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Jing, Q., Bélanger, G., Baron, V., Bonesmo, H., & Virkajärvi, P. (2013). Simulating the Nutritive Value of Timothy Summer Regrowth. Agronomy Journal, 105(3), 563.
Abstract: The process-based grass model, CATIMO, simulates the spring growth and nutritive value of timothy (Phleum pratense L.), a forage species widely grown in Scandinavia and Canada, but the nutritive value of the summer regrowth has never been simulated. Our objective was to improve CATIMO for simulating the N concentration, neutral detergent fiber (NDF), in vitro digestibility of NDF (dNDF), and in vitro true digestibility of dry matter (IVTD) of summer regrowth. Daily changes in summer regrowth nutritive value were simulated by modifying key crop parameters that differed from spring growth. More specifically, the partitioning fraction to leaf blades was increased to increase the leaf-to-weight ratio, and daily changes in NDF and dNDF of leaf blades and stems were reduced. The modified CATIMO model was evaluated with data from four independent experiments in eastern and western Canada and Finland. The model performed better for eastern Canada than for the other locations, but the nutritive value attributes of the summer regrowth across locations (range of normalized RMSE = 8-25%, slope < 0.17, R-2 < 0.10) were not simulated as well as those of the spring growth (range of normalized RMSE = 4-16%, 0.85 < slope < 1.07, R-2 > 0.61). These modeling results highlight knowledge gaps in timothy summer regrowth and prospective research directions: improved knowledge of factors controlling the nutritive value of the timothy summer regrowth and experimental measurements of leaf-to-weight ratio and of the nutritive value of leaves and stems.
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Schils, R., Olesen, J. E., Kersebaum, K. - C., Rijk, B., Oberforster, M., Kalyada, V., et al. (2018). Cereal yield gaps across Europe. Europ. J. Agron., 101, 109–120.
Abstract: Europe 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.
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Cirillo, V., Masin, R., Maggio, A., & Zanin, G. (2018). Crop-weed interactions in saline environments. Europ. J. Agron., 99, 51–61.
Abstract: Soil salinization is one of the most critical environmental factors affecting crop yield. It is estimated that 20% of cultivated land and 33% of irrigated agricultural land are affected by salinity. In the last decades, considerable effort to manage saline agro-ecosystems has focused on 1) controlling soil salinity to minimize/reduce the accumulation of salts in the root zone and 2) improving plants ability to cope with osmotic and ionic stress. Less attention has been given to other components of the agro-ecosystem including weed populations, which also react and adapt to soil salinization and indirectly affect plant growth and yield. Weeds represent an increasing challenge for crop systems since they have high genetic resilience and adaptation ability to adverse environmental conditions such as soil salinization. In this review, we assess current knowledge on salinity tolerance of weeds in agricultural contexts and discuss critical components of crop-weed interactions that may increase weeds competitiveness under salinity. Compared to crop species, weeds generally exhibit greater salt tolerance due to high intraspecific variability, associated with diverse physiological adaptation mechanisms (e.g. phenotipic plasticity, seed heteromorphism, allelopathy). Weed competitiveness in saline soils may be enhanced by their earlier emergence, faster growth rates and synergies occurring between soil salts and allelochemicals released by weeds. In the future, a better understanding of crop-weed relationships and molecular, physiological and agronomic stress responses under salinity is essential to design efficient strategies to achieve weed control under altered climatic and environmental conditions.
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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., van Oijen, M., Constantin, J., et al. (2016). Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands. European Journal of Agronomy, 88, 41–52.
Abstract: For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data.
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Makowski, D. (2017). A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations. Europ. J. Agron., 88, 76–83.
Abstract: Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved.
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