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  Subjects -> AGRICULTURE (Total: 785 journals)
    - AGRICULTURAL ECONOMICS (69 journals)
    - AGRICULTURE (550 journals)
    - CROP PRODUCTION AND SOIL (92 journals)
    - DAIRYING AND DAIRY PRODUCTS (28 journals)
    - POULTRY AND LIVESTOCK (46 journals)

AGRICULTURE (550 journals)

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Journal Cover European Journal of Agronomy
  [SJR: 1.488]   [H-I: 75]   [10 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1161-0301
   Published by Elsevier Homepage  [3043 journals]
  • Diverse dynamics in agroecological transitions on fruit tree farms
    • Abstract: Publication date: October 2017
      Source:European Journal of Agronomy, Volume 90
      Author(s): Marie Dupré, Thierry Michels, Pierre-Yves Le Gal
      Agroecological transition refers to the adoption by farmers of practices based on on-farm biological processes rather than imported or non-renewable inputs. Drawing from a comprehensive survey of 31 diversified farms cultivating citrus on Réunion Island (Indian Ocean, France), this study aims to understand the diverse dynamics in farmers’ agroecological transitions and to identify the factors and processes driving farmers’ choices. The analysis considers both the current protection, fertilization and weed control practices implemented by farmers in their orchards and the trajectories of change they have followed over the last thirty years. Orchard management was categorized according to the kind of inputs mobilized (i.e., “synthetic inputs”, “alternative off-farm inputs” and “alternative on-farm inputs”). Diverse managements were observed, targeting security, autonomy, ecology or simplicity. The six types of practice trajectories identified illustrate the diverse and incremental nature of agroecological transition. Drawing from these results, drivers of alternative practice adoption and lock-in effects in synthetic input reliance were characterized. Internal drivers, depending directly on the farmer and his/her farm, included the characteristics of the orchard and its environment, the labor force, and the farmer’s environmental concerns. External drivers included local citrus markets, public legislation, access to extension services, the organization of input supply and the social environment. The combination of these internal and external drivers at the farm level makes each farm relatively unique. However, three factors determine the main differences in practices: the marketing channel used, the farmer’s environmental objectives, and the farmer’s economic behavior, which is linked to the weight of the crop activity in farm revenue. Understanding farmers’ points of view and decisions regarding agroecological transition deserves the attention of scientists, agricultural advisors and policy makers when designing innovative cropping systems, new support methodologies and incentives to respond effectively to farmers’ objectives and contexts of action.

      PubDate: 2017-08-03T09:20:07Z
       
  • Understanding effects of multiple farm management practices on barley
           performance
    • Abstract: Publication date: October 2017
      Source:European Journal of Agronomy, Volume 90
      Author(s): Libère Nkurunziza, Iman Raj Chongtham, Christine A. Watson, Håkan Marstorp, Ingrid Öborn, Göran Bergkvist, Jan Bengtsson
      Because of the complexity of farming systems, the combined effects of farm management practices on nitrogen availability, nitrogen uptake by the crop and crop performance are not well understood. To evaluate the effects of the temporal and spatial variability of management practices, we used data from seventeen farms and projections to latent structures analysis (PLS) to examine the contribution of 11 farm characteristics and 18 field management practices on barley performance during the period 2009–2012. Farm types were mixed (crop-livestock) and arable and were categorized as old organic, young organic or conventional farms. The barley performance indicators included nitrogen concentrations in biomass (in grain and whole biomass) and dry matter at two growing stages. Fourteen out of 29 farm characteristics and field management practices analysed best explained the variation of the barley performance indicators, at the level of 56%, while model cross-validation revealed a goodness of prediction of 31%. Greater crop diversification on farm, e.g., a high proportion of rotational leys and pasture, which was mostly observed among old organic farms, positively affected grain nitrogen concentration. The highest average grain nitrogen concentration was found in old organic farms (2.3% vs. 1.7 and 1.4% for conventional and young organic farms, respectively). The total nitrogen translocated in grain was highest among conventional farms (80kgha−1 vs. 33 and 39kgha−1 for young and old organic farms, respectively). The use of mineral fertilizers and pesticides increased biomass leading to significant differences in average grain yield which became more than double for conventional farms (477±24gm−2) compared to organic farms (223±37 and 196±32gm−2 for young and old organic farms, respectively). In addition to the importance of weed control, management of crop residues and the organic fertilizer application methods in the current and three previous years, were identified as important factors affecting the barley performance indicators that need closer investigation. With the PLS approach, we were able to highlight the management practices most relevant to barley performance in different farm types. The use of mineral fertilizers and pesticides on conventional farms was related to high cereal crop biomass. Organic management practices in old organic farms increased barley N concentration but there is a need for improved management practices to increase biomass production and grain yield. Weed control, inclusion of more leys in rotation and organic fertilizer application techniques are some of the examples of management practices to be improved for higher N concentrations and biomass yields on organic farms.

      PubDate: 2017-08-03T09:20:07Z
       
  • Climate change effects on leaf rust of wheat: Implementing a coupled
           crop-disease model in a French regional application
    • Abstract: Publication date: October 2017
      Source:European Journal of Agronomy, Volume 90
      Author(s): Julie Caubel, Marie Launay, Dominique Ripoche, David Gouache, Samuel Buis, Frédéric Huard, Laurent Huber, François Brun, Marie Odile Bancal
      Leaf rust is responsible for significant wheat yield losses. Its occurrence and severity have increased in recent years, partly because of warmer climate. It is therefore critical to understand and anticipate the effects of climate change on leaf rust. Direct climate effects and indirect effects via host plants that provide a biophysical environment for disease development were both considered. The coupled STICS-MILA model simulates both crop and pathogen dynamics in a mechanistic way and their interaction is managed by two sub-models: one calculating the microclimate within the canopy and the other converting numbers of spores and lesions to affected surfaces. In this study, STICS-MILA was first calibrated and evaluated using leaf rust severity observed at various sites in France for multiple years. STICS-MILA was then run on three contrasting French sites under 2.6, 4.5 and 8.5 RCP future climate scenarios. Results focused firstly on changes in disease earliness and intensity, secondly on disease dynamics, particularly the synchronism between plant and disease developments, and finally on elementary epidemic processes. The calibration and evaluation of STICS-MILA revealed a high sensitivity to the initial amount of primary inoculum (a forcing variable in STICS-MILA) and thus the need to properly simulate the summering and overwintering pathogen survival. The simulations in the context of future climate showed a significant change in host-pathogen synchronism: in the far future, according to RCP 4.5 and 8.5 scenarios, disease onset is expected to occur not only with an advance of around one month but also at an earlier developmental stage of wheat crops. This positive effect results from rising temperatures, nevertheless partly counter-balanced during spring by lower wetness frequency. The crop growth accelerates during juvenile stages, providing a greater support for disease development. The resulting microclimate shortens latency periods and increases infection and sporulation efficiencies, thus causing more infectious cycles. An increase of final disease severity is thus forecasted with climate change.

      PubDate: 2017-08-03T09:20:07Z
       
  • Dynamics of oil and tocopherol accumulation in sunflower grains and its
           impact on final oil quality
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): R. González Belo, S. Nolasco, C. Mateo, N. Izquierdo
      Tocopherols are one of the most important bioactive compounds in vegetable oils. It is known that these antioxidants present a dilution like relationship with oil weight per grain but the mechanism underlying this relationship are unknown. The aim of this work was to analyze the dynamics of tocopherol accumulation in sunflower grains, its relationship with oil accumulation and its effects on final oil quality in genotypes with different fatty acid composition. Three field experiments were conducted with genotypes with different potential fatty acid composition (a traditional, a high oleic and a high stearic−high oleic) and treatments with different source (intercepted solar radiation) or sink (grains) during grain filling to obtain varied grain filling conditions and grains with different oil concentration and oil unsaturation. Intercepted solar radiation modified oil per grain but did not affect tocopherol per grain. The rate of accumulation explained 79% and 74% of the oil and tocopherol per grain variation, respectively. When intercepted solar radiation increased, the duration of the period of oil and tocopherols accumulation increased, being the first the most responsive. These differences in the duration of accumulation periods are reflected in a larger relative increase in oil than tocopherols per grain and thus a dilution of the latter in the oil. These differences in the dynamics of oil and tocopherol accumulation are common to genotypes with different level of unsaturation. These results help to understand the mechanism associated with the dilution curve of oil tocopherol concentration reported in the literature.

      PubDate: 2017-08-03T09:20:07Z
       
  • Modelling fertiliser significance in three major crops
    • Abstract: Publication date: October 2017
      Source:European Journal of Agronomy, Volume 90
      Author(s): Ben Parkes, Benjamin Sultan, Philippe Ciais, Xuhui Wang
      We present work using two long-term climate datasets to show that nitrogen fertiliser is an important aspect of yield projection for three major crops. The ability of linear models using climate variables as predictors to accurately project the yield of maize, rice and wheat over multi-decadal scales is improved with the addition of fertiliser as an input. Highly productive nations including Argentina, India, Poland and South Africa show significant improvement in yield simulations and show that fertiliser use should not be discounted when estimating yield variability. The use of nitrogen fertiliser in the generalised linear models improves yield forecast by 18% using the Princeton climate dataset and 23% using the WFDEI climate dataset. This work therefore supports the use of additional predictors than climate for improving the ability of statistical models to reconstruct yield variability.

      PubDate: 2017-07-24T09:09:46Z
       
  • Productivity of organic and conventional arable cropping systems in
           long-term experiments in Denmark
    • Abstract: Publication date: October 2017
      Source:European Journal of Agronomy, Volume 90
      Author(s): Ambreen Shah, Margrethe Askegaard, Ilse A. Rasmussen, Eva Maria Cordoba Jimenez, Jørgen E. Olesen
      A field experiment comparing different arable crop rotations was conducted in Denmark during 1997–2008 on three sites varying in climatic conditions and soil types, i.e. coarse sand (Jyndevand), loamy sand (Foulum), and sandy loam (Flakkebjerg). The crop rotations followed organic farm management, and from 2005 also conventional management was included for comparison. Three experimental factors were included in the experiment in a factorial design: 1) crop rotation (organic crop rotations varying in use of whole-year green manure (O1 and O2 with a whole-year green manure, and O4 without), and a conventional system without green manure (C4)), 2) catch crop (with and without), and 3) manure (with and without). The experiment consisted of three consecutive cycles using four-course rotations with all crops present every year, i.e. 1997–2000 (1st cycle), 2001–2004 (2nd cycle), and 2005–2008 (3rd cycle). In the 3rd cycle at all locations C4 was compared with two organic rotations, i.e. O2 and O4. The O2 rotation in the third cycle included spring barley, grass-clover, potato, and winter wheat, whereas C4 and O4 included spring barley, faba bean, potato, and winter wheat. For the O2 rotation with green manure there was a tendency for increased DM yield over time at all sites, whereas little response was seen in N yield. In the O4 rotation DM and N yields tended to increase at Foulum over time, but there was little change at Flakkebjerg. The DM yield gap between organic and conventional systems in the 3rd cycle varied between sites with 34–66% at Jyndevad, 21–44% at Foulum, and 32–52% at Flakkebjerg. The inclusion of grass-clover resulted in lower cumulated yield over the rotation than the treatment without grass-clover. The use of manure reduced the DM yield gap between conventional and organic systems on an average by 15 and 21%-points in systems with and without grass-clover, respectively, and the use of catch crops reduced the yield gap by 3 and 5%-points in the respective systems. Across all crops the agronomic efficiency of N in manure (yield benefit for each kg of mineral N applied) was greater in O4 compared with O2 for all crops.

      PubDate: 2017-07-24T09:09:46Z
       
  • Cassava yield loss in farmer fields was mainly caused by low soil
           fertility and suboptimal management practices in two provinces of the
           Democratic Republic of Congo
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): K. Kintché, S. Hauser, N.M. Mahungu, A. Ndonda, S. Lukombo, N. Nhamo, V.N.E. Uzokwe, M. Yomeni, J. Ngamitshara, B. Ekoko, M. Mbala, C. Akem, P. Pypers, K.P. Matungulu, A. Kehbila, B. Vanlauwe
      A better understanding of the factors that contribute to low cassava yields in farmers’ fields is required to guide the formulation of cassava intensification programs. Using a boundary line approach, we analysed the contribution of soil fertility, pest and disease infestation and farmers’ cultivation practices to the cassava yield gap in Kongo Central (KC) and Tshopo (TSH) provinces of the Democratic Republic of Congo. Data were obtained by monitoring 42 and 37 farmer-managed cassava fields during two cropping cycles in KC and one cropping cycle in TSH, respectively. Each field was visited three times over the cassava growing period for the observations. Logistic model was fitted against the observed maximum cassava root yields and used to calculate the achievable yield per field and for individual factor. At field level, the factor that led to the lowest achievable yield (Yup(i)1) was considered as the dominant yield constraint. Cassava yield loss per field was expressed as the increase in the maximal root yield observed per province (Yatt- attainable yield) compared to Yup(i)1. Yatt was 21 and 24tha−1 in TSH and KC, respectively. With the cassava varieties that farmers are growing in the study areas, pests and diseases played a sparse role in the yield losses. Cassava mosaic was the only visible disease we observed and it was the dominant yield constraint in 3% and 12% of the fields in KC and TSH, respectively. The frequent yield constraints were suboptimal field management and low soil fertility. Cultivation practices and soil parameters led to Yup(i)1 in 47% and 50% of the fields in KC, and in 47% and 41% of those in TSH, respectively. Individual soil parameters were the yield constraint in few fields, suggesting that large-scale programs in terms of lime application or recommendation of the blanket fertilisers would result in sparse efficacy. In KC, yield losses caused by low soil fertility averaged 6.2tha−1 and were higher than those caused by suboptimal field management (5.5tha−1); almost nil for cassava mosaic disease (CMD). In TSH, yield losses caused by low soil fertility (4.5tha−1) were lower than those caused by suboptimal field management (6.5tha−1) and CMD (6.1tha−1). Irrespective of the constraint type, yield loss per field was up to 48% and 64% of the Yatt in KC and TSH, respectively. Scenario analysis indicated that the yield losses would remain at about two third of these levels while the dominant constraint was only overcome. We concluded that integrated and site-specific management practices are needed to close the cassava yield gap and maximize the efficacy of cassava intensification programs.

      PubDate: 2017-07-11T23:44:15Z
       
  • Does long-term plastic film mulching really decrease sequestration of
           organic carbon in soil in the Loess Plateau'
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): Feng Zhang, Wenjuan Zhang, Ming Li, Yongshun Yang, Feng-Min Li
      Plastic film mulching (PM) is used extensively in China to increase the productivity of food crops, especially in the arid and semi-arid regions, although a recent concern is whether the practice decreases soil organic carbon (SOC). A process-based biogeochemical model applied in the Loess Plateau, where PM is widespread, examined the status of SOC over 30 years of maize cultivation with PM. The model explained 96% of the observed variance in SOC. The root mean square error was 0.39gkg−1, the mean absolute error was 0.32gkg−1, and the bias value from the SOC simulation was −0.006gkg−1. The model’s projections showed that PM has no significant impact on the overall average content of SOC across the entire study area compared to the fields without mulching, or control (CK), assuming that 5% of the crop residue was ploughed back into soil. However, based on individual simulation points in the 0–50cm soil profile after 30 years of PM, 59.29% of the points, mainly on the western parts of the Loess Plateau, showed significantly higher SOC than that in CK, 8.30% showed significantly lower, and 32.41%, mainly on the south-eastern part of the Plateau, showed no significant difference between sites with PM and without it. Mulching increased the biomass, rhizodeposition, and the speed of turnover of SOC significantly, compared to the corresponding values in CK. High biomass in PM led to more carbon (C) being retained in soil and lowered the depletion of SOC. Ploughing the crop residues back into soil increased SOC, the greater the percentage of residue thus returned to soil, the higher the SOC. For a given residue return percentage, PM could increase more SOC than the CK. At 15%, which is the current average residue return ratio in the Loess Plateau, SOC levels remained stable or even increased in 77% of the study area if it adopted PM; in CK, the corresponding figure was only 63%. In regions such as the Loess Plateau where biomass is in great demand as a fuel or animal feed, PM provides greater biomass, which also means that more of it is available to be returned to soil—PM thus promotes the sequestration of SOC.

      PubDate: 2017-07-02T23:08:03Z
       
  • Within-field variations in sugar beet yield and quality and their
           correlation with environmental variables in the East of England
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): Salar A. Mahmood, Alistair J. Murdoch
      Spatial variability of sugar beet yield and quality within fields and their correlation with environmental variables was investigated in order to explore the potential for more precise agronomy. In three uniformly-managed, commercial sugar beet fields in the east of England spatial variation in the commercial value of the sugar yield ranged from £232 to £3320 per hectare. This variation was not random; there were high and low yielding patches in each field. Sugar beet root yield was positively correlated with the spatial distribution of crop plant population, soil organic matter and soil moisture, but negatively with weed density and canopy temperature. Correlations of sugar beet yield with soil type, elevation and soil available phosphate, potassium and magnesium were, however, inconsistent between the three fields and over two seasons. With respect to sugar beet quality, spatial variation in the amino acid and potassium concentrations in the sugar beet roots was associated with soil type and elevation, whereas sugar percentage varied randomly in two of the fields. Interventions and research that could help to optimize yield on a spatially-variable basis are discussed.
      Graphical abstract image

      PubDate: 2017-07-02T23:08:03Z
       
  • Estimating uncertainty in crop model predictions: Current situation and
           future prospects
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Daniel Wallach, Peter J. Thorburn
      In this introductory paper to the special issue on crop model prediction uncertainty, we present and compare the methodological choices in the studies included in this issue, and highlight some remaining challenges. As a common framework for all studies, we define prediction uncertainty as the distribution of prediction error, which can be written as the sum of a bias plus a predictor uncertainty term that represents the random variation due to uncertainty in model structure, model parameters or model inputs. Several themes recur in many of the studies: Use of multi-model ensembles (MMEs) to quantify model structural uncertainty; Emphasis on uncertainty in those inputs related to prediction of regional results or climate change impact assessment; Simultaneous consideration of multiple sources of uncertainty; Emphasis on exploring the variability of uncertainty over space or time; Use of sensitivity analysis techniques to disaggregate the separate contributions to prediction uncertainty. Relatively new approaches include the estimation of both the bias and predictor uncertainty terms of prediction error, the construction of MMEs specifically designed to explore the uncertainty in model structure, the use of emulators for sensitivity analysis and the exploration of ways to reduce prediction uncertainty other than through model improvement. Major remaining challenges are standardization of approaches to quantifying uncertainty in model structure, parameters and inputs, going beyond studies of specific sources of uncertainty to estimation of overall prediction uncertainty, comparing and combining validation and uncertainty studies, and evaluation of uncertainty estimates. Looking forward, we suggest that assessment of prediction uncertainty should be a standard part of any modelling project. The studies here will contribute toward that goal.

      PubDate: 2017-07-02T23:08:03Z
       
  • Implications of climate model biases and downscaling on crop model
           simulated climate change impacts
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): D. Cammarano, M. Rivington, K.B. Matthews, D.G. Miller, G. Bellocchi
      In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50×50km) and bias corrected downscaled (site-specific) hindcast (1960–1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles.

      PubDate: 2017-07-02T23:08:03Z
       
  • A simple Bayesian method for adjusting ensemble of crop model outputs to
           yield observations
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): David Makowski
      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.

      PubDate: 2017-07-02T23:08:03Z
       
  • Assessing uncertainty and complexity in regional-scale crop model
           simulations
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Julian Ramirez-Villegas, Ann-Kristin Koehler, Andrew J. Challinor
      Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5–10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources.

      PubDate: 2017-07-02T23:08:03Z
       
  • A global sensitivity analysis of cultivar trait parameters in a sugarcane
           growth model for contrasting production environments in Queensland,
           Australia
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): J. Sexton, Y.L. Everingham, G. Inman-Bamber
      New sugarcane cultivars are continuously developed to improve sugar industry productivity. Despite this sugarcane crop models such as the ‘Sugar’ module in the Agricultural Productions System sIMulator (APSIM-Sugar) have not been updated to reflect the most recent cultivars. The implications of misrepresenting cultivar parameters in APSIM-Sugar is difficult to judge as little research has been published on the likely values of these parameters and how uncertainty in parameter values may affect model outputs. A global sensitivity analysis can be used to better understand how cultivar parameters influence simulated yields. A Gaussian emulator was used to perform a global sensitivity analysis on simulated biomass and sucrose yield at harvest for two contrasting sugarcane-growing regions in Queensland, Australia. Biomass and sucrose yields were simulated for 42 years to identify inter-annual variability in output sensitivities to 10 parameters that represent physiological traits and can be used to simulated differences between sugarcane cultivars. Parameter main effect (Si) and total effect (STi) sensitivity indices and emulator accuracy were calculated for all year-region-output combinations. When both regions were considered together parameters representing radiation use efficiency (rue), number of green leaves (green_leaf_no) and a conductance surrogate parameter (kL ) were the most influential parameters for simulated biomass in APSIM-Sugar. Simulated sucrose yield was most sensitive to rue, sucrose_fraction (representing the fraction of biomass partitioned as sucrose in the stem) and green_leaf_no. However, climate and soil differences between regions changed the level of influence cultivar parameters had on simulation outputs. Specifically, model outputs were more sensitive to changes in the transp_eff_cf and kL parameters in the Burdekin region due to lower rainfall and poor simulated soil conditions. Collecting data on influential traits that are relatively simple to measure (e.g. number of green leaves) during cultivar development would greatly contribute to the simulation of new cultivars in crop models. Influential parameters that are difficult to measure directly such as transp_eff_cf and sucrose_fraction are ideal candidates for statistical calibration. Calibrating crop models either through direct observation or statistical calibration would allow crop modellers to better test how new cultivars will perform in a range of production environments.

      PubDate: 2017-07-02T23:08:03Z
       
  • The interactions between genotype, management and environment in regional
           crop modelling
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Edmar I. Teixeira, Gang Zhao, John de Ruiter, Hamish Brown, Anne-Gaelle Ausseil, Esther Meenken, Frank Ewert
      Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e.M) to the variability in biomass yield (YT, total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25G x M scenarios were simulated at a 5 arc minute resolution (∼5km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of YT and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (Si), showed distinct spatial patterns for the two output variables. The YT was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of Si to environmental drivers. For example, the sensitivity of YT and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest.

      PubDate: 2017-07-02T23:08:03Z
       
  • Improved persistence of red clover (Trifolium pratense L.) increases the
           
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): A.H. Marshall, R.P. Collins, J. Vale, M. Lowe
      UK livestock agriculture can significantly reduce its protein imports by increasing the amount of forage based protein grown on-farm. Forage legumes such as red clover (Trifolium pratense L.) produce high dry matter yields of quality forage but currently available varieties lack persistence, particularly under grazing. To assess the impact of red clover persistence on protein yield, diploid red clover populations selected for improved persistence were compared with a range of commercially available varieties. All populations were grown over four harvest years in mixed swards with either perennial ryegrass (Lolium perenne L.) or perennial plus hybrid ryegrass (L. boucheanum Kunth). Red clover and total sward dry matter (DM) herbage yields were measured in Years 1–4, red clover plant survival in Years 3 and 4 and herbage protein (CP) yield and concentration in Years 2 and 4. In general, red clover DM yield in year 4 (3.4tha−1) was lower than in year 1 (13.9tha−1) but the red clover populations differed in the extent of this decline. Differences in the persistence of the red clover populations in terms of plant survival and yield were reflected in the contribution of red clover to the total sward yield in Year 4, which ranged from 61% for the highest yielding population, AberClaret, to 11% in the lowest yielding, Vivi. Increased red clover DM yield was reflected in a greater CP yield (protein weight per unit area), which ranged from 1.6tha−1 year−1 to 2.9tha−1 year−1 in Year 2 and from 1.1tha−1 year−1 to 1.9tha−1 year−1 in Year 4. CP concentration (protein weight per unit herbage weight) of all of the red clover populations was within a range considered suitable for ruminant production. The implication of these results for the future use of red clover in sustainable grassland systems is discussed.

      PubDate: 2017-06-22T09:17:34Z
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87


      PubDate: 2017-06-22T09:17:34Z
       
  • Early nitrogen deficiencies favor high yield, grain protein content and N
           use efficiency in wheat
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): Clémence Ravier, Jean-Marc Meynard, Jean-Pierre Cohan, Philippe Gate, Marie-Hélène Jeuffroy
      Nitrogen fertilization has been widely studied in wheat (Triticum aestivum L.), with a view to maximizing local yields and obtaining high grain protein contents. It has long been accepted that nitrogen nutrition must be non-limiting throughout the crop cycle for these targets to be reached. However, studies over the last 20 years have shown that some periods of N deficiency are detrimental, whereas others have no impact on grain yield. There is, therefore, still a need to define the precise N deficiency path that can be tolerated. Nitrogen nutrition index (NNI) is an appropriate indicator of N deficiency. Based on experiments with wheat crops showing various patterns of NNI dynamics from the start of stem elongation to flowering, we aimed to identify a minimum nitrogen nutrition path, including periods of N deficiency, defining the threshold above which there is no detrimental impact on wheat crop yield. We used experimental data from 18 site-year experiments, each including 1–14 cultivars and 2–8 fertilization strategies, with determinations of crop NNI at four growth stages (Z30, Z32, Z39 and Z60 on the Zadoks scale). Experimental treatments were assigned to two groups: those with and without yield loss due to N fertilization strategy, relative to the maximum yield in each trial. Using receiver operating characteristics curve analysis, we identified the NNI path best distinguishing between the two groups of treatments. We found that the lowest acceptable NNI value (i.e. the lowest value for which there was no yield loss), increased during the crop cycle. We characterized, for the cultivars studied, periods of N deficiency during vegetative growth that did not lead to a decrease in yield or grain protein content, and even some periods in which the deficiency improved nitrogen use efficiency. Finally, we concluded that references in NNI should be revised for more efficient N management and the threshold NNI path could be used to determine timing of N fertilizer application on the basis of real-time crop N status monitoring.

      PubDate: 2017-06-16T07:31:30Z
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88


      PubDate: 2017-06-12T14:36:38Z
       
  • Quantifying model-structure- and parameter-driven uncertainties in spring
           wheat phenology prediction with Bayesian analysis
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Phillip D. Alderman, Bryan Stanfill
      Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relative contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. This study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.

      PubDate: 2017-06-12T14:36:38Z
       
  • Spatial and temporal uncertainty of crop yield aggregations
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Vera Porwollik, Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Toshichika Iizumi, Deepak K. Ray, Alex C. Ruane, Almut Arneth, Juraj Balkovič, Philippe Ciais, Delphine Deryng, Christian Folberth, Roberto C. Izaurralde, Curtis D. Jones, Nikolay Khabarov, Peter J. Lawrence, Wenfeng Liu, Thomas A.M. Pugh, Ashwan Reddy, Gen Sakurai, Erwin Schmid, Xuhui Wang, Allard de Wit, Xiuchen Wu
      The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r=0.28). For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r=0.56 (maize, India), r=0.05 (wheat, Russia), r=0.13 (rice, Vietnam), and r=−0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

      PubDate: 2017-06-12T14:36:38Z
       
  • Multi-model simulation of soil temperature, soil water content and biomass
           in Euro-Mediterranean grasslands: Uncertainties and ensemble performance
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): R. Sándor, Z. Barcza, M. Acutis, L. Doro, D. Hidy, M. Köchy, J. Minet, E. Lellei-Kovács, S. Ma, A. Perego, S. Rolinski, F. Ruget, M. Sanna, G. Seddaiu, L. Wu, G. Bellocchi
      This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R2 >0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R2 <0.6 with individual models and <∼0.5 with MMM) and biomass (R2 <∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5°C for ST, −0.3m3 m−3 for SWC and 360gDMm−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.

      PubDate: 2017-06-12T14:36:38Z
       
  • Impact analysis of climate data aggregation at different spatial scales on
           simulated net primary productivity for croplands
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Matthias Kuhnert, Jagadeesh Yeluripati, Pete Smith, Holger Hoffmann, Marcel van Oijen, Julie Constantin, Elsa Coucheney, Rene Dechow, Henrik Eckersten, Thomas Gaiser, Balász Grosz, Edwin Haas, Kurt-Christian Kersebaum, Ralf Kiese, Steffen Klatt, Elisabet Lewan, Claas Nendel, Helene Raynal, Carmen Sosa, Xenia Specka, Edmar Teixeira, Enli Wang, Lutz Weihermüller, Gang Zhao, Zhigan Zhao, Stephen Ogle, Frank Ewert
      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 (∼34,000km2), 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 1–100km 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 (1.9–4.2gCm−2 d−1 and 2.7–6.1gCm−2 d−1 for 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 aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

      PubDate: 2017-06-12T14:36:38Z
       
  • Accounting for both parameter and model structure uncertainty in crop
           model predictions of phenology: A case study on rice
    • Abstract: Publication date: August 2017
      Source:European Journal of Agronomy, Volume 88
      Author(s): Daniel Wallach, Sarath P. Nissanka, Asha S. Karunaratne, W.M.W. Weerakoon, Peter J. Thorburn, Kenneth J. Boote, James W. Jones
      We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2°C, is estimated as 108days2, with model error contributing 86days2, followed by model structure uncertainty which contributes 15days2 and parameter uncertainty which contributes 7days2. We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures.

      PubDate: 2017-06-12T14:36:38Z
       
  • Improved evaluation of field experiments by accounting for inherent soil
           variability
    • Abstract: Publication date: September 2017
      Source:European Journal of Agronomy, Volume 89
      Author(s): K. Heil, U. Schmidhalter
      Well-controlled field experiments are used to test agronomic management practices and evaluate the performance of cultivars in highly managed plots at experimental stations, in breeding nurseries or on-farm. However, the performance of crops and therefore the interpretation of experiments is affected by the inherent soil variability. To avoid large residual errors, replicate measurements or optimized designs are usually helpful but seldom adequately consider the unknown soil variability. The use of spatial covariates, such as proximally sensed data, in the statistical modelling of the target variable may provide a better estimate of such experimental residual variations (errors). Therefore, the purpose of this study was to determine whether the apparent soil electrical conductivity, topographical parameters and location information (expressed as Gauß-Krüger coordinates) could be used for an enhanced spatial and temporal characterization of the long-term and annual wheat yields within a static, long-term nitrogen fertilizer experiment that included six different forms of nitrogen and three levels of nitrogen fertilizer. Furthermore, this investigation aimed to propose statistical strategies for analysing this background variation by testing ANOVA (Analysis of variance) and ANCOVA (Analysis of covariance). ANCOVA with soil ECa, location information and topographic parameters as covariates improved the accuracy of the yield estimates of the multi-annual means for all treatments. Without these independent variables in ANOVA, the coefficient of determination (R2) was smaller and the root mean square difference (RMSD) was larger than those of ANCOVA (fertilized plots ANOVA: R2 =0.19, RMSD=3.26 dt ha−1; ANCOVA: R2 =0.87, RMSD=1.29 dt ha−1). In addition to the factor level of fertilization and form of nitrogen fertilizer, ECa was the dominant covariate for the averaged long-term and annual yields. The ECa was measured with different sensors and configurations and represented a significant independent variable. Of the topographic relief parameters, the predictor plancurvature was the dominant independent variable. The inclusion of plot-wise, time-invariant soil and relief parameters significantly improved the discrimination of testing the treatment performance within the long-term field trial. A further application of this approach to other experimental sites and breeding nurseries would likely be highly rewarding.

      PubDate: 2017-06-06T19:02:08Z
       
  • Modelling phenological and agronomic adaptation options for narrow-leafed
           lupins in the southern grainbelt of Western Australia
    • Abstract: Publication date: Available online 1 June 2017
      Source:European Journal of Agronomy
      Author(s): Chao Chen, Andrew Fletcher, Roger Lawes, Jens Berger, Michael Robertson
      Australian modern narrow-leafed lupin (Lupinus angustifolius L.) cultivars tend to flower early and are vernalisation-unresponsive (VU). Cultivars have generally been selected for the warmer climates zones and sandy soils of the northern grain belt of Western Australia (NWA), where lupins are predominantly grown. In areas where climates are cooler and growing seasons are longer and wetter, such as the southern grain belt of Western Australia (SWA), it is probable that lupin would have a higher yield potential. Given that VU cultivars would have a longer vegetative phase (i.e. late flowering) we hypothesise that they may be more productive than those that are early flowering. Here we used a modelling approach to: 1) test the hypothesis that cool-climate SWA would have higher lupin yield than warm-climate NWA; 2) explore lupin phenological adaptation and yield potential in SWA over a range of proposed VU cultivars; and 3) further evaluate the combined effects of cultivar phenology, sowing time and seasonal type on lupin yields. Simulations from the Agricultural Production Systems Simulator (APSIM) showed that, on average, lupin yield in SWA was higher than that in NWA, with 23% greater yield for the early-flowering cultivar Mandelup. Proposed cultivars flowering 22days (late-flowering) and 15days (medium flowering) later than Mandelup would have their phenology better adapted in the high and medium rainfall zones of SWA, producing 16 and 7% more grain in the two rainfall zones, respectively. The proposed late-flowering cultivar sown before the end of April achieved higher yields for all seasons in the high rainfall zone and for above average and average rainfall seasons in the medium rainfall zone. In more water-limited situations early sowing was preferable with no obvious difference in yield among cultivars. Despite this, the early-flowering cultivar yielded more when sown in late April. The results indicate that lupin production would benefit from breeding VU varieties with a long vegetative phase for the SWA that should be sown in mid to late April.

      PubDate: 2017-06-06T19:02:08Z
       
  • Zinc biofortification of wheat through preceding crop residue
           incorporation into the soil
    • Abstract: Publication date: Available online 29 May 2017
      Source:European Journal of Agronomy
      Author(s): Amir Hossein Khoshgoftarmanesh, Mojtaba Norouzi, Majid Afyuni, Rainer Schulin
      We conducted a two-year field experiment to investigate the potential benefit of preceding crop residue incorporation into the soil as a strategy to enhance the density of bioavailable grain zinc (Zn) in a subsequent wheat (Triticum aestivum L.) crop. Sunflower (Heilianthus annuus L. cv. Allstar), sorghum (Sorghum bicolor L. cv. Speed Feed), clover (Trifolium pratense L.) and safflower (Carthamus tinctorius L. cv. Koseh-e-Isfahan) were grown as preceding crop (precrop) on a Zn-deficient calcareous soil in central Iran, followed by a culture of two wheat cultivars i.e., Kavir and Back Cross Rushan. The harvested aboveground plant matter was air-dried, crushed into pieces of 0.5–2cm size, mixed, and after taking a sample for analysis, incorporated manually into the upper 15cm of the soil of one half of the same plot from which it had been harvested, while the other half received no residues. The aboveground residues of precrops were incorporated into soil or removed. A treatment with no preceding crop (fallow) and no residue incorporation, but with the same management otherwise, was implemented as control treatment. For both wheat cultivars studied, higher grain yield was obtained after clover (between 14 and 25.6%) and sunflower (between 11.3 and 19.5%) than that after safflower, sorghum and the fallow. All precrop treatments significantly increased the accumulation of grain Zn and N and decreased the phytic-acid-to-Zn (PA:Zn) molar ratio (by 5–41% in Kavir and by 11–48% in Back Cross), most effectively the clover treatment. The treatment effects on grain Zn were closely correlated with soil pH and dissolved soil organic carbon (DOC). The results show that the cultivation of appropriate precrops, especially legumes, can be an effective strategy to biofortify wheat grains with Zn without compromising yields.

      PubDate: 2017-06-01T18:54:49Z
       
  • Modelling productivity and resource use efficiency for grassland
           ecosystems in the UK
    • Abstract: Publication date: Available online 24 May 2017
      Source:European Journal of Agronomy
      Author(s): Aiming Qi, Philip J. Murray, Goetz M. Richter
      Estimating spatially resolved grassland productivity is essential for benchmarking the total UK productive potential to assess food, feed and fuel trade-offs in the context of whole systems analyses. Our objectives were to adapt and evaluate a well-known process-based model (PBM) and estimate productivity of improved (permanent, temporary) and semi-natural grassland systems using meta-models (MM) trained by extensive PBM scenario simulations. Observed dry matter (DM) yields in multi-site nitrogen (N) response (0, 150 and 300kgNha−1) experiments were well emulated describing the average productivity of rough grazing, permanent and temporary grassland (3.1, 7.4 and 9.8tDMha−1, respectively). Cross-validated with independent and long-term data (Park Grass Experiment), the PBM explained more variation when considering all systems combined (81%) than across all improved grasslands (61%) but little for rough grazing (26%). The PBM-trained MMs explained 48, 72 and 70% of the simulated yield variation in the grasslands of increasing management intensity, and 43 and 75% of observed variation in the combined improved and all three grassland systems, respectively. Considering the assessment of ecosystem services, like drainage and water productivity, PBM scenario simulations are essential. Compared to improved grassland rough grazing will result in 40% more groundwater recharge due to its lower simulated water use and water productivity (12 versus 25 and 43kgha−1 mm−1 for permanent and temporary grassland, respectively).

      PubDate: 2017-05-28T03:54:02Z
       
  • Yield, growth and grain nitrogen response to elevated CO2 in six lentil
           (Lens culinaris) cultivars grown under Free Air CO2 Enrichment (FACE) in a
           semi-arid environment
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): M. Bourgault, J. Brand, S. Tausz-Posch, R.D. Armstrong, G.L. O’Leary, G.J. Fitzgerald, M. Tausz
      Atmospheric CO2 concentrations ([CO2]) are predicted to increase from current levels of about 400ppm to reach 550ppm by 2050. The direct benefits of elevated [CO2] (e[CO2]) to plant growth appear to be greater under low rainfall conditions, but there are few field (Free Air CO2 Enrichment or FACE) experimental set-ups that directly address semi-arid conditions. The objectives of this study were to investigate the following research questions: 1) What are the effects of e[CO2] on the growth and grain yield of lentil (Lens culinaris) grown under semi-arid conditions under FACE? 2) Does e[CO2] decrease grain nitrogen in lentil? and 3) Is there genotypic variability in the response to e[CO2] in lentil cultivars? Elevated [CO2] increased yields by approximately 0.5tha−1 (relative increase ranging from 18 to 138%) by increasing both biomass accumulation (by 32%) and the harvest index (by up to 60%). However, the relative response of grain yield to e[CO2] was not consistently greater under dry conditions and might depend on water availability post-flowering. Grain nitrogen concentration was significantly reduced by e[CO2] under the conditions of this experiment. No differences were found between the cultivars selected in the response to elevated [CO2] for grain yield or any other parameters observed despite well expressed genotypic variability in many traits of interest. Biomass accumulation from flowering to maturity was considerably increased by elevated [CO2] (a 50% increase) which suggests that the indeterminate growth habit of lentils provides vegetative sinks in addition to reproductive sinks during the grain-filling period.

      PubDate: 2017-05-22T18:45:30Z
       
  • A methodology for multi-objective cropping system design based on
           simulations. Application to weed management
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): Nathalie Colbach, Floriane Colas, Olivia Pointurier, Wilfried Queyrel, Jean Villerd
      Weeds are harmful for crop production but important for biodiversity. In order to design cropping systems that reconcile crop production and biodiversity, we need tools and methods to help farmers to deal with this issue. Here, we developed a novel method for multi-objective cropping system design aimed at scientists and technical institutes, combining a cropping system database, decision trees, the “virtual field” model FlorSys and indicators translating simulated weed floras into scores in terms of weed harmfulness (e.g. crop yield loss, weed-borne parasite risk, field infestation), weed-mediated biodiversity (e.g. food offer for bees) and herbicide use intensity. 255 existing cropping systems were simulated with FlorSys, individual indicator values were aggregated into a multi-performance score, and decision trees were built to identify combinations of management practices and probabilities for reaching performance goals. These trees are used to identify the characteristics of existing cropping systems that must be changed to achieve the chosen performance goals, depending on the user's risk strategy. Alternative systems are built and simulated with FlorSys to evaluate their multi-criteria performance. The method was applied to an existing oilseed rape/wheat/barley rotation with yearly mouldboard ploughing from Burgundy which was improved to reconcile weed harmfulness control, reduced herbicide use and biodiversity promotion, based on a risk-minimizing strategy. The best alternative replaced a herbicide entering plants via shoot tips (during emergence) and roots after barley sowing by a spring herbicide entering via leaves, introduced crop residue shredding before cereals and rolled the soil at sowing, which reduced the risk of unacceptable performance from 90% to 40%. When attempting to reconcile harmfulness control and reduced herbicide use, the best alternative changed the rotation to oilseed rape/wheat/spring pea/wheat, replaced one herbicide in oilseed rape by mechanical weeding, delayed tillage before rape and applied the PRE herbicide before oilseed rape closer to sowing. This option reduced the risk of unacceptable performance to 30%. None of the initial or alternative cropping systems succeeded in optimal performance, indicating that more diverse cropping systems with innovative management techniques and innovative combinations of techniques are needed to build the decision trees. This approach can be used in workshops with extension services and farmers in order to design cropping systems. Compared to expert-based design, it has the advantage to go beyond well-known options (e.g. plough before risky crops) to identify unconventional options, with a particular focus on interactions between cultural techniques.

      PubDate: 2017-05-22T18:45:30Z
       
  • Uncertainty from model structure is larger than that from model parameters
           in simulating rice phenology in China
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): Shuai Zhang, Fulu Tao, Zhao Zhang
      Rice models have been widely used in simulating and predicting rice phenology in contrasting climate zones, however the uncertainties from model structure (different equations or models) and/or model parameters were rarely investigated. Here, five rice phenological models/modules (i.e., CERES-Rice, ORYZA2000, RCM, Beta Model and SIMRIW) were applied to simulate rice phenology at 23 experimental stations from 1992 to 2009 in two major rice cultivation regions of China: the northeastern China and the southwestern China. To investigate the uncertainties from model biophysical parameters, each model was run with randomly perturbed 50 sets of parameters. The results showed that the median of ensemble simulations were better than the simulation by most models. Models couldn’t simulate well in some specific years despite of parameters optimization, suggesting model structure limit model performance in some cases. The models adopting accumulative thermal time function (e.g., CERES-Rice and ORYZA2000) had better performance in the southwestern China, in contrast, those adopting exponential function (e.g., Beta model and RCM model) had better performance in the northeastern China. In northeastern China, the contribution of model structure and model parameters to model total variance was, respectively, about 55.90% and 44.10% in simulating heading date, and about 75.43% and 24.57% in simulating maturity date. In the southwestern China, the contribution of model structure and model parameters to model total variance was, respectively, about 79.97% and 27.03% in simulating heading date, about 92.15% and 7.85% in simulating maturity date. Uncertainty from model structure was the most relevant source. The results highlight that the temperature response functions of rice development rate under extreme climate conditions should be improved based on environment-controlled experimental data.

      PubDate: 2017-05-12T18:10:59Z
       
  • Differences in gluten protein composition between old and modern durum
           wheat genotypes in relation to 20th century breeding in Italy
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): Michele A. De Santis, Marcella M. Giuliani, Luigia Giuzio, Pasquale De Vita, Alison Lovegrove, Peter R. Shewry, Zina Flagella
      The impact of breeding on grain yields of wheat varieties released during the 20th century has been extensively studied, whereas less information is available on the changes in gluten quality associated with effects on the amount and composition of glutenins and gliadins. In order to explore the effects of breeding during the 20th century on gluten quality of durum wheat for processing and health we have compared a set of old and modern Italian genotypes grown under Mediterranean conditions. The better technological performance observed for the modern varieties was found to be due not only to the introgression of superior alleles of high (HMW-GS) and low molecular weight (LMW-GS) glutenin subunits encoded at Glu-B1 and Glu-B3 loci, but also to differential expression of specific storage proteins. In particular, the higher gluten index observed in modern genotypes was correlated with an increased glutenin/gliadin ratio and the expression of B-type LMW-GS which was, on average, two times higher in the modern than in the old group of durum wheat genotypes. By contrast, no significant differences were found between old and modern durum wheat genotypes in relation to the expression of α-type and γ-type gliadins which are major fractions that trigger coeliac disease (CD) in susceptible individuals. Furthermore, a drastic decrease was observed in the expression of ω-type gliadins in the modern genotypes, mainly ω-5 gliadin (also known as Tri a 19) which is a major allergen in wheat dependent exercise induced anaphylaxis (WDEIA). Immunological and 2DE SDS-PAGE analyses indicated that these differences could be related either to a general down-regulation or to differences in numbers of isoforms. Lower rainfall during grain filling period was related to overall higher expression of HMW-GS and ω-gliadins. In conclusion, breeding activity carried out in Italy during the 20th century appears to have improved durum wheat gluten quality, both in relation to technological performance and allergenic potential.
      Graphical abstract image

      PubDate: 2017-05-02T09:29:55Z
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86


      PubDate: 2017-05-02T09:29:55Z
       
  • Functional identity has a stronger effect than diversity on mycorrhizal
           symbiosis and productivity of field grown organic tomato
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): Ezekiel Mugendi Njeru, Gionata Bocci, Luciano Avio, Cristiana Sbrana, Alessandra Turrini, Manuela Giovannetti, Paolo Bàrberi
      Beneficial soil biota, and in particular, arbuscular mycorrhizal fungi (AMF) are increasingly being recognized as key elements of organic and low-input agriculture where agrobiodiversity is central to enhanced crop production. However, the role of AMF in diversified organic systems, especially in field crops, is still poorly understood. A 3-year field experiment was carried out in Central Italy to investigate whether organic cropping systems that promote species and genetic diversity are more prone to mycorrhizal symbiosis increasing tomato growth, production and yield quality. Three tomato cultivars with varying genetic diversity were grown following four cover treatments: Indian mustard (Brassica juncea L. Czern.), hairy vetch (Vicia villosa Roth), a commercial mixture of seven cover crop species (Mix 7) and no-till fallow. Plants were either inoculated or not in nursery, with the two AMF isolates Funneliformis mosseae (IMA1) and Rhizoglomus intraradices (IMA6) used alone or mixed in a 1:1 volume ratio. On average, Mix 7 produced higher shoot dry matter (5.0tha−1) than V. villosa (3.5tha−1) or B. juncea (2.5tha−1). Pre-transplant inoculation increased tomato root colonization at flowering and harvest compared to the non inoculated plants (31.8 vs 23.6%) and cv. Rio Grande was on average the best colonized. The mean fresh weight of marketable fruits was 18.4, 28.0 and 28.6tha−1 for cvs. Rio Grande, Roma and Perfect Peel, respectively. Cover crops inconsistently affected tomato marketable fruit production in year 1, while in years 2 and 3, Vicia villosa and Mix 7 showed the best effect respectively. In year 3, among the pre-inoculated plants those treated with isolate IMA6 showed a higher production of marketable fruit number m−2 (56.7) than those inoculated either with IMA1 (51.5) or the mixed inocula (52.1). Most fruit quality parameters were affected by tomato genotype. This study shows that while increased agrobiodiversity is important to increase agroecosystem resilience, AMF, crop and cover crop functional identity may be more important than diversity per se to promote mycorrhizal symbiosis and productivity of field grown organic tomato.

      PubDate: 2017-05-02T09:29:55Z
       
  • A RVI/LAI-reference curve to detect N stress and guide N fertigation using
           combined information from spectral reflectance and leaf area measurements
           in potato
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): Zhenjiang Zhou, Finn Plauborg, Anton G. Thomsen, Mathias Neumann Andersen
      More user-friendly methods are needed to detect crop N status/stress and guide the timing of in-season N application. In the current study, a reference curve method of detecting N stress was proposed to remedy practical problems of methods that require leaf sampling or maintaining a N sufficient strip in the field. The reference curve method was derived from the integrated information of ratio vegetation index (RVI) and leaf area index (LAI), which were obtained from field experimental potato crops. Different N treatments received 42kgNha−1 at planting and, subsequently, the rest of N was applied during the season. The total N ranged from 0 to180kgNha−1. RVI and LAI from the economically optimum 180kgNha−1 treatments were used to derive the reference curve. RVI and LAI from 180kgNha−1 treatment had a high (R2 =0.97) correlation and were best fitted with a 2nd order polynomial function, which was independent of season. The treatments where N fertigation was stopped before reaching 180kgNha−1 started to deviate from the 95% confidence interval of the reference curve about 10days after N-fertigation was stopped. This corresponded to 10–20kgha−1 difference in total plant N uptake between reference and the N deprived treatments, implying that a deviation from the reference curve occurred for small N deficits. Besides, running crop simulation model to alert for impendent N stress closely corresponded to the reference curve and was recommended as a second management tool. Therefore two tools are hereby made available to guide supplementary N-fertilization. These will be helpful in regional potato production for diagnosis of N status, and allow discrimination between situations of sub-optimal and optimal N supply.

      PubDate: 2017-04-18T09:05:41Z
       
  • Radiation use efficiency, chemical composition, and methane yield of
           biogas crops under rainfed and irrigated conditions
    • Abstract: Publication date: July 2017
      Source:European Journal of Agronomy, Volume 87
      Author(s): Burkhard Schoo, Henning Kage, Siegfried Schittenhelm
      For biomethane production, the cup plant (Silphium perfoliatum L.) is considered a promising alternative substrate to silage maize (Zea mays L.) due to its high biomass potential and associated ecological and environmental benefits. It has also been suggested to grow cup plant on less productive soils because of its presumed drought tolerance, but robust information on the impact of water shortage on biomass growth and substrate quality of cup plant is rare. Therefore, this study assesses the effects of soil water availability on the chemical composition and specific methane yield (SMY) of cup plant. Furthermore above-ground dry matter yield (DMY) was analysed as a function of intercepted photosynthetic active radiation (PAR) and radiation use efficiency (RUE). Data were collected in a two-year field experiment under rainfed and irrigated conditions with cup plant, maize, and lucerne-grass (Medicago sativa L., Festuca pratensis Huds., Phleum pratense L.). The cup plant revealed a slight decrease of −6% in the SMY in response to water shortage (less than 50% of plant available water capacity). The average SMY of cup plant [306l (kg volatile solids (VS))−1] was lower than that of maize [362l (kg VS)−1] and lucerne-grass [334l (kg VS)−1]. The mean drought-related reduction of the methane hectare yield (MHY) was significantly greater for cup plant (−40%) than for maize (−17%) and lucerne-grass (−13%). The DMY reduction in rainfed cup plant was mainly attributed to a more severe decrease in RUE (−29%) than for maize (−16%) and lucerne-grass (−12%). Under water stress, the mean cup plant RUE (1.3gMJ−1) was significantly lower than that of maize (2.9gMJ−1) and lucerne-grass (1.4gMJ−1). Compared to RUE, the reduced PAR interception was less meaningful for DMY in rainfed crops. Hence, the cup plant is not suitable for growing on drought prone lands due to its high water demand required to produce reasonably high MHYs.

      PubDate: 2017-04-18T09:05:41Z
       
  • Cytokinins: A key player in determining differences in patterns of canopy
           senescence in Stay-Green and Fast Dry-Down sunflower (Helianthus annuus
           L.) hybrids
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): Mariano A. Mangieri, Antonio J. Hall, Gustavo G. Striker, Claudio A. Chimenti
      Leaf senescence during grain filling can reduce crop yield. We studied, under field conditions and during grain-filling, the association between leaf cytokinin levels and the onset of leaf senescence in sunflower hybrids of contrasting canopy senescence patterns (Paraiso75, stay-green [SG] and Paraiso65, fast dry down [FDD]). At crop level, dynamics of live root length density (LRLD) and green leaf area index (GLAI) were followed, while at leaf level dynamics of total chlorophyll content, trans-Zeatin content, net photosynthesis and PSII quantum yield, were followed in leaf positions 17, 20, 22 and 24. Responses of these leaf variables to exogenous cytokinin applications to leaves at position 17 were also followed. SG exhibited greater (p<0.05) LRLD and GLAI values at anthesis. In both hybrids, LRLD began to fall before GLAI. All variables decreased earlier (p<0.05) in FDD. Initial leaf levels of trans-Zeatin were three times higher (p<0.05) in SG. Exogenous cytokinin applications maintained leaf-level variables. These are the first results showing associations between LRLD dynamics with the dynamics of leaf cytokinin levels and changes in indicators of leaf functionality. Also, this is the first study in which estimates are made of cytokinin thresholds below which leaf senescence begins in two hybrids of contrasting canopy senescence patterns. These advances in the understanding, at both crop and leaf levels, of the controls and consequences of SG during grain filling, a trait known to improve crop water uptake under drought and increase biomass accumulation during grain filling, provide support for breeding efforts aimed at profiting from this trait to increase crop yields.

      PubDate: 2017-04-10T21:57:41Z
       
  • Changes of starch composition by postflowering environmental conditions in
           kernels of maize hybrids with different endosperm hardness
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): R.D. Martínez, A.G. Cirilo, A. Cerrudo, F.H. Andrade, L. Reinoso, O.R. Valentinuz, C.N. Balbi, N.G Izquierdo
      Starch composition of maize grains is of great importance when used in animal feed and many processing industries. Maize production involves hybrids with different kernel composition and hardness, sown at areas that range from subtropical to temperate cold climates. Therefore, it is relevant to understand how the environment influences starch composition. The objective of this work was to analyze the effect of location and sowing date on starch composition of maize grains. Field experiments were carried out at five locations across the argentinean maize-production area during two growing seasons. At each location, two sowing dates and three hybrids differing in endosperm hardness (i.e. semi-dent, a semi-flint and flint) were evaluated. Late sowing dates reduced amylose percentage and amylose/starch ratio. This last variable increased as latitude decreased. Minimum temperature during effective grain filling period explained those latitude and sowing date effects. This finding would be helpful to estimate starch composition of maize kernels to be expected in order to satisfy specific end uses.

      PubDate: 2017-04-10T21:57:41Z
       
  • Tree-crop interactions in maize-eucalypt woodlot systems in southern
           Rwanda
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): C.P. Mugunga, K.E. Giller, G.M.J. Mohren
      We studied the interaction between Eucalyptus saligna woodlots and maize crop in southern Rwanda. Three sites were selected and in each, a eucalypt woodlot with mature trees and a suitable adjoining crop field of 12.75m×30m was selected. This was split into two plots of 6m×12m and further subdivided into nine sub-plots running parallel to the tree-crop interface. Maize was grown in both 6m×12m plots and one of these received fertiliser. Soil moisture, nutrients and solar radiation were significantly reduced near the woodlots, diminishing grain yield by 80% in the 10.5m crop-field strip next to the woodlot. This reduction however affects only 10.5% of the maize crop field, leaving 89.5% unaffected. Spreading the loss to a hectare crop field, leads to an actual yield loss of 0.21tha−1, equivalent to 8.4%. Expressing yield loss in tree-crop systems usually presented as a percentage of yield recorded near the trees to that obtained in open areas may be misleading. Actual yields should be reported with corresponding crop field areas affected. Variation in grain yield coincided with those for soil moisture, soil N and K; all increasing from the woodlot-maize interface up to 10.5m and remaining similar to the values in open areas thereafter. Solar radiation continued to increase with distance up to 18m from the woodlot-maize interface. Harvest index in unfertilised maize exceeded that in the fertilised treatment reflecting the crop’s strategy to allocate resources to grain production under unfavourable conditions. Fertilisation increased maize yield from 1.3–2.6tha−1 but the trend in the woodlot effects on maize remained unaltered.

      PubDate: 2017-04-10T21:57:41Z
       
  • Crop yield and energy use in organic and conventional farming: A case
           study in north-east Italy
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): Nicola Dal Ferro, Giuseppe Zanin, Maurizio Borin
      The role played by organic farming as an alternative system to conventional farming is widely questioned, since conflicting results on crop yields sometimes greatly affect system efficiency. As a result, prolonged monitoring studies on organic (OF) and conventional farming (CF) systems are still required, especially in real-life farm conditions, in which the entire production process is quantified. In this context, this study reports crop yields (winter wheat, maize, soybean) and energy efficiency, over a 13-year monitoring period, on a farm in north-east Italy in which two sectors are farmed following OF and CF practices. Results showed that organic yields were always lower than conventional ones, averaging 69%, although their range varied greatly over the years (from 45% to 90%) and depended on crop type. Several management constraints had effects on the lower yields, especially reduced available nutrients and cropping season, but also the timings and types of tillage operations. By contrast, OF practices usually had positive effects on the environment, due to reduced energy input mainly fertilisation (−33.4%MJha−1 y−1) and the generally higher productivity of invested energy (EOut EIn −1 =4.53 in OF and 4.28 in CF); energy use differences per product unit were mainly equal. Other factors, such as local climate and soil variability, may have influenced system performance, but as the two experimental sites were located at a distance of 3.5km from each other, the data reported here are still valuable, in that they represent the results of 13years of monitoring, during which farm management played a major role. This case study, although conducted in two separate sites, did not highlight the best overall solution at farm level, it does indicate that the agricultural systems applied would be better suited for different situations and targets (e.g., productive, energetic, ecologic).

      PubDate: 2017-03-27T19:13:49Z
       
  • Accumulation, partitioning, and bioavailability of micronutrients in
           summer maize as affected by phosphorus supply
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): Wei Zhang, Dun-Yi Liu, Chao Li, Xin-Ping Chen, Chun-Qin Zou
      Decreased micronutrient concentration in cereal grains caused by excessive application of phosphorus (P) fertilizer may contribute to reduce their nutritional quality. To help correct this problem in maize grain, a 3-year field experiment was conducted to determine how P application rate affects micronutrient partitioning in maize shoots and other plant organs and micronutrient bioavailability in grain. Phosphorus application significantly decreased shoot zinc (Zn) and copper (Cu) concentrations at all growth stages but had no effects on shoot iron (Fe) and manganese (Mn) concentrations. As the P application rate increased, shoot Zn and Cu contents decreased, and shoot Fe and Mn contents increased. The ratios of pre-anthesis to post-anthesis mineral contents were not affected by P application rate except Zn. P application increased the percentage of Zn that was allocated to grain and decreased the percentage that was allocated to other tissues, but had no effects on the allocation of other micronutrients among tissues. The bioavailability of Zn, Cu, Fe, and Mn in grain decreased as P application rate increased. Overall, taking account of grain yield and nutrients concentration, P fertilizer rates should range from 12.5 to 25.0kg P ha−1 under the local condition. It can be concluded that not only grain yields, but also nutritional quality, should be considered in assessing optimal P rates in maize.

      PubDate: 2017-03-27T19:13:49Z
       
  • Long-term P and K fertilisation strategies and balances affect soil
           availability indices, crop yield depression risk and N use
    • Abstract: Publication date: May 2017
      Source:European Journal of Agronomy, Volume 86
      Author(s): Frederik van der Bom, Jakob Magid, Lars Stoumann Jensen
      The last century has seen a large increase of fertiliser use, along with a subsequent rise of crop productivity. However, in many places its intensive use has become a burden to the environment, and legislation has been introduced to restrict nutrient applications. In combination with changing production scenarios as a result of climate change, this means an improved understanding is needed of how low nutrient availability and climatic stress factors affect yields and yield stability. We examined the long-term effects mineral and organic fertilisation on a nutrient-depleted field, and observed large annual variations: depending on the year, average spring barley yields under unfertilised management (U) were between 17–75% lower than the reference N½P½K½ (60–10–60kgha−1). Yields increased up to 174% under N1P1K1 (120–20–120kgha−1), while animal manure applications at an N availability level corresponding to N1 were between 79 and 137%. No temporal yield trends could be observed, but long-term changes of Olsen-P and exchangeable K were related to the nutrient balances (inputs-offtake) (r2 =0.60 and 0.59, respectively, P <0.001). Multiple linear regression analysis was used to examine the effects of the treatments in combination with annual weather variations. The results could be split into two outcomes, 1) a general relation between yields and temperatures for the periods of early spring (P< 0.01, multiple R2 =0.31) and summer (P< 0.001, multiple R2 =0.45), and 2) an interaction between temperature and nutrient applications during crop establishment, leading to a diverse response of relative yields (P< 0.001, multiple R2 =0.64), i.e. relative yield losses under the unfertilised treatment (U) were greater in years with lower spring temperatures, and, conversely, the increased nutrient availability in the fully mineral and organically fertilised treatments could partially alleviate the negative effects. After 13 years of repeated fertilisation, inputs were suspended for a single year and only N was applied to evaluate the residual effects. Yields were significantly affected by the different fertilisation histories (P< 0.001). Likewise, apparent nitrogen recovery tended to improve with previous inputs, but the observations were highly variable. Overall, the analyses agree with the notion that brief periods of stress at a critical stage may significantly affect yields, and confirmed that management of sufficient nutrient availability is critical for maintaining high and stable yields.
      Graphical abstract image

      PubDate: 2017-03-08T17:40:10Z
       
 
 
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