Climate service driven adaptation may alleviate the impacts of climate change in agriculture

Building a resilient and sustainable agricultural sector requires the development and implementation of tailored climate change adaptation strategies. By focusing on durum wheat (Triticum turgidum subsp. durum) in the Euro-Mediterranean region, we estimate the benefits of adapting through seasonal cultivar-selection supported by an idealised agro-climate service based on seasonal climate forecasts. The cost of inaction in terms of mean yield losses, in 2021–2040, ranges from −7.8% to −5.8% associated with a 7% to 12% increase in interannual variability. Supporting cultivar choices at local scale may alleviate these impacts and even turn them into gains, from 0.4% to 5.3%, as soon as the performance of the agro-climate service increases. However, adaptation advantages on mean yield may come with doubling the estimated increase in the interannual yield variability.info:eu-repo/semantics/publishedVersio

Modelling potential maize yield with climate and crop conditions around flowering

Abstract Understanding, and then modelling, the effects of sowing date and cultivar on maize yield is essential to develop appropriate climate change adaptation strategies. Here we test the WOFOST model and a hybrid model, based on physiological crop conditions around flowering, against observed data collected during 4 years of field experiments in a Mediterranean environment under fully irrigated conditions. We simulate sowing date and cultivar responses by using 45-year historical meteorological records from the experimental weather station and future climate conditions till 2060 as projected by a set of regional climate models. Both WOFOST and the hybrid approach reveal good performance in simulating average maize yield. However, the hybrid one outperforms WOFOST with respect to its responsiveness to changes in sowing date and cultivar. These findings, besides stressing the importance of crop conditions around flowering in determining maize yield, point to lower yields (14 %–17 %, average reduction) under future climate conditions. The estimated losses may only be partially offset by changes in phenology and sowing dates

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

JRC MARS Bulletin - Crop monitoring in Europe, November 2018

Harvesting of root and tuber crops also affected In large parts of central Europe, persistently dry soil conditions, complicated field preparations and sowing operations, and limited plant emergence and early crop development. Rapeseed areas in Germany, eastern Poland and northern Czechia are expected to be significantly reduced. Soft wheat can still be (re)sown in some countries. Favourable conditions for the sowing and emergence of winter crops prevailed in most parts of western and northern Europe.JRC.D.5-Food Securit

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

Cereal yield gaps across Europe

peer-reviewedEurope accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha−1 for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets.We received financial contributions from the strategic investment funds (IPOP) of Wageningen University & Research, Bill & Melinda Gates Foundation, MACSUR under EU FACCE-JPI which was funded through several national contributions, and TempAg (http://tempag.net/)

How do various maize crop models vary in their responses to climate change factors?

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information

Optimizing triticale sowing densities across the Mediterranean Basin

Triticale and wheat are similar crops, but triticale represents a valuable alternative to wheat due to its greater biomass production and grain yield in Mediterranean-type growing conditions. However, despite the higher yield potential and increasing importance of this crop, there are no dynamic crop models available to assist triticale adaptation via simulation experiments. In a previous study, the APSIM-Nwheat model was parameterized for triticale resulting in a new APSIM-Triticale model but the new model was never compared with detailed experimental triticale data in a one-to-one comparison. Here, the new model was tested with detailed field experimental observations. APSIM-Triticale was able to reproduce phenology, biomass, grain yields and soil water dynamics. The model performed well over several years and management options that included different sowing densities, sowing dates and a short and tall cultivar. The tested model was then used to explore management options to maximize triticale yield across the Mediterranean Basin. The response to sowing density was cultivar and rainfall-environment dependent. The simulation analysis indicated that there was no yield advantage with higher sowing densities with a tall cultivar type in high yielding environments, despite its higher biomass growth rates. The highest yields were achieved at the early sowing date at the sowing densities between 100 and 300 plants/m2 in the high rainfall regions for both short and the tall cultivars. The simulation study suggests that sowing a short cultivar with a reduced radiation use efficiency but early vigour growth could increase current yields across different regions, seasons and management options in the Mediterranean climate

Optimising sowing date of durum wheat in a variable Mediterranean environment

Sowing date and cultivar choice influence yield of wheat in Mediterranean climatic regions where crop production is constrained by waterlogging in winter on clay soils and terminal water deficit in spring. Because of the large seasonal variability in Mediterranean environments, a combination of experimental data and simulation results were used to investigate optimal sowing date and cultivar choice. The Agricultural Production Systems SIMulator (APSIM), which had been rigorously tested for bread wheat (Triticum aestivum L.) in Mediterranean-type environments, was further tested with measured durum wheat (Triticum turgidum L. var. durum) experimental data from the Mediterranean basin. The model reproduced most of the observed seasonal variability of grain yields but tended to overestimate, particularly some of the observed low grain yields. Comparing the model with detailed field experiments from two seasons and three durum wheat cultivars over a wide range of sowing dates highlighted the importance of reproducing the measured phenology and in particularly the observed anthesis dates for the general performance of the model. The use of a specific phyllochron for each sowing date instead of a single value per cultivar regardless of sowing date, as in APSIM, improved anthesis predictions and other aspects of the model.Hence, the phyllochron was varied in the model through a simple relationship based on observed phyllochrons and sowing dates. The new phyllochron routine was then used to explore management options to increase yields by combining the model with 47 years of historical weather records from Oristano, Sardinia, Italy. The simulation results showed that sowing wheat before December can result in higher yields in the absence of waterlogging. However, the high frequency of waterlogging on the clay soils, even with the observed decline of rainfall in the last 20 years in winter, showed no average yield advantage of sowing before December. Early maturing cultivars outperformed late cultivars at standard and late sowing dates. Hence, sowing early cultivars as soon as rainfall has started from December onwards is currently proposed to give the best yields in this environment in most seasons. Increasing temperatures and declining rainfalls in all months of the year as a consequence of future climate change projections will substantially reduce grain yields. Under such conditions, sowing as early as October to avoid terminal water shortage and heat stress will minimise the negative impact from climate change