Luca Doro

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment

Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ETN), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ETN may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy.

C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

Una valutazione integrata degli impatti produttivi ed economici del cambiamento della variabilità climatica in un’area mediterranea irrigua

Una valutazione integrata degli impatti produttivi ed economici del cambiamento della variabilita climatica in un’area mediterranea irrigua Quest’articolo analizza l’effetto dei cambiamenti climatici su temperature, piovosita, esigenze irrigue e disponibilita idrica di un’area mediterranea, integrando relazioni bio-fisiche ed economiche. L’analisi della distribuzione di probabilita (Pdf) delle variabili climatiche mostra che nel futuro prossimo aumentera la probabilita di avere temperature e fabbisogni irrigui alti e disponibilita idrica bassa. Un modello economico simula le scelte delle imprese agricole data l’incertezza rappresentata dalla variabilita delle Pdf. Le colture soffrono soprattutto per la minore disponibilita idrica, che si puo contrastare migliorando le infrastrutture irrigue collettive. La produzione del latte bovino patisce l’aumento delle temperature, che si puo mitigare con sistemi di raffrescamento.

Assessing the economic and productive impact of climate change in a Mediterranean irrigated agricultural area subject to water shortage

Climate changes in agriculture act on various climate variables (precipitation, temperature etc.) at different times of crop cycles. Many physical and technical relationships have to be represented even when analyzing a limited aspect of farm management. This work employs the net evapotranspiration (ETN) estimated with the EPIC model, as a synthetic index of the physical factors that the farmer considers in decisions on irrigation. The probability distribution of ETN is inserted into a territorial model of DSP that represents farm choices in conditions of uncertainty about water availability and irrigation requirements of crops. Recent trends of ETN suggest that the probability distribution of this variable may appreciably change in the near future. Also, water availabil- ity may become more variable due to changed rainfall. These modifications amplify uncertainty of management and, consequently, costs incurred by the farm typologies of the study area, which in many cases suffer an appreciable drop in income.

An evaluation of the economic impact of climate change by a 3-stage discrete stochastic programming model

Climate changes in agriculture act on various climate variables (precipitation, temperature etc.) and at different times of crop cycles. This paper provides an evaluation of the economic impact of changes in multiple events and the associated uncertainty in an area of Sardinia, through a multidisciplinary approach, namely economic and agronomic. The economic approach uses a three stages Discrete Stochastic Programming model, while the irrigation requirements of some major crops for the agricultural economy of the area uses the EPIC agronomic model. The results show that the whole agricultural area adapt itself with a low cost, changing crops or cultural practices. This cost, however, is higher for certain types of farms that suffer a significant income reduction.