these models have been limited to only a few works, with studies mainly based on biogeochemical models rather than process-based crop models. Furthermore, a multi-model ensemble analysis, which proved to be the best option for crop system analysis, has not been done extensively for the simulation of N2O emissions to addressing the various options for mitigations practices related to maize crop fertilization systems.Our objective is to evaluate the performances of several process-based models in simulating N2O emissions under different type, amount, rate of N fertilizer, i) quantify N2O emission, as a function of nitrogen inputs, across a wide range of soil types and environmental contexts; ii) assess the uncertainty in simulating N2O emissions, and iii) identify efficient mitigation of N-fertilized maize systems

Future climate change, yield variation, and impacts on farm management: a case study at a pilot regions in Finland

Crop production in northern regions such as in Finland is projected to benefit from longergrowing seasons brought by future climate change. However, production is also facing multiplechallenges under more frequent and extreme weather. More frequent drought stress, heat stressand other environment-related constraints may lead to higher yield variability in different regionsand increase the yield risks faced by farmers. Changes in yield potential and relative profitabilitybetween crops caused by climate change is likely to be different in different regions. The purposeof this paper is to develop a method to evaluate the impacts of adaptation and mitigation optionson farms with different socio-economic characteristics. Both socio-economic and biophysicalfactors affect rational decision-making process at a farm level and production decisions. Based onthe results from carefully chosen climate models under three SRES scenarios, together withdifferent market price scenarios, we attempt to identify how future changes in mean yields andyield variation caused by climate change in two regions in Finland may affect local farm landallocation and farming management practice. We study how management choices such as cropchoice, crop rotation, fertilization, crop protection and liming are affected and if these changesare in synergy or in conflict with mitigation. This study contributes to the development ofintegrated modelling methods needed to assess impacts of global changes on farming systems

The problem of series of days without rainfall in a view of efficiency of agricultural output under climate change

Modelling future is key issue in studying CC impacts on agriculture across disciplines and scales. Improving models basing on empirical data coming from diverse micro regions let obtain synergic effects important in shaping food security. Especially, rainfall distribution is most important factor determining agricultural output.The amount of cereal yield depends on an occurrence of long series of days without rain during a growing season. Based on statistical analysis of daily totals it was found that in Central Poland the length of series of days without rainfall during growing season is 40 days. Statistical analysis was done for years 1971-2015. The data allowed finding empirical probability distribution of a length of the series. Average value of the length of series is 4.31 while SD is 4.41. Values of parameters of gamma distribution estimated by the likelihood method are: α=0.9542, β=4.5150. Value of the parameter α (shape parameter) suggests that distribution of the length of series is similar to exponential distribution.Goodness of fit test with gamma distribution was carried out using λ-Kolmogorov and χ2-Pearson tests. Both prove high conformity between empirical and gamma distribution. Based on assumption that gamma distribution can be accepted as distribution of the length of rainless series, further is determined distribution of the length of the longest series in n-element random sample. On the theory of distributions of asymptotic order statistics it is known that the random variable T(n) with appropriate normalization has asymptotic double exponential distribution. Based on that one can conclude that probability to occur 30-day rainless series or longer equals approx. to 0.48. This is useful in forecasting agricultural output depended on rainfall distribution

Assessing the Importance of Accounting for the Impacts of Global Climate Change on Relative Competitiveness and International Trade in the Agricultural Sector

Climate change is expected to cause substantial changes in agricultural productivity across the globe. Because the impacts will differ between crops, production practices, and regions, there will be changes in the relative profitability of alternative land uses and effects on the relative competitiveness of production in different countries. However, many previous studies have focused on climate change impacts in one country or region without explicitly assessing the importance of impacts on the rest of the world in determining the net impacts on the focus country or region. Even when they include endogenous trade flows, domestic partial equilibrium models generally do not capture productivity changes in the rest of the world in detail and therefore do not adequately address impacts on relative competitiveness, international trade, and global markets and associated food security outcomes. In this study, we apply the GLObal BIOsphere Model (GLOBIOM), which is a detailed global partial equilibrium model of agriculture, forestry, and bioenergy to evaluate the relative contribution of direct climate change impacts on agriculture occurring within a country vs. those taking place in the rest of the world. We run a set of scenarios for multiple major agricultural regions comparing the outcomes when climate impacts are applied only to that region relative to applied to all regions of the world, using multiple climate scenarios and alternative assumptions regarding trade flexibility. This enables us to compare the relative importance of accounting for impacts outside the country of interest and the extent to which the relative impacts differ for developing vs. developed countries as well as for major commodity exporters vs. importers

Yield gaps of cereals across Europe.

To find proper compromises between land productivity, resource use efficiency and environmental impact, benchmarking of yields is a helpful starting point. Yield gaps are defined as the difference between potential or water-limited yield and actual yield. The GYGA project applies a consistent bottom-up approach to estimate yield gaps per country. Here we focus on the application for wheat, barley and maize in Europe. For each country, a climate zonation is overlaid with a crop area map. Within climate zones with important crop areas, weather stations are selected with at least 10 years of daily data. For the dominant soil types within a 100 km zone around the weather stations, the potential and water-limited yields are simulated with the WOFOST crop model, using location-specific knowledge on crop systems. Data from variety trials or other experiments, potential or water-limited yields, are used for validation and calibration of the model. Actual yields are taken from sub-national statistics. Yields and yield gaps are scaled up to climate zones and subsequently to countries. The average national simulated potential wheat yields under rainfed conditions varied from around 5 to 6 t/ha/year in the Mediterranean to nearly 12 t/ha/year on the British Isles and in the Low Countries. The average actual wheat yield varied from around 2 to 3 t/ha/year in the Mediterranean and some countries in East Europe to nearly 9 t/ha/year on the British Isles and in the Low Countries. The average relative yield gaps varied from around 10% to 30% in many countries in northwest Europe to around 50% to 70% in some countries in the Mediterranean and eastern Europe. For an initial understanding of yields and yield gaps, we assess differences between climate zones, soils and in relation to nitrogen input

Open data journal as a publishing and data sharing mechanism

This deliverable lays out the work as done as part of MACSUR CropM on data publishing, with the focus on improving data sharing and discovery and have shared data curation for future use. As part of the first phase MACSUR, The Open Data Journal for Agricultural Research (www.odjar.org) was started and documented in Deliverable C2.2 as part of Crop M. Odjar.org mainly focuses on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen URThis deliverable is a short update on the process of creating such a data journal by demonstrating a set of articles published through the journal, some of which are based on MACSUR results, as well as related networks. The deliverable does not further explain what the journal is, as this is part of the previous deliverable.