1
|
Dueri S, Brown H, Asseng S, Ewert F, Webber H, George M, Craigie R, Guarin JR, Pequeno DNL, Stella T, Ahmed M, Alderman PD, Basso B, Berger AG, Mujica GB, Cammarano D, Chen Y, Dumont B, Rezaei EE, Fereres E, Ferrise R, Gaiser T, Gao Y, Garcia-Vila M, Gayler S, Hochman Z, Hoogenboom G, Kersebaum KC, Nendel C, Olesen JE, Padovan G, Palosuo T, Priesack E, Pullens JWM, Rodríguez A, Rötter RP, Ramos MR, Semenov MA, Senapati N, Siebert S, Srivastava AK, Stöckle C, Supit I, Tao F, Thorburn P, Wang E, Weber TKD, Xiao L, Zhao C, Zhao J, Zhao Z, Zhu Y, Martre P. Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5715-5729. [PMID: 35728801 PMCID: PMC9467659 DOI: 10.1093/jxb/erac221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
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.
Collapse
Affiliation(s)
- Sibylle Dueri
- LEPSE, Univ. Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Hamish Brown
- The New Zealand Institute for Plant & Food Research Limited, Christchurch, New Zealand
| | - Senthold Asseng
- Department of Life Science Engineering, Digital Agriculture, Technical University of Munich, Freising, Germany
| | - Frank Ewert
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Heidi Webber
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
- Brandenburg University of Technology, Faculty of Environment and Natural Sciences, Cottbus, Germany
| | - Mike George
- The New Zealand Institute for Plant & Food Research Limited, Christchurch, New Zealand
| | - Rob Craigie
- Foundation for Arable Research, Templeton, New Zealand
| | - Jose Rafael Guarin
- Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, USA
- Center for Climate Systems Research, Earth Institute, Columbia University, New York, NY, USA
- NASA Goddard Institute for Space Studies, New York, NY, USA
| | - Diego N L Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Mexico DF, Mexico
| | - Tommaso Stella
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Mukhtar Ahmed
- Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences Umeå, Sweden
| | - Phillip D Alderman
- Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Bruno Basso
- Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA
- W. K. Kellogg Biological Station, Michigan State University, East Lansing, MI, USA
| | - Andres G Berger
- National Institute of Agricultural Research (INIA), Colonia, Uruguay
| | - Gennady Bracho Mujica
- Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
| | | | - Yi Chen
- Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China
| | - Benjamin Dumont
- Plant Sciences Axis – Crop Science, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium
| | | | - Elias Fereres
- IAS-CSIC & DAUCO, University of Cordoba, Cordoba, Spain
| | - Roberto Ferrise
- Department of Agriculture, food, environment and forestry (DAGRI), University of Florence, Florence, Italy
| | - Thomas Gaiser
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
| | - Yujing Gao
- Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | | | - Sebastian Gayler
- Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
| | - Zvi Hochman
- CSIRO Agriculture and Food, Brisbane, Queensland, Australia
| | - Gerrit Hoogenboom
- Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL, USA
- Institute for Sustainable Food Systems, University of Florida, Gainesville, FL, USA
| | - Kurt C Kersebaum
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
- Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
- Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic
| | - Claas Nendel
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
- Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic
- Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Jørgen E Olesen
- Department of Agroecology, Aarhus University, Tjele, Denmark
- Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic
| | - Gloria Padovan
- Department of Agriculture, food, environment and forestry (DAGRI), University of Florence, Florence, Italy
| | - Taru Palosuo
- Natural Resources Institute Finland (Luke), Helsinki, Finland
| | - Eckart Priesack
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Alfredo Rodríguez
- CEIGRAM, Technical University of Madrid, Madrid, Spain
- Department of Economic Analysis and Finances, University of Castilla-La Mancha, Toledo, Spain
| | - Reimund P Rötter
- Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
- Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany
| | | | | | | | - Stefan Siebert
- Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany
- Department of Crop Sciences, University of Göttingen, Göttingen, Germany
| | - Amit Kumar Srivastava
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
| | - Claudio Stöckle
- Biological Systems Engineering, Washington State University, Pullman, WA, USA
| | - Iwan Supit
- Water Systems & Global Change Group, Wageningen University, Wageningen, The Netherlands
| | - Fulu Tao
- Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China
- Natural Resources Institute Finland (Luke), Helsinki, Finland
| | - Peter Thorburn
- CSIRO Agriculture and Food, Brisbane, Queensland, Australia
| | - Enli Wang
- CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | | | - Liujun Xiao
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Chuang Zhao
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Jin Zhao
- Department of Agroecology, Aarhus University, Tjele, Denmark
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Zhigan Zhao
- CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | | |
Collapse
|
2
|
Bregaglio S, Savian F, Raparelli E, Morelli D, Epifani R, Pietrangeli F, Nigro C, Bugiani R, Pini S, Culatti P, Tognetti D, Spanna F, Gerardi M, Delillo I, Bajocco S, Fanchini D, Fila G, Ginaldi F, Manici LM. A public decision support system for the assessment of plant disease infection risk shared by Italian regions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115365. [PMID: 35642822 DOI: 10.1016/j.jenvman.2022.115365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/05/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012-2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE = 4-14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.
Collapse
Affiliation(s)
- Simone Bregaglio
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy.
| | - Francesco Savian
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Elisabetta Raparelli
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Danilo Morelli
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Rosanna Epifani
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Fabio Pietrangeli
- Regional Agrometeorological Centre, Abruzzo Region, Contrada Colle Comune Scerni I-66020, Chieti CH, Italy
| | - Camilla Nigro
- Lucana Agency for Development and Innovation in Agriculture, Basilicata Region, Via Annunziatella, 64, I-75100 Matera MT, Italy
| | - Riccardo Bugiani
- Plant Protection Service, Emilia-Romagna Region, Via Saliceto 81, I-40128, Bologna BO, Italy
| | - Stefano Pini
- Servizi Alle Imprese Agricole e Florovivaismo, CAAR (Centro Agrometeorologia Applicata Regionale), Laboratori Regionali Analisi Terreni-Produzioni Vegetali e Fitopatologico, I-19038 Sarzana SP, Liguria Region, Italy
| | - Paolo Culatti
- Regione Lombardia, Plant Protection Service, I-20124 Milan MI, Italy
| | - Danilo Tognetti
- Centro Operativo Agrometeo ASSAM, Marche Region, Via Cavour, 29, I-62010 Treia MC, Italy
| | - Federico Spanna
- Regional Phytosanitary Service, Piemonte Region, Agrometeo Sector, I-10144, Torino, TO, Italy
| | - Marco Gerardi
- LAORE Sardegna, Regional Agency for Agriculture Development, Via Caprera 8, I-09123 Cagliari CA, Italy
| | - Irene Delillo
- ARPAV. Dipartimento Regionale per La Sicurezza Del Territorio. U.O.C. Meteorologia e Climatologia, Veneto Region, Via Marconi 55, I-35037 Teolo, PD, Italy
| | - Sofia Bajocco
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Davide Fanchini
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Gianni Fila
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Fabrizio Ginaldi
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Luisa M Manici
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| |
Collapse
|
3
|
Evaluating Effects of Medium-Resolution Optical Data Availability on Phenology-Based Rice Mapping in China. REMOTE SENSING 2022. [DOI: 10.3390/rs14133134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The phenology-based approach has proven effective for paddy rice mapping due to the unique flooding and transplanting features of rice during the early growing season. However, the method may be greatly affected if no valid observations are available during the flooding and rice transplanting phase. Here, we compare the effects of data availability of different sensors in the critical phenology phase, thereby supporting paddy rice mapping based on phenology-based approaches. Importantly, our study further analyzed the effects of the spatial pattern of the valid observations related to certain factors (i.e., sideslips, clouds, and temporal window lengths of flooding and rice transplanting), which supply the applicable area of the phenology-based approach indications. We first determined the flooding and rice transplanting phase using in situ observational data from agrometeorological stations and remote sensing data, then evaluated the effects of data availability in this phase of 2020 in China using all Landsat-7 and 8 and Sentinel-2 data. The results show that on the country level, the number of average valid observations during the flooding and rice transplanting phase was more than ten for the integration of Landsat and Sentinel images. On the sub-country level, the number of average valid observations was high in the cold temperate zone (17.4 observations), while it was relatively lower in southern China (6.4 observations), especially in Yunnan–Guizhou Plateau, which only had three valid observations on average. Based on the multicollinearity test, the three factors are significantly correlated with the absence of valid observations: (R2 = 0.481) and Std.Coef. (Std. Err.) are 0.306 (0.094), −0.453 (0.003) and −0.547 (0.019), respectively. Overall, these results highlight the substantial spatial heterogeneity of valid observations in China, confirming the reliability of the integration of Landsat-7 and 8 and Sentinel-2 imagery for paddy rice mapping based on phenology-based approaches. This can pave the way for a national-scale effort of rice mapping in China while further indicating potential omission errors in certain cloud-prone regions without sufficient optical observation data, i.e., the Sichuan Basin.
Collapse
|
4
|
Hassall KL, Coleman K, Dixit PN, Granger SJ, Zhang Y, Sharp RT, Wu L, Whitmore AP, Richter GM, Collins AL, Milne AE. Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 824:153824. [PMID: 35182632 PMCID: PMC9022088 DOI: 10.1016/j.scitotenv.2022.153824] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/31/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.
Collapse
Affiliation(s)
- Kirsty L Hassall
- Computational and Analytical Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Kevin Coleman
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Prakash N Dixit
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Steve J Granger
- Sustainable Agriculture Sciences department, Rothamsted Research, North Wyke, Oakhampton EX20 2SB, UK.
| | - Yusheng Zhang
- Sustainable Agriculture Sciences department, Rothamsted Research, North Wyke, Oakhampton EX20 2SB, UK.
| | - Ryan T Sharp
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Lianhai Wu
- Sustainable Agriculture Sciences department, Rothamsted Research, North Wyke, Oakhampton EX20 2SB, UK.
| | - Andrew P Whitmore
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Goetz M Richter
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| | - Adrian L Collins
- Sustainable Agriculture Sciences department, Rothamsted Research, North Wyke, Oakhampton EX20 2SB, UK.
| | - Alice E Milne
- Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.
| |
Collapse
|
5
|
Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques. AGRIENGINEERING 2022. [DOI: 10.3390/agriengineering4010019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry.
Collapse
|
6
|
Teixeira E, Kersebaum KC, Ausseil AG, Cichota R, Guo J, Johnstone P, George M, Liu J, Malcolm B, Khaembah E, Meiyalaghan S, Richards K, Zyskowski R, Michel A, Sood A, Tait A, Ewert F. Understanding spatial and temporal variability of N leaching reduction by winter cover crops under climate change. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 771:144770. [PMID: 33736187 DOI: 10.1016/j.scitotenv.2020.144770] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 06/12/2023]
Abstract
Winter cover crops are sown in between main spring crops (e.g. cash and forage crops) to provide a range of benefits, including the reduction of nitrogen (N) leaching losses to groundwater. However, the extent by which winter cover crops will remain effective under future climate change is unclear. We assess variability and uncertainty of climate change effects on the reduction of N leaching by winter oat cover crops. Field data were collected to quantify ranges of cover crop above-ground biomass (7 to 10 t DM/ha) and N uptake (70 to 180 kg N/ha) under contrasting initial soil conditions. The data were also used to evaluate the APSIM-NextGen model (R2 from 62 to 96% and RMSEr from 7 to 50%), which was then applied to simulate cover crop and fallow conditions across four key agricultural locations in New Zealand, under baseline and future climate scenarios. Cover crops reduced N leaching risks for all location/scenario combinations but with large variability in space and time (e.g. 21 to 47% of fallow) depending on the climate change scenario. For instance, end-of-century estimates for northern (warmer) locations mostly showed non-significant effects of climate change on cover crop effectiveness and N leaching. In contrast for southern (colder) locations, there was a systematic increase in N leaching risks with climate change intensity despite a concomitant, but less than proportional, increase in cover crop effectiveness (up to ~5% of baseline) due to higher winter yields and N uptake. This implies that climate change may not only modify the geography of N leaching hotspots, but also the extent by which cover crops can locally reduce pollution risks, in some cases requiring complementary adaptive measures. The patchy- and threshold-nature of leaching events indicates that fine spatio-temporal resolutions are better suited to evaluate cover crop effectiveness under climate change.
Collapse
Affiliation(s)
- Edmar Teixeira
- The New Zealand Institute for Plant & Food Research Limited, New Zealand.
| | | | | | - Rogerio Cichota
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Jing Guo
- Manaaki Whenua - Landcare Research, New Zealand
| | - Paul Johnstone
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Michael George
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Jian Liu
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Brendon Malcolm
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Edith Khaembah
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | | | - Kate Richards
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Robert Zyskowski
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Alexandre Michel
- The New Zealand Institute for Plant & Food Research Limited, New Zealand
| | - Abha Sood
- The National Institute of Water and Atmospheric Research (NIWA), New Zealand
| | - Andrew Tait
- The National Institute of Water and Atmospheric Research (NIWA), New Zealand
| | - Frank Ewert
- The Leibniz Centre for Agricultural Landscape Research (ZALF), Germany
| |
Collapse
|
7
|
Farina R, Sándor R, Abdalla M, Álvaro-Fuentes J, Bechini L, Bolinder MA, Brilli L, Chenu C, Clivot H, De Antoni Migliorati M, Di Bene C, Dorich CD, Ehrhardt F, Ferchaud F, Fitton N, Francaviglia R, Franko U, Giltrap DL, Grant BB, Guenet B, Harrison MT, Kirschbaum MUF, Kuka K, Kulmala L, Liski J, McGrath MJ, Meier E, Menichetti L, Moyano F, Nendel C, Recous S, Reibold N, Shepherd A, Smith WN, Smith P, Soussana JF, Stella T, Taghizadeh-Toosi A, Tsutskikh E, Bellocchi G. Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils. GLOBAL CHANGE BIOLOGY 2021; 27:904-928. [PMID: 33159712 DOI: 10.1111/gcb.15441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.
Collapse
Affiliation(s)
- Roberta Farina
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | - Renata Sándor
- Centre for Agricultural Research, Agricultural Institute, Martonvásár, Hungary
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand, France
| | | | | | | | | | | | - Claire Chenu
- Université Paris Saclay, INRAE, AgroParisTech, Paris, France
| | - Hugues Clivot
- INRAE, BioEcoAgro, Barenton-Bugny, France
- Université de Lorraine, INRAE, LAE, Colmar, France
| | | | - Claudia Di Bene
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | | | | | | | | | - Rosa Francaviglia
- Research Centre for Agriculture and Environment, CREA - Council for Agricultural Research and Economics, Rome, Italy
| | - Uwe Franko
- Helmholtz Centre for Environmental Research, Halle, Germany
| | - Donna L Giltrap
- Manaaki Whenua - Landcare Research, Palmerston North, New Zealand
| | - Brian B Grant
- Ottawa Research and Development Centre, Agriculture and Agri-Food, Ottawa, ON, Canada
| | - Bertrand Guenet
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
- Laboratoire de Géologie de l'ENS, PSL Research University, Paris, France
| | | | | | - Katrin Kuka
- JKI - Federal Research Centre for Cultivated Plants, Braunschweig, Germany
| | | | - Jari Liski
- Finnish Meteorological Institute, Helsinki, Finland
| | - Matthew J McGrath
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | | | | | - Claas Nendel
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
- University of Potsdam, Potsdam, Germany
| | - Sylvie Recous
- Université de Reims Champagne Ardenne, INRAE, FARE, Reims, France
| | | | - Anita Shepherd
- University of Aberdeen, Aberdeen, UK
- formerly Rothamsted Research, North Wyke, UK
| | - Ward N Smith
- Ottawa Research and Development Centre, Agriculture and Agri-Food, Ottawa, ON, Canada
| | | | | | - Tommaso Stella
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | | | - Elena Tsutskikh
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | - Gianni Bellocchi
- Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, Clermont-Ferrand, France
| |
Collapse
|
8
|
Zheng J, Wang W, Liu G, Ding Y, Cao X, Chen D, Engel BA. Towards quantification of the national water footprint in rice production of China: A first assessment from the perspectives of single-double rice. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 739:140032. [PMID: 32758949 DOI: 10.1016/j.scitotenv.2020.140032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 05/27/2020] [Accepted: 06/04/2020] [Indexed: 06/11/2023]
Abstract
Rice is one of the most important crops in China, contributing to approximately 28% of total cereal yield. Despite substantial production, given that rice is a high water-consuming crop, the water shortage due to the irreversible decline in available water resources on a global scale induced by undergoing climate change will pose grave challenges to rice reproductive growth and related water resources utilization. As a consequence, investigating the responses of rice productivity and water consumption to more pronounced climate changes is of great significance for water resources sustainable utilization in terms of reducing irrigation water requirements and ensuring food security. Present water footprint (WF) methods do not calculate the weighted average of each WF component at the national level when evaluating the effects of prospective climate change upon rice production. The national water footprint (NWF), i.e. taking the share of each province in the total production of crops as weighting factors, has been regarded as an effective approach to determine where each WF component is originally located. In this study, the temporal change characteristics of NWF for single-rice (SR), early-rice (ER) and late-rice (LR) in different agro-ecological zones across China during 2001-2010 were assessed for the first time. The results exhibited that NWF of rice was an estimated 304,848 million cubic meters (MCM) per year. The SR accounted for the greatest portion of NWF, followed by ER and LR. The NWF rank was SR-V > SR-I > ER-VI > SR-IV > LR-III > LR-VI > SR-II > ER-III. The blue water footprint (WFb) presents decreasing trends in most agro-ecological zones (SR-I, SR-II, SR-IV, ER-III and LR-VI), while green water footprint (WFg) exhibits increasing trends within these regions. This study provides a beneficial approach for decision-making processes aiming at better agricultural water resources management strategies to alleviate water resources scarcity and reduce food risk in the context of surging demand, which will support agricultural water resources management of China towards a more balanced direction at the national level.
Collapse
Affiliation(s)
- Jiazhong Zheng
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Weiguang Wang
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
| | - Guoshuai Liu
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Yimin Ding
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Xinchun Cao
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environment in Southern China of Ministry of Education, Hohai University, Nanjing, Jiangsu 210098, China; College of Agricultural Sciences and Engineering, Hohai University, Nanjing 210098, China
| | - Dan Chen
- Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environment in Southern China of Ministry of Education, Hohai University, Nanjing, Jiangsu 210098, China; College of Agricultural Sciences and Engineering, Hohai University, Nanjing 210098, China
| | - B A Engel
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47906, USA
| |
Collapse
|
9
|
Liu H, Pequeno DN, Hernández-Ochoa IM, Krupnik TJ, Sonder K, Xiong W, Xu Y. A consistent calibration across three wheat models to simulate wheat yield and phenology in China. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109132] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
10
|
A Spatial Analysis Framework to Assess Responses of Agricultural Landscapes to Climates and Soils at Regional Scale. INNOVATIONS IN LANDSCAPE RESEARCH 2020. [DOI: 10.1007/978-3-030-37421-1_25] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
11
|
Kipling R, Topp C, Bannink A, Bartley D, Blanco-Penedo I, Cortignani R, del Prado A, Dono G, Faverdin P, Graux AI, Hutchings N, Lauwers L, Özkan Gülzari Ş, Reidsma P, Rolinski S, Ruiz-Ramos M, Sandars D, Sándor R, Schönhart M, Seddaiu G, van Middelkoop J, Shrestha S, Weindl I, Eory V. To what extent is climate change adaptation a novel challenge for agricultural modellers? ENVIRONMENTAL MODELLING & SOFTWARE : WITH ENVIRONMENT DATA NEWS 2019; 120:104492. [PMID: 31787839 PMCID: PMC6876672 DOI: 10.1016/j.envsoft.2019.104492] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 06/10/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
Collapse
Affiliation(s)
- R.P. Kipling
- Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion, SY23 3EE, UK
| | | | - A. Bannink
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - D.J. Bartley
- Disease Control, Moredun Research Institute, Pentlands Science Park, Bush Loan, Penicuik, EH26 0PZ, UK
| | - I. Blanco-Penedo
- Swedish University of Agricultural Sciences, Department of Clinical Sciences, SE-750 07, Uppsala, Sweden
- IRTA, Animal Welfare Subprogram, ES-17121, Monells, Girona, Spain
| | - R. Cortignani
- Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University, Viterbo, Italy
| | - A. del Prado
- Basque Centre for Climate Change (BC3), Edificio Sede Nº 1, Planta 1, Parque Científico de UPV/EHU, Barrio Sarriena s/n, 48940, Leioa, Bizkaia, Spain
| | - G. Dono
- Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University, Viterbo, Italy
| | - P. Faverdin
- PEGASE, Agrocampus Ouest, INRA, Saint-Gilles, 35590, France
| | - A.-I. Graux
- PEGASE, Agrocampus Ouest, INRA, Saint-Gilles, 35590, France
| | - N.J. Hutchings
- Department of Agroecology, Aarhus University, Postbox 50, Tjele, 8830, Denmark
| | - L. Lauwers
- Flanders Research Institute for Agriculture, Fisheries and Food, Merelbeke, Belgium
- Department of Agricultural Economics, Ghent University, Ghent, Belgium
| | - Ş. Özkan Gülzari
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
- Department of Animal and Aquacultural Sciences, Faculty of Veterinary Medicine and Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway
- Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway
| | - P. Reidsma
- Plant Production Systems, Wageningen University & Research, P.O. Box 430, Wageningen, 6700 AK, the Netherlands
| | - S. Rolinski
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A31, D-14473, Potsdam, Germany
| | - M. Ruiz-Ramos
- Universidad Politécnica de Madrid, CEIGRAM-ETSIAAB, 28040, Madrid, Spain
| | - D.L. Sandars
- School of Water, Energy, and Environment (SWEE), Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK
| | - R. Sándor
- Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Brunszvik u 2, Martonvásár, H-2462, Hungary
| | - M. Schönhart
- Institute for Sustainable Economic Development, BOKU University of Natural Resources and Life Sciences, Feistmantelstraße 4, 1180, Vienna, Austria
| | - G. Seddaiu
- Desertification Research Centre and Dept. Agricultural Sciences, Univ. Sassari, Sassari, Italy
| | - J. van Middelkoop
- Wageningen Livestock Research, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | | | - I. Weindl
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A31, D-14473, Potsdam, Germany
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany
| | - V. Eory
- SRUC, West Mains Rd, Edinburgh, EH9 3JG, UK
| |
Collapse
|