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Publisher: Elsevier   (Total: 3163 journals)

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Showing 1 - 200 of 3163 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 95, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 36, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 413, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 251, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 152, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 23, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 32, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 29, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 24)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 44, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 58, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 22)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 11)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 9)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 63)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 1, SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 397, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 10, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 33, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 343, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 451, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 3)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 52, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 28, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 46)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 209, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 64, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 174, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 194, SJR: 1.58, CiteScore: 3)

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Journal Cover
Agricultural and Forest Meteorology
Journal Prestige (SJR): 1.818
Citation Impact (citeScore): 5
Number of Followers: 17  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0168-1923
Published by Elsevier Homepage  [3163 journals]
  • Modeling soil evaporation and the response of the crop coefficient to leaf
           area index in mature Populus tomentosa plantations growing under different
           soil water availabilities
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Nan Di, Ye Wang, Brent Clothier, Yang Liu, Liming Jia, Benye Xi, Haixiang Shi In order to improve water management in Populus tomentosa plantations, the variation in the FAO-56 parameters of the basal crop coefficient (Kcb) and soil evaporation (Es) in mature P. tomentosa plantations under different soil water availability treatments were investigated over two growing seasons. Empirical models for predicting Es in well-watered drip irrigated stands were constructed and validated. Changes in the relationship between Kcb and leaf area index (LAI) during the growing seasons and differing soil water availabilities, were also studied. The Kcb in all treatments increased rapidly to its maximum in about mid-May, and then decreased and remained relatively constant until late August or September. The Kcb increased sharply with increasing LAI, but plateaued when the LAI reached a critical value. With increasing stand age, the limit of Kcb of all treatments increased, while the critical values of the LAI declined markedly. In contrast, these two values declined with decreasing soil water availability, but the difference in Kcb limit could disappear with increasing stand age. Distinct spatial heterogeneity in Es appeared only from April to June, during which the Es from the wet soil zone accounted for 66% of the total Es on average. Among the five empirical models, the L-ww (constructed model based on LAI data during the “well-watered” period) and LT-ww (constructed model based on data of LAI multiplied by soil temperature at 20 cm depth during the “well-watered” period) models had the lowest Es prediction errors (RMSE of 0.25 and 0.20 mm d−1), and the highest modelling efficiency (0.49 and 0.63) and index of agreement (0.84 and 0.88) for the “whole year” and “well-watered” periods, respectively. In conclusion, for predicting stand transpiration more accurately using a crop coefficient model, the quantitative relationship between LAI and Kcb needs to be adjusted for stand age and soil water availability. Spatial heterogeneity in Es should be considered when estimating Es in drip irrigated plantations of P. tomentosa and other tree species.
  • Upscaling soil-atmosphere CO2 and CH4 fluxes across a topographically
           complex forested landscape
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Daniel L. Warner, Mario Guevara, Shreeram Inamdar, Rodrigo Vargas Upscaling soil-atmosphere greenhouse gas (GHG) fluxes across complex landscapes is a major challenge for environmental scientists and land managers. This study employs a quantile-based digital soil mapping approach for estimating the spatially continuous distributions (2 m spatial resolution) and uncertainties of seasonal mean mid-day soil CO2 and CH4 fluxes. This framework was parameterized using manual chamber measurements collected over two years within a temperate forested headwater watershed. Model accuracy was highest for early (r2 = 0.61) and late summer (r2 = 0.64) for CO2 and CH4 fluxes. Model uncertainty was generally lower for predicted CO2 fluxes than CH4 fluxes. Within the study area, predicted seasonal mean CO2 fluxes ranged from 0.17 to 0.58 μmol m−2 s−1 in winter, and 1.4 to 5.1 μmol m−2 s−1 in early summer. Predicted CH4 fluxes across the study area ranged from −0.52 to 0.02 nmol m−2 s−1 in winter, and −2.1 to 0.61 nmol m−2 s−1 in early and late summer. The models estimated a per hectare net GHG potential ranging from 0.44 to 4.7 kg CO2 eq. hr−1 in winter and early summer, with an estimated 0.4 to 1.5% of emissions offset by CH4 uptake. Flux predictions fell within ranges reported in other temperate forest systems. Soil CO2 fluxes were more sensitive to seasonal temperature changes than CH4 fluxes, with significant temperature relationships for soil CO2 emissions and CH4 uptake in pixels with high slope angles. In contrast, soil CH4 fluxes from flat low-lying areas near the stream network within the watershed were significantly correlated to seasonal precipitation. This study identified key challenges for modeling high spatial resolution soil CO2 and CH4 fluxes, and suggests a larger spatial heterogeneity and complexity of underlying processes that govern CH4 fluxes.
  • Ensemble forecasting of monthly and seasonal reference crop
           evapotranspiration based on global climate model outputs
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Tongtiegang Zhao, Quan J. Wang, Andrew Schepen, Morwenna Griffiths Long-range forecasts of climatic variables are generated by climate centres around the world using global climate models (GCMs). This paper investigates ensemble forecasting of reference crop evapotranspiration (ETo) based on GCM outputs. The Penman-Monteith formula is used to calculate raw forecasts of ETo from GCM forecasts of solar radiation, temperature, wind speed, and vapor pressure. The Bayesian joint probability (BJP) modelling approach is applied to post-process raw monthly forecasts, separately for different lead times (month 1, 2 and 3 ahead). The Schaake shuffle is then employed to link the ensemble members of post-processed forecasts for all lead times to give a temporal structure. Forecasts of seasonal ETo total are obtained by aggregating the monthly forecasts. For comparison purposes, seasonal forecasts are also derived directly by post-processing raw seasonal forecasts without going through the monthly steps. Three case studies are presented for post-processing forecasts from the Australian Community Climate and Earth System Simulator-Seasonal (ACCESS-S1). Both raw forecasts and observations of monthly and seasonal ETo are found to be reasonably normally distributed. The post-processed forecasts of monthly and seasonal ETo are skilful in reference to climatology forecasts and statistically reliable in ensemble spread. The indirect and direct ways of generating forecasts of seasonal ETo total show similar skill and reliability, demonstrating the effectiveness of the Schaake shuffle. In this paper, the proposed post-processing method is evaluated through leave-one-out cross validation. The method can be easily adapted for post-processing raw GCM forecasts in real-time to produce ensemble forecasts of monthly and seasonal ETo.
  • Growth response of alpine treeline forests to a warmer and drier climate
           on the southeastern Tibetan Plateau
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Chunming Shi, Miaogen Shen, Xiuchen Wu, Xiao Cheng, Xiaoyan Li, Tianyi Fan, Zongshan Li, Yuandong Zhang, Zexin Fan, Fangzhong Shi, Guocan Wu Forest growth at high altitudes and latitudes is sensitive to climate warming. However, warming-induced drought stress has decreased forest growth and survival rates, and constitutes a key uncertainty in projections of forest ecosystem dynamics. A fast warming rate has occurred over the Tibetan Plateau (TP), and the response pattern of alpine forest growth on the TP to a warmer and possibly drier climate is still unknown. By compiling tree-ring width records from ten alpine treeline ecotones (ATEs), we developed an index of regional tree growth in ATEs (RTGA) on the southeastern TP, which is a major forested region of the TP. Our results showed a stable and clear coherence between RTGA and the regional summer (June-August) minimum temperature during the studied period (1950–2012, R2 = 0.59, P 
  • Implications of crop model ensemble size and composition for estimates of
           adaptation effects and agreement of recommendations
    • Abstract: Publication date: Available online 9 October 2018Source: Agricultural and Forest MeteorologyAuthor(s): A. Rodríguez, M. Ruiz-Ramos, T. Palosuo, T.R. Carter, S. Fronzek, I.J. Lorite, R. Ferrise, N. Pirttioja, M. Bindi, P. Baranowski, S. Buis, D. Cammarano, Y. Chen, B. Dumont, F. Ewert, T. Gaiser, P. Hlavinka, H. Hoffmann, J.G. Höhn, F. Jurecka Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.
  • N2O flux measurements over an irrigated maize crop: A
           comparison of three methods
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): T. Tallec, A. Brut, L. Joly, N. Dumelié, D. Serça, P. Mordelet, N. Claverie, D. Legain, J. Barrié, T. Decarpenterie, J. Cousin, B. Zawilski, E. Ceschia, F. Guérin, V. Le Dantec This paper presents the NitroCOSMES campaign, aimed at testing and evaluating the performance of three methods for monitoring N2O fluxes over an agricultural field. The experiment was conducted from May to August 2012 at a site located in the south-west of France. N2O fluxes from a 24 ha irrigated maize field were measured using eddy covariance (EC), automated chamber (AC) and static chamber (SC) methodologies. Uncertainties were calculated according to the specificities of each set-up. Measurements were performed over a large range of water-filled pore spaces (WFPS), soil temperatures, and mineral nitrogen availability, and offered the opportunity to compare methodologies over a wide range of N2O emission intensities. The average N2O fluxes were compared among the three methodologies during the same periods of measurement and for different intensities of emissions (low, moderate and high). Periods of comparison were determined according to the AC results. On average, the three methods gave comparable results for the low (SC: 14.7 ± 2.2, EC: 15.7 ± 10.1, AC: 17.5 ± 1.6 ng N2O-N m−² s−1) and the high (SC: 131.7 ± 22.1, EC: 125.3 ± 8, AC: 125.1 ± 8.9 ng N2O-N m−² s−1) N2O emission ranges. For the moderate N2O emission range, AC measurements gave higher emissions (57.2 ± 3.9 ng N2O-N m−² s−1) on average than both the SC (41.6 ± 6.6 ng N2O-N m−² s−1) and EC (33.8 ± 3.9 ng N2O-N m−² s−1) methods, which agreed better with each other. The relative standard deviation coefficient (RSD) indicated that EC methodology gave highly variable values during periods of low N2O emissions, from -52.2 ± 88.1 to 62.2 ± 50.7 ng N2O-N m−² s−1, with a mean RSD of 151%. Water vapour effects (dilution and spectroscopic cross-sensitivity) were discussed in an attempt to explain the high variability in low N2O emission measurements. Even after applying the Webb term correction, there could still be a spectroscopic cross-sensitivity effect of water vapour on the N2O trace gas signal because of the layout of the analysers, which was not determined during the experiment. This study underlined that EC methodology is a promising way to estimate and refine N2O budgets at the field scale and to analyse the effects of different agricultural practices more finely with continuous flux monitoring. It also highlighted the need to continue the effort to assess and develop chambers and EC methodologies, especially for the low N2O emission measurement range, for which values and systematic uncertainties remain high and highly variable.
  • Evaluation and calibration of a high-resolution soil moisture product for
           wildfire prediction and management
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Vinodkumar, Imtiaz Dharssi Soil moisture deficit is a key variable used in operational fire prediction and management applications. In Australia, operational fire management practices use simple, empirical water balances models to estimate soil moisture deficit. The Bureau of Meteorology has recently developed a prototype, high-resolution, land surface modelling based, state-of-the-art soil moisture analyses for Australia. The present study examines this new product for use in operational fire prediction and management practices in Australia. The approach used is twofold. First, the new soil moisture product is evaluated against observations from ground based networks. Among the results, the mean Pearson’s correlation for surface soil moisture across the three in-situ networks is found to be between 0.78 and 0.85. Secondly, the study evaluate a few different calibration methods to facilitate the ready utilization of the new soil moisture product in the current operational fire prediction framework. The calibration approaches investigated here are: minimum-maximum matching, mean-variance matching and, cumulative distribution function matching. Validation of the calibrated products using extended triple collocation technique shows that the minimum-maximum method has the highest skill. Evaluation of the calibrated products against MODIS fire radiative power data highlights that large fires correspond to a drier soil in minimum-maximum outputs compared to other calibration results and the current operational method.
  • Spatio-temporal downscaling of gridded crop model yield estimates based on
           machine learning
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): C. Folberth, A. Baklanov, J. Balkovič, R. Skalský, N. Khabarov, M. Obersteiner Global gridded crop models (GGCMs) are essential tools for estimating agricultural crop yields and externalities at large scales, typically at coarse spatial resolutions. Higher resolution estimates are required for robust agricultural assessments at regional and local scales, where the applicability of GGCMs is often limited by low data availability and high computational demand. An approach to bridge this gap is the application of meta-models trained on GGCM output data to covariates of high spatial resolution. In this study, we explore two machine learning approaches – extreme gradient boosting and random forests - to develop meta-models for the prediction of crop model outputs at fine spatial resolutions. Machine learning algorithms are trained on global scale maize simulations of a GGCM and exemplary applied to the extent of Mexico at a finer spatial resolution. Results show very high accuracy with R2>0.96 for predictions of maize yields as well as the hydrologic externalities evapotranspiration and crop available water with also low mean bias in all cases. While limited sets of covariates such as annual climate data alone provide satisfactory results already, a comprehensive set of predictors covering annual, growing season, and monthly climate data is required to obtain high performance in reproducing climate-driven inter-annual crop yield variability. The findings presented herein provide a first proof of concept that machine learning methods are highly suitable for building crop meta-models for spatio-temporal downscaling and indicate potential for further developments towards scalable crop model emulators.
  • Response of crop yield to different time-scales of drought in the United
           States: Spatio-temporal patterns and climatic and environmental drivers
    • Abstract: Publication date: 15 January 2019Source: Agricultural and Forest Meteorology, Volume 264Author(s): Marina Peña-Gallardo, Sergio M. Vicente-Serrano, Steven Quiring, Marc Svoboda, Jamie Hannaford, Miquel Tomas-Burguera, Natalia Martín-Hernández, Fernando Domínguez-Castro, Ahmed El Kenawy This article presents an analysis of the response of the annual crop yield in five main dryland cultivations in the United States to different time-scales of drought, and explores the environmental and climatic characteristics that determine the response. For this purpose we analysed barley, winter wheat, soybean, corn and cotton. Drought was quantified by means of the Standardized Precipitation Evapotranspiration Index (SPEI). The results demonstrate a strong response in the interannual variability of crop yields to the drought time-scales in the different cultivations. Moreover, the response is highly spatially variable. Crop types showed considerable differences in the month in which their yields are most strongly linked to drought conditions. Some crops (e.g. winter wheat) responded to drought at medium to long SPEI time-scales, while other crops (e.g. soybean and corn) responded to short or long drought time-scales. The study confirms that the differences in the patterns of crop yield response to drought time-scales are mostly controlled by average climate conditions, in general, and water availability (precipitation), in particular. Generally, we found that there is a weaker link between crop yield and drought severity in humid environments and also that the response tends to occur over longer time-scales.
  • Optical-based and thermal-based surface conductance and actual
           evapotranspiration estimation, an evaluation study in the North China
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Xiaolong Hu, Liangsheng Shi, Lin Lin, Baozhong Zhang, Yuanyuan Zha Accurate estimation of surface conductance (Gs) and evapotranspiration (ET) from remote sensing data has received increasing interest, but the data interpretation method requires further development. The objective of this study is to evaluate the capability of optical and thermal information to quantify Gs and ET in the frame of the Penman-Monteith model. We evaluated the three remote sensing data-based retrievals of daily Gs and ET using Moderate Resolution Imaging Spectroradiometer (MODIS) data and eddy covariance measurements at three sites in the North China Plain. The Gs models were established on the basis of (1) single vegetation index (VI), including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), (2) temperature vegetation dryness index (TVDI), and (3) combination of VI and TVDI. The results demonstrated that the combination of NDVI and theoretical TVDI achieved the best accuracy of quantifying Gs and ET. The single VI-based model also performed well. The empirical TVDI-based model failed to estimate Gs and ET since there existed significant uncertainties in the calculation of the dry and wet edge. In contrast, the theoretical TVDI with an apparent seasonal pattern was of more value to acquire Gs and ET due to its explicit physical mechanism. From this study, the combination of VI and TVDI, as well as single VI, were recommended to build alternative approaches to acquiring ET. These Gs models highly rely on remote sensing data and thus show promising potential in regional-scale application.
  • Cotton yield prediction with Markov Chain Monte Carlo-based simulation
           model integrated with genetic programing algorithm: A new hybrid
           copula-driven approach
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Mumtaz Ali, Ravinesh C. Deo, Nathan J. Downs, Tek Maraseni Reliable data-driven models designed to accurately estimate cotton yield, an important agricultural commodity, can be adopted by farmers, agricultural system modelling experts and agricultural policy-makers in strategic decision-making processes. In this paper a hybrid genetic programing model integrated with the Markov Chain Monte Carlo (MCMC) based Copula technique is developed to incorporate climate-based inputs as the predictors of cotton yield, for selected study regions: Faisalabad (31.4504 °N, 73.1350 °E), Multan (30.1984 °N, 71.4687 °E) and Nawabshah (26.2442 °N, 68.4100 °E), as important cotton growing hubs in the developing nation of Pakistan. Several different types of GP-MCMC-copula models were developed, each with the well-known copula families (i.e., Gaussian, student t, Clayton, Gumble Frank and Fischer-Hinzmann functions) to screen and utilize an optimal cotton yield forecast model for the present study region. The results of the GP-MCMC based hybrid copula model were evaluated with a standalone GP and the MCMC based copula model in accordance with statistical analysis of the predicted yield based on correlation coefficient (r), Willmott’s index (WI), Nash-Sutcliffe coefficient (NSE), root mean squared error (RMSE) and mean absolute error (MAE) in the independent test phase. Further performance preciseness was evaluated by the Akiake Information Criterion (AIC), the Bayesian Information Criterion (BIC) and the Maximum Likelihood (MaxL) for the GP-MCMC based copula as well as the MCMC based copula model. GP-MCMC-Clayton copula model generated the most accurate result for the Multan station. For the optimal GP-MCMC-Clayton copula model, the acquired model evaluation metrics for Multan were: (LM≈0.952; RRMSE≈2.107%; RRMAE≈1.771%) followed by the MCMC based Gaussian copula model (LM≈0.895; RRMSE≈4.541%; RRMAE≈0.3.214%) and the standalone GP model (LM≈0.132; RRMSE≈23.638%; RRMAE≈22.652%), indicating the superiority of the GP-MCMC-Clayton copula model in respect to the other benchmark models. The performance of GP-MCMC based copula model was also found to be superior in the case of Faisalabad and Nawabshah station as confirmed by AIC, BIC, MaxL metrics, including a larger value of the Legates-McCabe’s (LM) index, utilized in conjunction with the relative percentage RRMSE and the relative mean absolute error (RMAE). Accordingly, it is averred that the developed GP-MCMC copula model can be considered as a pertinent data-intelligent tool used for accurate prediction of cotton yield, utilizing the readily available climate datasets in agricultural regions and is of relevance to agricultural yield simulation and sectoral decision-making.
  • Physiological drought responses improve predictions of live fuel moisture
           dynamics in a Mediterranean forest
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Rachael H. Nolan, Javier Hedo, Carles Arteaga, Tetsuto Sugai, Víctor Resco de Dios The moisture content of live fuels is an important determinant of forest flammability. Current approaches for modelling live fuel moisture content typically focus on the use of drought indices. However, these have mixed success partly because of species-specific differences in drought responses. Here we seek to understand the physiological mechanisms driving changes in live fuel moisture content, and to investigate the potential for incorporating plant physiological traits into live fuel moisture models. We measured the dynamics of leaf moisture content, access to water resources (through stable isotope analyses) and physiological traits (including leaf water potential, stomatal conductance, and cellular osmotic and elastic adjustments) across a fire season in a Mediterranean mixed forest in Catalonia, NE Spain. We found that differences in both seasonal variation and minimum values of live fuel moisture content were a function of access to water resources and plant physiological traits. Specifically, those species with the lowest minimum moisture content and largest seasonal variation in moisture (Cistus albidus: 49–137% and Rosmarinus officinalis: 47–144%) were most reliant on shallow soil water and had the lowest values of predawn leaf water potential. Species with the smallest variation in live fuel moisture content (Pinus nigra: 96–116% and Quercus ilex: 56–91%) exhibited isohydric behaviour (little variation in midday leaf water potential, and relatively tight regulation of stomata in response to soil drying). Of the traits measured, predawn leaf water potential provided the strongest predictor of live fuel moisture content (R2 = 0.63, AIC = 249), outperforming two commonly used drought indices (both with R2 = 0.49, AIC = 258). This is the first study to explicitly link fuel moisture with plant physiology and our findings demonstrate the potential and importance of incorporating ecophysiological plant traits to investigating seasonal changes in fuel moisture and, more broadly, forest flammability.
  • Ratooning as an adaptive management tool for climatic change in rice
           systems along a north-south transect in the southern Mississippi valley
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Lewis H. Ziska, David H. Fleisher, Steve Linscombe The effect of climate change on recent and projected increases in surface temperatures is well-documented. For agriculture, such changes can impact crop phenology and production, but the degree of impact will depend, in part, on contemporaneous changes in crop management. In the current study, we quantified recent (last 40 years) and projected (to 2095) changes in air temperature and associated changes in growing season duration for rice along a latitudinal north-south gradient of the lower Mississippi valley. Recent and projected climate data indicated an ongoing increase in air temperature and growing season length with latitudes above ∼31 °N. We then applied the DD50 growing degree day model to these data to determine if ratooning, a management practice that produces a second rice harvest with minimal resource input, could be employed. The model results were analyzed and used relative to the southernmost location, Cameron Parish, where the season length and daily temperatures currently allow for ratooning to be a common practice for long-grain cultivars (e.g., Cocodrie, Catahoula). The recent and projected increases in temperature and seasonality indicate that ratooning could already be adopted in Avoyelles Parish, and is potentially possible as far north as Cape Girardeau County (37 °N) by the end of the 21 st century. While additional information regarding possible effects of heat stress, water availability, rising carbon dioxide (CO2) levels, and other factors will be necessary to fully assess ratooning potential, our research indicated that ongoing increases in temperature and season length may allow agronomic management practices, such as ratooning, to help adapt rice production to climatic uncertainty.
  • Implications of structural diversity for seasonal and annual carbon
           dioxide fluxes in two temperate deciduous forests
    • Abstract: Publication date: Available online 21 September 2018Source: Agricultural and Forest MeteorologyAuthor(s): Rijan Tamrakar, Mark B. Rayment, Fernando Moyano, Martina Mund, Alexander Knohl The effects of structural diversity on the carbon dioxide exchange (CO2) of forests has become an important area of research for improving the predictability of future CO2 budgets. We report the results of a paired eddy covariance tower study with 11 years of data on two forest sites of similar mean stand age, near-identical site conditions, and dominated by beech trees (Fagus sylvatica), but with a very different stand structure (incl. age, diameter distribution, stocks of dead wood and species composition) because of different management regimes. Here we address the question of how management and related structural diversity may affect CO2 fluxes, and tested the hypothesis that more structurally diverse stands are less sensitive to variations in abiotic and biotic drivers. Higher annual net ecosystem productivity (NEP) was observed in the managed, even-aged, and homogenous forest (585 ± 57.8 g C m−2 yr−1), than in the unmanaged, uneven-aged, and structurally diverse forest (487 ± 144 g C m−2 yr−1). About two-third of the difference in NEP between the sites was contributed by a higher annual gross primary productivity (GPP, 1627 ± 164 vs 1558 ± 118 g C m−2 yr−1) and one-third by a lower annual ecosystem respiration (Reco, 1042 ± 60 vs 1071 ± 96 g C m−2 yr−1) in the homogenous forest. Spring (April – May) and summer (June – July) were the two main seasons contributing to the overall annual differences between the sites, also, the sensitivities of seasonal NEP and GPP to environmental variables were stronger in the homogenous forest during those periods. Inter-annual variation of NEP was higher in the homogenous forest (coefficient of variation (CV) = 25%) compared to the heterogeneous forest (CV = 12%). At annual time scale, the higher variability of NEP in the homogenous forest is attributed to biotic factors such as fruit production and a time-dependent growth trend, outweighing differences in environmental sensitivities.
  • Correction of anisotropy effects on penta-needle heat-pulse probe sap-flux
           density and thermal property measurements
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Sheng Wang, Jun Fan, Scott B. Jones Growing interest in methods for estimating plant stem/trunk sap-flux density and thermal properties include the use of heated needles inserted into the plant. A penta-needle heat-pulse probe (PHPP) coupled with an on-chip integrated INV-WATFLX algorithm was newly developed for inverse estimation of isotropic porous media thermal-diffusivity, κ, -conductivity, λ, and heat velocity, Vh (converted to water-flux density, J), thus heat capacity, C (=λ/κ), and water content could also be derived. This integrated sensor, however, has yet to be applied in anisotropic sapwood sensing. Here, we conducted a numerical simulation of the PHPP heat pulse and a deviation analysis when using an INV-WATFLX code developed by Yang and Jones [Comput. Geosci.—UK. 35 (2009) 2250] in anisotropic porous media. Deviations in J were up to +40% and as low as -30%, and within 12% in κ, λ and C at static conditions for varied PHPP installation angles, α, in sapwood. We developed a correction of anisotropy effects, and followed up with a field test of the sensors installed on standing poplar (Populus simonii Carr.) trees using α = 0°, 15° and 30°. Field tests showed the corrected J estimated using PHPPs at α = 15° and 30° both agreed well with J from thermal dissipation probes (TDPs) in 1:1 line (R2 = 0.87 and 0.83, P 
  • Climate change impact on Mexico wheat production
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Ixchel M. Hernandez-Ochoa, Senthold Asseng, Belay T. Kassie, Wei Xiong, Ricky Robertson, Diego Notelo Luz Pequeno, Kai Sonder, Matthew Reynolds, Md Ali Babar, Anabel Molero Milan, Gerrit Hoogenboom Wheat is one of the most important cereal crops in Mexico, but the impact of future climate change on production is not known. To quantify the impact of future climate change together with its uncertainty, two wheat crop models were executed in parallel, using two scaling methods, five Global Climate Models (GCMs) and two main Representative Concentration Pathways (RCPs) for the 2050s. Simulated outputs varied among crop models, scaling methods, GCMs, and RCPs; however, they all projected a general decline in wheat yields by the 2050s. Despite the growth-stimulating effect of elevated CO2 concentrations, consistent yield declines were simulated across most of the main wheat growing regions of Mexico due to the projected increase in temperature. Exceptions occurred in some cooler areas, where temperature improved sub-optimal conditions, and in a few areas where rainfall increased, but these increases only provided negligible contributions to national production. Larger and more variable yield declines were projected for rainfed wheat due to current and projected spatial variability of temperature and rainfall patterns. Rainfed wheat, however, only contributes about 6% of Mexico’s wheat production. When aggregating the simulated climate change impacts, considering temperature increase, rainfall change, and elevated atmospheric CO2 concentrations for irrigated and rainfed wheat cropping systems, national wheat production for Mexico is projected to decline between 6.9% for RCP 4.5 and 7.9% for RCP 8.5. Model uncertainty (combined for crop and climate models) in simulated yield changes, and across two scaling methods, was smaller than temporal and spatial variability in both RCPs. Spatial variability tends to be the largest in both future scenarios. To maintain or increase future wheat production in Mexico, adaptation strategies, particularly to increasing temperatures affecting irrigated wheat, or expanding the cropping area, will be necessary.
  • Wind tunnel study of airflow recovery on the lee side of single plants
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Hong Cheng, Kaidi Zhang, Chenchen Liu, Xueyong Zou, Liqiang Kang, Tianle Chen, Weiwei He, Yi Fang Plants play an important role on reducing the soil erosion rate and preventing blown sand motion. The primary cause is the airflow change around the plant, especially for the lee side of plants. Although scientist have researched this topic, significant problems remain concerning airflow around plants. Therefore, we conducted a series of wind tunnel experiments to simulate average airflow speed and turbulence intensities on the lee side of eight single plants with varying characteristics under different shear velocities by utilizing a hot film anemometer. We come to the following conclusions:(1) Variation in the airflow speed along the plant downwind direction is related to the porosity and the height-to-width ratio (H/W). The weakened degree of wind speed decreases with plant porosity, and the minimum wind speeds (umin) at different heights are different for different H/W. For large H/W (H/W ≥ 2), the values of umin appear at the location of 1 H in the lee side of the plant, while the location where umin occurs for small H/W (H/W ≤ 0.5) is related to the height. The location most frequently occurs between 3 h and 5 h.(2) This paper presented a modification for relaxation equation to express airflow recovery on the lee side of plant and developed the relationships of the minimal wind speed (umin), occurring lee-side location (x0), and the characteristic length (l) in this modified relation equation, with different plant characteristic. The value of umin increases with the plant porosity (β) in a linear function of umin = 0.0183β-0.65 and the location (x0) where umin occurs and the characteristic length for wind speed recovery are proportional to the reciprocal of the ratio of plant height-to-width. Their relationships can be expressed as x0 = 1.68(H/W)−1 and l = 5.30(H/W)−1, respectively.(3) The turbulence intensity downwind direction of the plant is several times the intensity of the incoming flow, and the peak turbulence intensity can reach up to 50%. The more significantly the wind speed weakens, the more significant the increase in the turbulence intensity. The standard deviation of the wind speed varies slightly.
  • Estimation of forest leaf area index using terrestrial laser scanning data
           and path length distribution model in open-canopy forests
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Yiming Chen, Wuming Zhang, Ronghai Hu, Jianbo Qi, Jie Shao, Dan Li, Peng Wan, Chen Qiao, Aojie Shen, Guangjian Yan Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.
  • Sources of uncertainty in gross primary productivity simulated by light
           use efficiency models: Model structure, parameters, input data, and
           spatial resolution
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Yi Zheng, Li Zhang, Jingfeng Xiao, Wenping Yuan, Min Yan, Tong Li, Zhiqiang Zhang Accurate estimation of gross primary productivity (GPP) is essential for understanding ecosystem function and global carbon cycling. However, there is still substantial uncertainty in the magnitude, spatial distribution, and temporal dynamics of GPP. Using light use efficiency (LUE) models, we conducted a comprehensive analysis of the uncertainty in GPP estimation resulting from various sources: model structure, model parameters, input data, and spatial resolution. We first evaluated the influences of model structures, namely the fraction of absorbed photosynthetically active radiation (FPAR), water scalar (WS), and temperature scalar (TS), on site-level GPP estimates. We then used the Sobol’ sensitivity analysis to quantify the relative contributions of model input variables to the uncertainty in GPP. In addition, we used different land cover and meteorological datasets to examine the effects of input data and spatial resolution on the magnitude and spatiotemporal patterns of GPP. We found that the model structures affected not only model performance but also model parameters in a manner that differed with vegetation type and region. Thus, proper model structures and rigorous model parameterization and calibration should be adopted in GPP modeling. The Sobol’ sensitivity analysis showed that the meteorological drivers including photosynthetically active radiation (PAR) and daily minimum temperature (TMIN) had larger contribution to the uncertainty in simulated GPP than did the surface reflectance-based indices including enhanced vegetation index (EVI) and normalized difference water index (NDWI). At the regional scale, different land cover datasets had the largest impacts on GPP simulations, especially in heterogeneous areas, followed by the scale effects from different spatial resolutions; changing meteorological datasets had the smallest effects. Therefore, more accurate and finer-resolution land cover maps and meteorological datasets are essential for more accurate GPP estimates. Our findings have implications for improving our understanding of the full uncertainty in carbon flux estimates and reducing the uncertainty in carbon cycle simulations.Graphical abstractGraphical abstract for this article
  • Evapotranspiration partitioning at the ecosystem scale using the stable
           isotope method—A review
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Wei Xiao, Zhongwang Wei, Xuefa Wen Terrestrial evapotranspiration (ET) consists of evaporation (E) from canopy-intercepted water, evaporation from soil and open water, and transpiration (T) from plants. Determining the contribution of T to ET (hereafter T/ET) is challenging but necessary for improving water resource management and understanding the response of ecosystem water/energy budgets to climate change. Water stable isotopes provide unique information on ecosystem processes and can be used to partition evapotranspiration at the ecosystem scale. In this paper, the aim is to review the state of the science on the isotope method for ecosystem ET partitioning, with a focus on uncertainties related to estimating the three isotopic end members (isotopic compositions of ET, T and E). The published results show larger T/ET variations during the growing season in croplands due to water management and rapid leaf area index (LAI) changes compared to in other natural ecosystems. Another robust result is that on average, grasslands have lower T/ET than woodlands. The isotopic composition of ET is provided by measurements, while the isotopic compositions of T and E are generally obtained using the Craig-Gordon model with appropriate modifications. Significant advances have been made in the techniques for estimating the isotopic composition of ET, largely due to the availability of fast-responding instruments for in situ measurements of water vapor isotopic composition. The largest source of uncertainty in the T/ET estimation comes from uncertainties in the isotopic composition of ET. Based on published results of the uncertainties in the three end members, we estimate that a typical uncertainty range for T/ET is ±21% (one standard deviation). This review provides background information and theoretical references for studies on isotopic hydrology, ecosystem processes and climate change.
  • The surface-atmosphere exchange of carbon dioxide in tropical rainforests:
           Sensitivity to environmental drivers and flux measurement methodology
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Zheng Fu, Tobias Gerken, Gabriel Bromley, Alessandro Araújo, Damien Bonal, Benoît Burban, Darren Ficklin, Jose D. Fuentes, Michael Goulden, Takashi Hirano, Yoshiko Kosugi, Michael Liddell, Giacomo Nicolini, Shuli Niu, Olivier Roupsard, Paolo Stefani, Chunrong Mi, Zaddy Tofte, Jingfeng Xiao, Riccardo Valentini Tropical rainforests play a central role in the Earth system by regulating climate, maintaining biodiversity, and sequestering carbon. They are under threat by direct anthropogenic impacts like deforestation and the indirect anthropogenic impacts of climate change. A synthesis of the factors that determine the net ecosystem exchange of carbon dioxide (NEE) at the site scale across different forests in the tropical rainforest biome has not been undertaken to date. Here, we study NEE and its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), across thirteen natural and managed forests within the tropical rainforest biome with 63 total site-years of eddy covariance data. Our results reveal that the five ecosystems with the largest annual gross carbon uptake by photosynthesis (i.e. GEP > 3000 g C m−2 y-1) have the lowest net carbon uptake – or even carbon losses – versus other study ecosystems because RE is of a similar magnitude. Sites that provided subcanopy CO2 storage observations had higher average magnitudes of GEP and RE and lower average magnitudes of NEE, highlighting the importance of measurement methodology for understanding carbon dynamics in ecosystems with characteristically tall and dense vegetation. A path analysis revealed that vapor pressure deficit (VPD) played a greater role than soil moisture or air temperature in constraining GEP under light saturated conditions across most study sites, but to differing degrees from -0.31 to -0.87 μmol CO2 m−2 s-1 hPa-1. Climate projections from 13 general circulation models (CMIP5) under the representative concentration pathway that generates 8.5 W m−2 of radiative forcing suggest that many current tropical rainforest sites on the lower end of the current temperature range are likely to reach a climate space similar to present-day warmer sites by the year 2050, warmer sites will reach a climate not currently experienced, and all forests are likely to experience higher VPD. Results demonstrate the need to quantify if and how mature tropical trees acclimate to heat and water stress, and to further develop flux-partitioning and gap-filling algorithms for defensible estimates of carbon exchange in tropical rainforests.
  • Attributing the energy imbalance by concurrent lysimeter and eddy
           covariance evapotranspiration measurements
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Peter Widmoser, Georg Wohlfahrt The commonly observed lack of energy balance closure at eddy covariance flux tower sites represents an outstanding problem in micrometeorology and significantly compromises the value of eddy covariance latent and sensible heat flux measurements. Here we used concurrent lysimeter and eddy covariance evapotranspiration measurements to correct for the energy imbalance attributable to the eddy covariance latent heat flux measurements (32%) and then, by assuming that the Bowen ratio is correctly quantified by the eddy covariance method, attributed the remainder of the energy balance to the sensible heat flux (10%) and the available energy (58%). We discuss our findings with respect to the ongoing discussion on the causes of the energy imbalance and approaches to force energy balance closure.
  • Improving leaf area index (LAI) estimation by correcting for clumping and
           woody effects using terrestrial laser scanning
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Xi Zhu, Andrew K. Skidmore, Tiejun Wang, Jing Liu, Roshanak Darvishzadeh, Yifang Shi, Joe Premier, Marco Heurich Leaf area index (LAI) has frequently been measured in the field using traditional optical methods such as digital hemispherical photography (DHP). However, in the DHP retrieved LAI, there is always contribution of woody components due to the difficulty in distinguishing woody and foliar materials. In addition, the leaf angle distribution which strongly affects the estimation of LAI is either ignored while using the convergent angle 57.5°, or inversed simultaneously with LAI using multiple directions. Terrestrial laser scanning (TLS) provides a 3-dimensional view of the forest canopy, which we used in this study to improve LAI estimation by directly retrieving leaf angle distribution, and subsequently correcting foliage clumping and woody effects. The leaf angle distribution was retrieved by estimating the angle between the leaf normal vectors and the zenith vectors. The clumping index was obtained by using the gap size distribution method, while the woody contribution was evaluated based on an improved point classification between woody and foliar materials. Finally, the gap fraction derived from TLS was converted to effective LAI, and thence to LAI. The study was conducted for 31 forest plots including deciduous, coniferous and mixed plots in Bavarian Forest National Park. The classification accuracy was improved by approximately 10% using our method. Results showed that the clumping caused an underestimation of LAI ranging from 1.2% to 48.0%, while woody contribution led to an overestimation from 3.0% to 31.9% compared to the improved LAI. The combined error ranged from −46.2% to 32.6% of the leaf area index (LAI) measurements. The error was largely dependent on forest types. The clumping index of coniferous plots on average was lower than that of deciduous plots, whereas deciduous plots had a higher woody-to-total area ratio. The proposed method provides a more accurate estimate of LAI by eliminating clumping and woody effects, as well as the effect of leaf angle distribution.
  • Response to comments by Hoffmann et al. on “Upland grasslands in
           Northern England were atmospheric carbon sinks regardless of management
    • Abstract: Publication date: Available online 10 September 2018Source: Agricultural and Forest MeteorologyAuthor(s): Samuel Eze, Sheila M. Palmer, Pippa J. Chapman Hoffmann et al. suspected a likely overestimation of carbon (C) sink reported in our paper (Eze et al., 2018) entitled “Upland grasslands in Northern England were atmospheric carbon sinks regardless of management regime”. They attributed this to potential sources of error associated with the estimation of C fluxes from closed-chamber measurements. We have explained why we think that the C sink reported in our paper was not overestimated as suspected by Hoffmann et al. However, we acknowledge the potential for error due to unavoidable operational and data limitations.
  • On the calculation of daytime CO2 fluxes measured by automated closed
           transparent chambers
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Peng Zhao, Albin Hammerle, Matthias Zeeman, Georg Wohlfahrt Automated transparent chambers have gained increasing popularity in recent years to continuously measure net CO2 fluxes between low-statured canopies and the atmosphere. In this study, we carried out four field campaigns with chamber measurements in a variety of mountainous grasslands. A mathematic stationary point (or critical point, a point at which the derivative of a function is zero) in the CO2 mixing ratio time series was found in a substantial fraction of the measurements at all the sites, which had a significant influence on the performances of the regression algorithms. The stationary point was probably due to condensed water on the inner wall of the chamber dome, which reduced the solar radiation and resulted in a reversal of the CO2 mixing ratio time series in the chamber (so called Clouded-Glass Effect or CGE in this study). This effect may be the cause of the observed underestimation of daytime CO2 fluxes when using common linear and exponential regression models on continuous automated chamber observations. In order to avoid biased flux estimation of daytime CO2 fluxes, we introduced a linearly increasing term to the exponential function so as to compensate for the influence of the CGE, which gives acceptable model errors and improves the CO2 flux estimation by 5% for temperate mountainous grasslands. We conclude that exponential regression models should be favoured over linear models and recommend to account for the effects of CGE by either excluding ambiguous observations from the flux computations where stationary points can be identified in the CO2 mixing ratio time series, or by adding a linearly increasing term to the exponential regression model.
  • Coupling transversal and longitudinal models to better predict Quercus
           petraea and Pinus sylvestris stand growth under climate change
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Patrick Vallet, Thomas Perot Climate change has swept away the former general principles of long-term stability in forest productivity. New types of models are needed to predict growth and to plan forest management under future climate conditions. These models must remain robust for silvicultural practices and variations in climate. In this study, we present a new type of model development to achieve these goals.Our study focused on pure and mixed stands of Quercus petraea and Pinus sylvestris in central France. We used National Forest Inventory (NFI) data: respectively, 525 and 548 pure plots of Quercus petraea and Pinus sylvestris, and 68 plots of mixed species. We also used 108 tree cores from an experimental site of the same species. The cores cover the period from 1971 to 2013, making a total of 4572 individual annual increments.We coupled two types of models. One was developed with NFI data (transversal data). This model takes into account mean diameter and stand density effects on stand growth. It includes a set of biophysical factors accounting for stand fertility. The other one was developed with the data from tree cores (longitudinal data), and provides a climate modulation thanks to the correlation between ring width and yearly climate. The model with tree core data reveals the influence of December to July rainfalls on yearly variability in stand growth for Quercus petraea and of May to August rainfalls for Pinus sylvestris.We obtained a coupled model that allowed us to project growth up to 2100 for all the different IPCC scenarios but one; the model was outside its area of validity beyond 2060 for the RCP 8.5 scenario.Graphical abstractModulation in basal area growth with climate for Quercus petraea according to three IPCC scenarios for the Orleans Forest area. Black dots correspond to IPCC historical climate values, colored dots correspond to modulation in basal area growth for three IPCC climate projections. The dashed line corresponds to smoothed model extrapolations for scenario RCP 8.5.Graphical abstract for this article
  • Evaluation of SVM, ELM and four tree-based ensemble models for predicting
           daily reference evapotranspiration using limited meteorological data in
           different climates of China
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Junliang Fan, Wenjun Yue, Lifeng Wu, Fucang Zhang, Huanjie Cai, Xiukang Wang, Xianghui Lu, Youzhen Xiang Accurate estimation of reference evapotranspiration (ET0) is of great importance for the regional water resources planning and irrigation scheduling design. The FAO-56 Penman-Monteith model is recommended as the reference model to predict ET0, but its application is commonly restricted by lack of complete meteorological data at many worldwide locations. This study evaluated the potential of machine learning models, particularly four relatively simple tree-based assemble algorithms (i.e. random forest (RF), M5 model tree (M5Tree), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost)), for estimating daily ET0 with limited meteorological data using a K-fold cross-validation method. For assessment of the tree-based models in terms of prediction accuracy, stability and computational costs, these models were further compared with their corresponding support vector machine (SVM) and extreme learning machine (ELM) models. Four input combinations of daily maximum and maximum temperature (Tmax and Tmin), relative humidity (Hr), wind speed (U2), global and extra-terrestrial solar radiation (Rs and Ra) with Tmax, Tmin and Ra as the base dataset were considered using meteorological data during 1961–2010 from eight representative weather stations in different climates of China. The results showed that, when lack of complete meteorological data, the machine learning models using Tmax, Tmin, Hr, U2 and Ra obtained satisfactory ET0 estimates in the temperate continental, mountain plateau and temperate monsoon zones of China (RMSE 
  • An integrated simulation-assessment study for optimizing wind barrier
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Hui Fang, Xiaoxu Wu, Xueyong Zou, Xiaofan Yang Wind barriers are artificial structures that are widely built to abate wind erosion by reducing wind velocity near surface, which requires optimal design in aeolian engineering. Previous studies have shown that numerical simulation is an effective method for optimal design of wind barriers. However, there still exist two challenging questions: 1) how to resolve fine-scale airflow fields around barriers? and 2) how to systematically evaluate the shelter efficiency? In the current study, we have conducted high-resolution 3D computational fluid dynamics (CFD) simulations for airflow passing through wind barriers then explored optimal designs. To validate the simulation results, we compared the simulated airflow results with those from wind-tunnel measurement. Moreover, we innovatively proposed a shelter index to evaluate the shelter efficiency, which has taken wind velocity reduction, economical cost and shelter degree into account. According to the calculated shelter index, wind barriers with porosity of 0.3–0.4 could provide the longest effective shelter distance, and a 2-row-a-belt scheme with inter-row spacing of 5–7h (h as the height of wind barriers) is the most effective. The optimal inter-belt spacing is suggested as 12–15h depending on local wind velocity. This study is intended to provide design references for constructing wind barriers in aeolian engineering.Graphical abstractGraphical abstract for this article
  • Contribution of leaf specular reflection to canopy reflectance under black
           soil case using stochastic radiative transfer model
    • Abstract: Publication date: Available online 31 August 2018Source: Agricultural and Forest MeteorologyAuthor(s): Bin Yang, Yuri Knyazikhin, Haimeng Zhao, Yuzhong Ma Numerous canopy radiative transfer models have been proposed based on the assumption of “ideal bi-Lambertian leaves” with the aim of simplifying the interactions between photons and vegetation canopies. This assumption may cause discrepancy between the simulated and measured canopy bidirectional reflectance factor (BRF). Few studies have been devoted to evaluate the impacts of such assumption on simulation of canopy BRF at a high-to-medium spatial resolution (∼30 m). This paper focuses on quantifying the contribution of leaf specular reflection on the estimation of canopy BRF under a black soil case using one of the most efficient radiative transfer models, the stochastic radiative transfer model. Analyses of field and satellite data collected over the boreal Hyytiälä forest in Finland show that leaf specular reflection may lead to errors of up to 33.1% at 550 nm and 32.8% at 650 nm in terms of relative root mean square error. The results suggest that, in order to minimize these errors, leaf specular reflection should be accounted for in modeling BRF.
  • Larval crowding during an insect outbreak reduces herbivory pressure on
           preferred shrubs in a warmer environment
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Javier G.P. Gamarra, Terry V. Callaghan, Helena Bylund, Dylan Gwynn-Jones With warming climate many species are predicted to shift their distributions toward the poles. However, climate change models developed to predict species distributions do not always incorporate interactions between them. The northerly shift of the boreal forest and associated dwarf shrub communities will be directly affected by warming. But warming will also indirectly affect plant communities via impacts on the intensity and frequency of associated insect outbreaks. We present a general model exploring plant host herbivory in response to the balance between insect crowding, host consumption and climate. We examined how these factors dictate the feeding preference of Epirrita autumnata larvae during an outbreak on dwarf shrub vegetation in Sub-arctic Fennoscandia. Data were collected from an outdoor experiment investigating future climate change scenarios (elevated CO2 and temperature) on the dwarf shrub community that included deciduous (Vaccinium myrtillus) and evergreen species (V. vitis-idaea and Empetrum nigrum). We observed that larval crowding was independent of treatment under outbreak conditions. We also tested and confirmed model predictions that larvae would prefer monospecific stands of either deciduous shrubs or its evergreen competitors. For current climate conditions, larvae had a preference to consume more deciduous shrubs in mixed stands. However, at elevated temperature bilberry consumption and herbivore pressure was lower, particularly in mixed stands. Our results show that during future warming, E. autumnata herbivory could promote the success of thermophile deciduous species and possible northward migration. Insect behaviour and preferences should therefore be considered when predicting future vegetation movements responding to warming.
  • Warming exerts greater impacts on subsoil than topsoil CO2
           efflux in a subtropical forest
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Weisheng Lin, Yiqing Li, Zhijie Yang, Christian P. Giardina, Jinsheng Xie, Shidong Chen, Chengfang Lin, Yakov Kuzyakov, Yusheng Yang How warming affects the magnitude of CO2 fluxes within the soil profile remains an important question, with implications for modeling the response of ecosystem carbon balance to changing climate. Information on belowground responses to warming is especially limited for the tropics and subtropics because the majority of manipulative studies have been conducted in temperate and boreal regions. We examined how artificial warming affected CO2 gas production and exchange across soil profiles in a replicated mesocosms experiment relying on heavily weathered subtropical soils and planted with Chinese fir (Cunninghamia lanceolata). Half of 2 × 2 m mesocosms (5 replications) was heated with cables buried at a 10 cm depth, which increased temperature in the whole soil profile by 4.5, 3.6 and 2.5 °C at 15, 30 and 60 cm soil depths, respectively. Using a combination of chamber-based and concentration gradient method (CGM) approaches, we found that warming increased soil CO2 efflux across the whole profile by 40%. Changes were unevenly distributed across soil depth: mean CO2 production rate decreased from 0.74 to 0.67 μmol CO2 m−2 s−1 in topsoils (0–15 cm depth) whereas it increased from 0.26 to 0.73 μmol CO2 m−2 s−1 in subsoils (15–60 cm depth). Warming reduced moisture more strongly in subsurface than surface soils and increased subsoil soluble N concentrations as well as fine root turnover, in line with previous temperate and boreal warming studies. This consistency indicates that overall responses of subtropical forests to warming may be similar to forests in higher latitudes.
  • Plastic film mulching improved rhizosphere microbes and yield of rainfed
           spring wheat
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Ying Zhu, Yinglong Chen, Xiaofang Gong, Yinan Peng, Zhiye Wang, Bin Ji Background and aimsPlastic film mulching (PFM) is critical for agricultural production in arid and semi-arid areas in the world. There is an evidence that PFM alters soil microbial populations and soil nutrients. However, how PFM altering rhizosphere microorganisms and nutrients with plant growth remain unknown. We investigated the changes of rhizosphere soil microbial metabolic characteristics in response to PFM management, and its consequent effects on soil nutrients, plant growth and yield of wheat.MethodsA field experiment of a local spring wheat cultivar Lunchun 8275 was carried out at a typical semi-arid area on the Loess Plateau. Wheat plants were treated with or without PFM, and measured for rhizosphere cultural microbial populations and microbial metabolic activities at jointing, flowering and maturity stages, respectively.ResultsRhizosphere cultural microbial populations and nutrient contents were significantly altered possibly due to the improvement of soil thermal and water status under the PFM treatment. The results of cultural microbial populations were consistent with the principal components analysis of microbial metabolic activities. PFM changed the linear regression coefficients between cultural microbial populations and nutrients, microbial metabolic activities and nutrients with 0.67 and 0.20 respectively, but with −0.24 and −0.37 in CK. Meanwhile, wheat grain yield increased by 19.2% and water use efficiency enhanced by 40.7% under PFM.ConclusionsThis study demonstrated that PFM improved rhizosphere micro-environment, including soil thermal and water status, rhizosphere nutrients, cultural microbial populations and their metabolic activities, thereby increased crop yield. The present study might enhance our understanding the influence of PFM on the rhizosphere microbes and their roles in nutrient acquisition and plant growth improvement.
  • Impact of agricultural water-saving practices on regional
           evapotranspiration: The role of groundwater in sustainable agriculture in
           arid and semi-arid areas
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Hang Chen, Zailin Huo, Xiaoqin Dai, Suying Ma, Xu Xu, Guanhua Huang Evapotranspiration (ET) is an important component of the water budget process and is characterized by complex spatiotemporal changes, especially in irrigated agricultural areas. The impact of various hydrological processes and human activities on ET is still a meaty theme to study and investigate. A typical agricultural irrigation district with shallow groundwater and arid climate conditions was selected as the case study area in this work. The impact of the supplied irrigation water, shallow groundwater, crop planting pattern, and weather conditions on regional ET was determined after the regional ET was estimated by a Surface Energy Balance Algorithm of Land (SEBAL) model with Moderate Resolution Imaging Spectroratiometer (MODIS) data. The results show that the regional ET in Hetao kept declining in the past 15 years. The positive correlation between the water input (water diversion and precipitation) and ET indicated that reduced water diversion controls the declining ET, also causing the drop of groundwater level. Due to capillary forces and root uptake, the shallow groundwater tended to move upward to support the crop water consumption because the soil suffered from a water deficit. Furthermore, we quantified the contribution of shallow groundwater to regional ET and found that the water supplied from shallow groundwater increased from 5% to 15% during the period of water–saving irrigation. However, the long–term decrease of irrigation water supply and groundwater level caused a soil water deficit over the crop growth period, and the variation of crop planting pattern reduced ET as well. Therefore, groundwater plays an important role in sustainable agricultural development in arid and semiarid areas and the contribution of shallow groundwater to regional water consumption cannot be neglected.
  • Interactive effects of seasonal drought and nitrogen deposition on carbon
           fluxes in a subtropical evergreen coniferous forest in the East Asian
           monsoon region
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Pan Li, Li Zhang, Guirui Yu, Congqiang Liu, Xiaoli Ren, Honglin He, Min Liu, Huimin Wang, Jianxing Zhu, Rong Ge, Na Zeng Subtropical forests in the East Asian monsoon region function as considerable carbon sinks in the Northern Hemisphere. Forest ecosystems in this region have experienced intensified seasonal drought that has limited their carbon sequestration capacity, but increasing atmospheric nitrogen deposition has contrarily enhanced their capacity to act as carbon sinks. Understanding and quantifying the interactive effects of seasonal drought and nitrogen deposition on the carbon sequestration of subtropical forests is of great significance for accurately predicting future changes to the terrestrial carbon cycle. In this study, we used the Community Land Model Version 4.5 (CLM4.5) to investigate how carbon fluxes, i.e. gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem productivity (NEP), respond to seasonal drought and nitrogen deposition in an evergreen coniferous forest in southern China. Our results showed that reduced GPP during the drought in the summers of 2003 and 2007 weakened the forest’s carbon sequestration capacity. The reduction in GPP mainly occurred at the sunlit canopy due to its higher sensitivity to soil water stress, and non-stomatal limitations played an important role in limiting leaf photosynthesis. The enhanced NEP by nitrogen deposition was attributed to increased plant growth, which could, in turn, be attributed to increases in leaf area. Interactions of seasonal drought and nitrogen deposition varied with drought severity. Interactive effects of the two drivers on GPP, Re, and NEP were additive under mild and moderate drought conditions but non-additive under severe drought. Their net effects on NEP shifted from +29% under mild and moderate drought conditions to -56% under severe drought. Our study highlights the importance of accounting for the interactive effects of seasonal drought and nitrogen deposition in assessing the carbon sequestration of subtropical forest ecosystems in the East Asian monsoon region.
  • Transpiration deficits increase host susceptibility to bark beetle attack:
           Experimental observations and practical outcomes for Ips typographus
           hazard assessment
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Bradley Matthews, Sigrid Netherer, Klaus Katzensteiner, Josef Pennerstorfer, Emma Blackwell, Patrick Henschke, Peter Hietz, Sabine Rosner, Per-Erik Jansson, Helmut Schume, Axel Schopf The projected increase in the frequency and severity with which bark beetle disturbances occur is forecasted to be partially driven by increases in drought episodes. Drought is widely considered to predispose host conifer trees to bark beetle attack; however, experimental data supporting this hypothesis are scarce. This study revisits the Rosalia Roof Project, the first throughfall manipulation experiment to investigate how attack by the Eurasian spruce bark beetle (Ips typographus) on mature Norway spruce (Picea abies) trees is affected by drought stress. Using the in situ “attack box” method, this study explores whether increased host acceptance by I. typographus and/or reduced host defense against attack coincide with increased tree transpiration deficits (i.e. the reduction from a potential transpiration caused by soil water limitation). To estimate transpiration deficits of the respective control and drought stress-induced (full-cover) trees, sap flow measurements were combined with simulations from a simple forest water balance routine. The model, which was calibrated against in situ hydrological measurements, has been developed for a hazard rating tool (PHENIPS-TDEF) which simulates both potential I. typographus phenology and tree drought stress in Norway spruce stands. While host acceptance appeared unaffected by tree transpiration deficits, acute and chronic transpiration deficits did lead to reduced host defense. Full cover trees for instance, which experienced an estimated 93 mm transpiration deficit in the previous May-Sep, could only defend against 70% of attacks. However, similar defended attack percentages on the full-cover and control trees during late summer demonstrate the difficulty in deriving simple stress proxy-infestation risk relationships. The experiment therefore highlights the utility and limitations of transpiration deficits within I. typographus disturbance models and hazard assessment tools, such as PHENIPS-TDEF.
  • Using high-resolution simulated climate projections in forest
           process-based modelling
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): J.H.N. Palma, R.M. Cardoso, P.M.M. Soares, T.S. Oliveira, M. Tomé Forest management decisions often rely on forest growth process based models. These models require climate data at a time-scale and a time-frame that is frequently not available in the area of interest. With the purpose of evaluating the use of modelled climate as a replacement for observational data, we compared the performance (efficiency, precision and bias) of a forest growth process based model (3-PG) when the inputs of the observational climate data were replaced by modelled climate data. Based on previous research, we focused on two promising regional climate models: 1) the Regional Atmospheric Climate Model (RACMO) and 2) the Weather Research and Forecast Modelling System and Program (WRF).Results suggest that when using simulated climate data there are minor losses of performance in the forest growth model predictions with a general growth overestimation, with RACMO providing the best results. A deeper analysis suggests that improving the temperature accuracy of the model will reduce the overestimation of the predictions.The use of simulated climate data with RACMO and WRF is therefore recommended when observed climate is scarce or inexistent. The use of these datasets can certainly widen the usage of forest growth process based models, improving the support for decision-making in forest management, especially when considering climate change, one of the cornerstones for which modelled climate is developed.
  • Modelling reference evapotranspiration using a new wavelet conjunction
           heuristic method: Wavelet extreme learning machine vs wavelet neural
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Ozgur Kisi, Meysam Alizamir Evapotranspiration is an important parameter in linking ecosystem functioning, climate and carbon feedbacks, agricultural management, and water resources. This study investigates the applicability of wavelet extreme learning machine (WELM) model which uses discrete wavelet transform and ELM methods in estimating daily reference evapotranspiration (ET0). Various combination of climatic data of temperature, solar radiation, relative humidity and wind speed from two stations, Ankara and Kirikkale, located in central Anatolia region of Turkey were used as inputs to the WELM models. The WELM estimates were compared with wavelet artificial neural networks (WANN) and single artificial neural network (ANN), ELM and online sequential ELM (OS-ELM) models. The results indicate that the models comprising four input variables as inputs provide better accuracy than the models with less inputs. Solar radiation was found to be the most effective variable on ET0. Wavelet conjunction models (e.g. WELM and WANN) generally show better accuracy compared to the single models and WELM model is found to be the best model in estimating ET0. The root mean square error and mean relative error accuracies of the ELM, ANN and WANN models were improved by 28–25%, 32–32% and 27–26% for the Ankara Station and by 14–14%, 58–58% and 32–36% for the Kirikkale Station.
  • Sensitivity of simulated crop yield and nitrate leaching of the
           wheat-maize cropping system in the North China Plain to model parameters
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Mohamed Jabloun, Xiaoxin Li, Xiying Zhang, Fulu Tao, Chunsheng Hu, Jørgen E. Olesen Process-based crop simulation models are often over-parameterised and are therefore difficult to calibrate properly. Following this rationale, the Morris screening sensitivity method was carried out on the DAISY model to identify the most influential input parameters operating on selected model outputs, i.e. crop yield, grain nitrogen (N), evapotranspiration and N leaching. The results obtained refer to the winter wheat-summer maize cropping system in the North China Plain. In this study, four different N fertiliser treatments over six years were considered based on a randomised field experiment at Luancheng Experimental Station to elucidate the impact of weather and nitrogen inputs on model sensitivity. A total of 128 parameters were considered for the sensitivity analysis. The ratios [output changes/parameter increments] demonstrated high standard deviations for the most relevant parameters, indicating high parameter non-linearity/interactions. In general, about 34 parameters influenced the outputs of the DAISY model for both crops. The most influential parameters depended on the output considered with sensitivity patterns consistent with the expected dominant processes. Interestingly, some parameters related to the previous crop were found to affect output variables of the following crop, illustrating the importance of considering crop sequences for model calibration. The developed RDAISY toolbox used in this study can serve as a basis for following sensitivity analysis of the DAISY model, thus enabling the selection of the most influential parameters to be considered with model calibration.
  • Direct and carry-over effects of summer rainfall on ecosystem carbon
           uptake and water use efficiency in a semi-arid woodland
    • Abstract: Publication date: 15 December 2018Source: Agricultural and Forest Meteorology, Volume 263Author(s): Qiaoqi Sun, Wayne S. Meyer, Petra Marschner Biological activity in semi-arid and arid ecosystems is strongly dependent on rainfall, particularly in summer. A period of favourable rainfall can alter ecosystem carbon balance and is likely influence inner-annual variability of the regional and global carbon cycle. The effect of rainfall variability on ecosystem carbon and water fluxes in semi-arid ecosystems, particularly in woody ecosystems has not been adequately investigated. In this study, we used eddy covariance data from four springs (September–November), four summers (December of that year–February of the following year) and three following autumns (March–May) between 2010 and 2013 in a semi-arid woodland of southern Australia to better understand the effect of pre-summer, summer and post-summer rainfall variability on diurnal pattern of carbon flux. In 2010/11 summer, La Niῆa conditions resulted in extensive rainfall, which marked an historic record over the last 100 years. The 2011/12 summer was also moist. In contrast, the two following summers (2012/13 and 2013/14) were dry. Cumulative net ecosystem productivity (NEP) was lower in dry summers than in moist summers, due to lower maximum carbon flux rate and total hours of net carbon uptake. Maximum NEP and gross primary productivity rates on a given day were reached earlier in dry summers, indicating that photosynthetic activity was not suppressed by high temperatures but by water availability. Ecosystem water use efficiency, calculated as the ratio of daily NEP to evapotranspiration, was higher in moist than dry summers. In addition, the effect of summer rain extended into the following autumn. Cumulative NEP and ecosystem water use efficiency in autumn following a dry summer were lower than when following a moist summer. We conclude that summer rainfall has a strong impact on the carbon cycle in semi-arid woodlands due to its direct and carry-over effect. Therefore seasonal rainfall variation is likely to determine inter-annual variability of annual net carbo uptake of this ecosystem.
  • Comment on “Upland grasslands in Northern England were atmospheric
           carbon sinks regardless of management regime” by Eze et al.
    • Abstract: Publication date: Available online 31 July 2018Source: Agricultural and Forest MeteorologyAuthor(s): Mathias Hoffmann, Vytas Huth, Jürgen Augustin
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