ORCID Profile
0000-0003-0918-550X
Current Organisations
University College London
,
University of Bristol
,
Aarhus University
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Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 03-2016
DOI: 10.1016/J.SCITOTENV.2015.12.104
Abstract: Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.
Publisher: Springer Science and Business Media LLC
Date: 06-06-2022
Publisher: MDPI AG
Date: 13-03-2020
Abstract: Nitrogen fertilization is the most critical agronomic input affecting barley production and farm profitability. The strict quality requirements for malting barley are challenging to achieve for farmers. In addition, soil variability and weather conditions can affect barley yield and quality. Thus, the objectives of this study are to (a) quantify the variability of soil properties, and (b) use spatial data in a crop simulation model, quantifying the impacts of climate−soil interactions on the barley crop yield and grain quality. Based on historical yield maps, a commercial field was ided into different yield stability zone levels. The Decision Support System for Agrotechnology Transfer model was used to evaluate soil and crop spatial data. The bulk density affected the soil water content and soil mineral N and hence the crop-growing conditions in each yield stability zone. Our observed and simulated results showed that 120 kg N ha−1 is the optimal rate to increase grain yield while still keeping within the grain N% requirements for malting quality. This study shows the great value of integrating crop modeling with on−farm experimental data for improving understanding of the factors which affect site−specific N fertilization of barley.
Publisher: Elsevier BV
Date: 04-2016
Publisher: MDPI AG
Date: 12-11-2020
Abstract: Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The sub ision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than in idually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.
Publisher: Springer Science and Business Media LLC
Date: 22-12-2014
DOI: 10.1038/NCLIMATE2470
Publisher: Inter-Research Science Center
Date: 28-09-2015
DOI: 10.3354/CR01322
Publisher: Springer Science and Business Media LLC
Date: 06-02-2013
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 08-2012
Publisher: Inter-Research Science Center
Date: 28-09-2015
DOI: 10.3354/CR01326
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 08-2017
Publisher: Frontiers Media SA
Date: 15-04-2021
Abstract: Heading time in barley is considered a key developmental stage controlling adaptation to the environment and it affects grain yield with the combination of agronomy (planting dates) and genetics being some of the determinants of adaptation to environmental conditions in order to escape late frost, heat, and terminal drought stresses. The objectives of this study are (i) to apply a gene-based characterization of 118 barley doubled haploid recombinants for vernalization, photoperiod, and earliness per se (ii) use such information to quantify the optimal combination of genotype/sowing date that escapes extreme weather events and (iii) how water and nitrogen management impact on grain yield. The doubled haploid barley genotypes with different allelic combinations for vernalization, photoperiod, and earliness per se were grown in eight locations across the Mediterranean basin. This information was linked with the crop growth model parameters. The photoperiod and earliness per se alleles modify the length of the phenological cycle, and this is more evident in combination with the recessive allele of the vernalization gene VRN-H2. In hot environments such as Algeria, Syria, and Jordan, early sowing dates (October 30 and December15) would be chosen to minimize the risk of exposing barley to heat stress. To maintain higher yields in the Mediterranean basin, barley breeding activities should focus on allelic combinations that have recessive VRN-H2 and EPS2 genes, since the risk of cold stress is much lower than the one represented by heat stress.
Publisher: Informa UK Limited
Date: 03-06-2021
Publisher: Wiley
Date: 2018
Publisher: Elsevier BV
Date: 08-2010
Publisher: Elsevier BV
Date: 09-2019
Publisher: MDPI AG
Date: 27-03-2014
DOI: 10.3390/RS6042827
Publisher: Informa UK Limited
Date: 09-2013
Publisher: Springer Science and Business Media LLC
Date: 04-09-2015
Publisher: CSIRO Publishing
Date: 2012
DOI: 10.1071/CP11324
Abstract: Projected increases in atmospheric carbon dioxide concentration ([CO2]) and air temperature associated with future climate change are expected to affect crop development, crop yield, and, consequently, global food supplies. They are also likely to change agricultural production practices, especially those related to agricultural water management and sowing date. The magnitude of these changes and their implications to local production systems are mostly unknown. The objectives of this study were to: (i) simulate the effect of projected climate change on spring wheat (Triticum aestivum L. cv. Lang) yield and water use for the subtropical environment of the Darling Downs, Queensland, Australia and (ii) investigate the impact of changing sowing date, as an adaptation strategy to future climate change scenarios, on wheat yield and water use. The multi-model climate projections from the IPCC Coupled Model Intercomparison Project (CMIP3) for the period 2030–2070 were used in this study. Climate scenarios included combinations of four changes in air temperature (0°C, 1°C, 2°C, and 3°C), three [CO2] levels (380 ppm, 500 ppm, and 600 ppm), and three changes in rainfall (–30%, 0%, and +20%), which were superimposed on observed station data. Crop management scenarios included a combination of six sowing dates (1 May, 10 May, 20 May, 1 June, 10 June, and 20 June) and three irrigation regimes (no irrigation (NI), deficit irrigation (DI), and full irrigation (FI)). Simulations were performed with the model DSSAT 4.5, using 50 years of daily weather data. We found that: (1) grain yield and water-use efficiency (yield/evapotranspiration) increased linearly with [CO2] (2) increases in [CO2] had minimal impact on evapotranspiration (3) yield increased with increasing temperature for the irrigated scenarios (DI and FI), but decreased for the NI scenario (4) yield increased with earlier sowing dates and (5) changes in rainfall had a small impact on yield for DI and FI, but a high impact for the NI scenario.
Publisher: Public Library of Science (PLoS)
Date: 19-01-2016
Publisher: Springer Science and Business Media LLC
Date: 12-09-2016
DOI: 10.1038/NCLIMATE3115
Publisher: Wiley
Date: 10-11-2016
DOI: 10.1111/GCB.13530
Abstract: Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield-temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security 'hot spots'. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production.
Publisher: PAGEPress Publications
Date: 22-12-2010
DOI: 10.4081/IJA.2011.E1
Publisher: Oxford University Press (OUP)
Date: 21-06-2022
DOI: 10.1093/JXB/ERAC221
Abstract: Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.
Publisher: Wiley
Date: 24-01-2019
DOI: 10.1111/GCB.14542
Abstract: Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 10-2012
Publisher: CSIRO Publishing
Date: 2012
DOI: 10.1071/CP12024
Abstract: Cotton is one of the most important irrigated crops in subtropical Australia. In recent years, cotton production has been severely affected by the worst drought in recorded history, with the 2007–08 growing season recording the lowest average cotton yield in 30 years. The use of a crop simulation model to simulate the long-term temporal distribution of cotton yields under different levels of irrigation and the marginal value for each unit of water applied is important in determining the economic feasibility of current irrigation practices. The objectives of this study were to: (i) evaluate the CROPGRO-Cotton simulation model for studying crop growth under deficit irrigation scenarios across ten locations in New South Wales (NSW) and Queensland (Qld) (ii) evaluate agronomic and economic responses to water inputs across the ten locations and (iii) determine the economically optimal irrigation level. The CROPGRO-Cotton simulation model was evaluated using 2 years of experimental data collected at Kingsthorpe, Qld The model was further evaluated using data from nine locations between northern NSW and southern Qld. Long-term simulations were based on the prevalent furrow-irrigation practice of refilling the soil profile when the plant-available soil water content is %. The model closely estimated lint yield for all locations evaluated. Our results showed that the amounts of water needed to maximise profit and maximise yield are different, which has economic and environmental implications. Irrigation needed to maximise profits varied with both agronomic and economic factors, which can be quite variable with season and location. Therefore, better tools and information that consider the agronomic and economic implications of irrigation decisions need to be developed and made available to growers.
Publisher: Wiley
Date: 11-2011
Publisher: Elsevier BV
Date: 11-2013
Publisher: Elsevier BV
Date: 02-2017
Publisher: Springer Science and Business Media LLC
Date: 19-07-2023
Publisher: Wiley
Date: 24-08-2018
DOI: 10.1111/GCB.14411
Abstract: A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between in idual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all in idual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Publisher: Elsevier BV
Date: 11-2011
Publisher: Elsevier BV
Date: 06-2016
Publisher: Wiley
Date: 06-02-2015
DOI: 10.1111/GCB.12830
Publisher: Wiley
Date: 04-01-2018
DOI: 10.1111/GCB.14019
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 10-2017
Publisher: Inter-Research Science Center
Date: 28-09-2015
DOI: 10.3354/CR01301
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 2018
Publisher: Island Press/Center for Resource Economics
Date: 2013
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 04-09-2015
Publisher: Wiley
Date: 09-07-2009
Publisher: Springer Science and Business Media LLC
Date: 09-06-2013
DOI: 10.1038/NCLIMATE1916
Publisher: Cambridge University Press (CUP)
Date: 09-2021
DOI: 10.1017/S0021859621000903
Abstract: The goals of this study are to analyse the impacts of 1.5 and 2.0°C scenarios on UK winter wheat using a combination of Global Climate Models (GCMs), crop models, planting dates and cultivars to evaluate the impact of increased air temperature on winter wheat phenology and potential yield to quantify the underlying uncertainties due to the multiple sources of variability introduced by climate scenarios, crop models and agronomic management. The data used to calibrate and evaluate three crop models were obtained from a field experiment with two irrigation amounts and two wheat cultivars that have different phenology and growth habit. After calibration, the model was applied to fifty locations across the wheat-growing area of the UK to cover all the main growing regions, with most points located in the main growing areas. Four GCMs, with two cultivars and five planting dates, were simulated at the end of the century. Results of this study showed that the UK potential wheat yield will increase by 2–8% under projected climate. Farmers will need to close such a gap in the future because it will have implications in terms of food security. Future climatic conditions might increase such a gap. Adaptation measures (e.g. moving the optimal planting time), along with climate-ready varieties bred for future conditions and with precision agriculture techniques can help to reduce this gap and ensure that the future actual UK wheat production will be close to the potential.
Publisher: Wiley
Date: 22-11-2018
DOI: 10.1111/GCB.14481
Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO
Publisher: Springer International Publishing
Date: 2023
Publisher: Research Square Platform LLC
Date: 17-03-2023
DOI: 10.21203/RS.3.RS-2667076/V1
Abstract: Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with sink-source improved traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The sink-source traits emerged as climate neutral with 16% yield increase with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century RCP8.5 climate scenario, fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.
Publisher: Springer International Publishing
Date: 2023
Publisher: Elsevier BV
Date: 09-2014
Publisher: Elsevier BV
Date: 10-2020
Publisher: Wiley
Date: 27-06-2019
DOI: 10.1111/TPJ.14414
Publisher: Informa UK Limited
Date: 02-2004
Publisher: Springer Science and Business Media LLC
Date: 17-07-2017
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
Publisher: CSIRO Publishing
Date: 17-05-2023
DOI: 10.1071/CP23096
Publisher: PAGEPress Publications
Date: 07-10-2010
Publisher: Elsevier BV
Date: 08-2011
Publisher: Springer Science and Business Media LLC
Date: 07-07-2022
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 2019
Publisher: CSIRO Publishing
Date: 2011
DOI: 10.1071/CP10100
Abstract: For this study we hypothesise that the use of canopy chlorophyll content index (CCCI) and crop greenness will be useful in assessing crop nutritional status and provide a robust management tool by growth stage DC30 for fertiliser application across multiple sites without being confounded by soil and biomass differences. The objectives of this study were: (i) to study the robustness of the CCCI and greenness as a measure of crop N content at two different locations, and (ii) to validate the model developed for crop nitrogen (N) determination. Data were collected from two rain-fed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south-eastern wheat belt of Australia (Horsham). Data collection was conducted during the growing season in 2006–07 (December–June) for the Italian site and during the 2006 and 2007 (June–December) growing seasons for the Australian site. Measurements included crop biophysical properties (leaf area index (LAI), biomass, crop N concentration), hyperspectral remote sensing data, and SPAD (chlorophyll meter) determination. An independent dataset including SPAD, biomass, and remotely sensed data from Horsham (Australia) was used to test the validity of the model developed. Results showed that there is good correlation between SPAD and crop N content. The relationship between greenness (measured as LAI*SPAD) and CCCI was fitted with an exponential model and was not affected by biomass accumulation or soil reflectance (r2 = 0.85 y = 15.1e4.5424x P 0.001). When this model was tested on the independent dataset it yielded good results for the estimation of greenness (y = 1.22x − 54.87 r2 = 0.90 P 0.001 root mean square error 32.2 relative error 15%). In conclusion, SPAD measurements combined with LAI could be used as a crop nutritional management tool by DC30 for fertiliser application across multiple sites.
Publisher: Elsevier BV
Date: 03-2015
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2021
End Date: 2022
Funder: Indiana Corn Marketing Council
View Funded ActivityStart Date: 2022
End Date: 2025
Funder: Ministeriet for Fø devarer, Landbrug og Fiskeri
View Funded ActivityStart Date: 2021
End Date: 2024
Funder: National Science Foundation, Belmont Forum
View Funded Activity