Advances in agronomy volume 76

253 48 0
  • Loading ...
    Loading ...
    Loading ...

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

Tài liệu liên quan

Thông tin tài liệu

Ngày đăng: 08/05/2019, 16:02

Agronomy DVANCES I N VOLUME 76 Advisory Board Martin Alexander Ronald Phillips Cornell University University of Minnesota Kenneth J Frey Kate M Scow Iowa State University University of California, Davis Larry P Wilding Texas A&M University Prepared in cooperation with the American Society of Agronomy Monographs Committee Lisa K Al-Almoodi David D Baltensperger Warren A Dick Jerry L Hatfield John L Kovar Diane E Stott, Chairman David M Kral Jennifer W MacAdam Matthew J Morra Gary A Pederson John E Rechcigl Diane H Rickerl Wayne F Robarge Richard Shibles Jeffrey Volenec Richard E Zartman Agronomy DVANCES IN VOLUME 76 Edited by Donald L Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware San Diego San Francisco New York Boston London Sydney Tokyo This book is printed on acid-free paper Copyright C ∞ 2002 by ACADEMIC PRESS All Rights Reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher The appearance of the code at the bottom of the first page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of specific clients This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc (222 Rosewood Drive, Danvers, Massachusetts 01923), for copying beyond that permitted by Sections 107 or 108 of the U.S Copyright Law This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale Copy fees for pre-2002 chapters are as shown on the title pages If no fee code appears on the title page, the copy fee is the same as for current chapters 0065-2113/2002 $35.00 Explicit permission from Academic Press is not required to reproduce a maximum of two figures or tables from an Academic Press chapter in another scientific or research publication provided that the material has not been credited to another source and that full credit to the Academic Press chapter is given Academic Press An Elsevier Science Imprint 525 B Street, Suite 1900, San Diego, California 92101-4495, USA http://www.academicpress.com Academic Press Harcourt Place, 32 Jamestown Road, London NW1 7BY, UK http://www.academicpress.com International Standard Book Number: 0-12-000794-0 PRINTED IN THE UNITED STATES OF AMERICA 02 03 04 05 06 07 SB Contents CONTRIBUTORS PREFACE vii ix THE POTENTIAL OF SOILS OF THE TROPICS TO SEQUESTER CARBON AND MITIGATE THE GREENHOUSE EFFECT R Lal I Introduction II Soil-Related Constraints to Biomass Production III Soil Degradation and Emission of Greenhouse Gases to the Atmosphere IV Soil Carbon Pool and Dynamics V Historic Loss of SOC Pool from Soils of the Tropics VI Need for Soil Restoration VII Strategies of Mitigating the Greenhouse Effect through Soil Carbon Sequestration VIII Potential of SOC Sequestration in the Tropics IX Dynamics of Soil Inorganic Carbon X Conclusions References 10 13 14 15 17 23 24 25 APPLICATIONS OF CROP/SOIL SIMULATION MODELS IN TROPICAL AGRICULTURAL SYSTEMS Robin Matthews, William Stephens, Tim Hess, Tabitha Middleton, and Anil Graves I Introduction II Applications of Models III The Way Forward References 32 33 95 108 INTERORGANISMAL SIGNALING IN SUBOPTIMUM ENVIRONMENTS: THE LEGUME–RHIZOBIA SYMBIOSIS F Zhang and D L Smith I Introduction II Symbiotic Nitrogen Fixation and Soil Fertility v 126 127 vi III IV V VI CONTENTS Principles of Legume Nodulation Legume Nodulation under Stressful Conditions Legume Nodulation with Preactivated Rhizobium Commercial Products References 128 138 146 150 153 SURFACE CHEMISTRY AND FUNCTION OF MICROBIAL BIOFILMS M A Chappell and V P Evangelou I Introduction: Definition and Importance of Microbial Biofilms II The Microbial Biofilm as an Interfacial Boundary Regulating Solution Equilibrium III Features and Properties of the Biofilm Surface IV Conclusion References 164 169 177 193 194 CROP SCHEDULING AND PREDICTION—PRINCIPLES AND OPPORTUNITIES WITH FIELD VEGETABLES D C E Wurr, J R Fellows, and K Phelps I Introduction II Identification of Distinct Stages and Phases of Growth and Development III Prediction of Duration of Developmental Phases for Given Temperature Regimes IV Additional Effects of Other Abiotic Factors on the Duration of Developmental Phases V Experimental Approaches to the Construction of Scheduling and Prediction Models VI The Accuracy of Measurement of Abiotic Factors VII Methods of Planning Production VIII Future Opportunities IX Concluding Comments References INDEX 202 205 206 213 216 219 222 228 231 231 235 Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin M A CHAPPELL (163), Department of Agronomy, Iowa State University, Ames, Iowa 50011 V P EVANGELOU (163), Department of Agronomy, Iowa State University, Ames, Iowa 50011 J R FELLOWS (201), Horticulture Research International, Wellesbourne, Warwick CV35 9EF, United Kingdom ANIL GRAVES (31), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, United Kingdom TIM HESS (31), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, United Kingdom R LAL (1), School of Natural Resources, The Ohio State University, Columbus, Ohio 43210 ROBIN MATTHEWS (31), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, United Kingdom TABITHA MIDDLETON (31), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, United Kingdom K PHELPS (201), Horticulture Research International, Wellesbourne, Warwick CV35 9EF, United Kingdom D L SMITH (125), Department of Plant Science, McGill University–Macdonald Campus, Saint Anne de Bellevue, Quebec H9X 3V9, Canada WILLIAM STEPHENS (31), Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, United Kingdom D C E WURR (201), Horticulture Research International, Wellesbourne, Warwick CV35 9EF, United Kingdom F ZHANG (125), Bios Agriculture, Inc., Saint Anne de Bellevue, Quebec H9X 3V9, Canada vii This Page Intentionally Left Blank Preface Volume 76 contains five excellent reviews on topics of great interest to crop and soil scientists as well as to others in various fields Chapter is concerned with the potential of tropical soils to sequester carbon Topics that are covered include: soil inorganic and organic pools and dynamics, loss of soil organic pools from tropical soils, and potential for C sequestration in tropical soils Chapter covers the applications of crop/soil simulation models in tropical agricultural systems Chapter deals with interorganismal signaling in suboptimum environments with emphasis on legume–rhizobia symbiosis Chapter discusses the surface chemistry and function of microbial biofilms The authors discuss biofilm formation and matrix architecture and general features and properties Chapter deals with vegetable crop scheduling and prediction Topics that are covered include: identification of stages of growth and development and experimental approaches for developing scheduling and prediction models I appreciate the authors’ timely and thoughtful reviews DONALD L SPARKS ix 228 D C E WURR ET AL VIII FUTURE OPPORTUNITIES Everaarts (1999), in his review of harvest date prediction, stated that systems will be developed to give more accurate estimates of the amount and quality of produce that will be ready for market on any specific date They will almost certainly need to make use of personal computer-based software and local meteorological data and will involve the use of new models However, for use in commercial situations these will need to be simple, robust, and realistic and are unlikely to have scope for including many parameters In recent years, the use of on-farm computers and meteorological stations has increased substantially, making it easier for information on crop growth and development and meteorological data to be drawn together to produce efficient crop scheduling systems However, currently there is no general software available for planning sequences of production and few algorithms linking production patterns to weather If a model of crop growth is available, then an estimate of harvest date can be made when all the inputs are described Predictions of harvest date will depend upon estimates of “average” temperature which involve some measure of variability, and this may be reduced by monitoring the crop as it develops The use of short- and medium-term weather forecasts should reduce that variability further Ideally, scheduling should be the inverse of that process, with the crop model indicating what sowing/planting dates should be used if a crop is required at time t For large-scale systems, temperature forecast data should be able to provide “better than average” inputs to the model so that schedules can be developed in real time The techniques currently used for crop planning in field vegetables are largely based on local experience and have little scientific basis Every year they result in unnecessary peaks and troughs of product, causing supply problems, which result in price instability, imports of produce, and lack of confidence in the production chain Thus, there are a number of new opportunities for crop scheduling A SOFTWARE FRAMEWORK FOR SCHEDULING Scheduling techniques for planning production sequences, driven by simple weather data and into which modular algorithms for individual crops and crop processes can be introduced, would offer the industry a sound basis for meeting the increasingly sophisticated needs of the market In addition, the interest shown by supermarkets in providing better control of product supply suggests that there must be an opportunity for futuristic software development, providing a “shell” within which more specific modules can be developed Thus, it is now timely to develop a coherent package of work on the scheduling of horticultural crops In addition, new crop schedules will need to be devised CROP SCHEDULING AND PREDICTION 229 as market requirements develop In parallel with this, developments of our understanding of crop physiology and improved forecasts of weather will continue and the horticultural industry will increasingly need to apply these technologies to ensure that crops are grown to meet market needs Many grower organizations now produce at several geographically different locations and need simple, sound scheduling systems to help them match product supply under different environmental conditions to retailer and processor requirements The increasing use of hybrids, with their improved uniformity, means that traditional planning techniques result in enhancement of the peaks and troughs of product supply What is needed are more plantings of different-sized blocks of crop to smooth out that pattern of supply Climate change is currently having disruptive effects on certain species because their developmental processes are temperature-sensitive and small changes from the optimum temperature can have a large effect on the timing of crop stage for harvest It will enable the benefits of improved weather forecasting techniques to be incorporated in production planning at an early stage and will allow accurate forecasts of future weather to be used to modify crop scheduling interactively as the season progresses This will help to maximize U.K production, provide a stable product base, and help those organizations which export produce to so with greater certainty It will allow growers to ask “what if” questions when planning their production so that they can simulate the effects of changes in frequency of planting, in location, and in environmental conditions on their intended production pattern B MODELING SUPPLY FUNCTION One suggestion for research, which could apply to virtually any horticultural crop, would be to develop a dynamic model of product supply over time (see Fig 14) using risk-analysis techniques, physiological information from relevant crops, and a range of scenarios using different types of meteorological data Such a generic model would incorporate improved estimates of weather forecasting It would be adjusted to suit individual crops and products by linking with new and existing models of crop growth and development, and it could significantly improve the planning, prediction, and monitoring of product supply throughout the food chain C PERSONALIZED INFORMATION Individuals in some sectors of the industry recognize the need for better information and techniques for the scheduling of their crops but not want that information to be available to everyone This view might be considered introspective 230 D C E WURR ET AL Figure 14 Updating a forecast with historical data The large arrows are intended to show how, as “current time” moves on, the prediction for harvest time and its corresponding confidence limits can be updated but there is clearly a need for “confidentiality” and there are opportunities for software to be developed so that a grower can use historical data from his or her own crops to produce production schedules for future dynamic use Alternatively, now that the supermarkets effectively dominate and control supply contracts, cooperation between growers might be a better tactic to adopt D CROP TEMPERATURES Undoubtedly, we now need to acquire better information relating crop physiology to measures of the environment and incorporate that information into simple models which can be used to describe, schedule, and predict patterns of crop development As we this, it will need to involve the use of data which accurately represent the physiological and biochemical processes occurring in each crop, rather than standard meteorological measurements There will be a need to use temperatures which accurately reflect the background processes involved, and it will involve the development of relationships between these processes and standard meteorological data This is of particular importance where dynamic models are used and where the temperatures are close to the base or ceiling temperatures CROP SCHEDULING AND PREDICTION 231 IX CONCLUDING COMMENTS It is highly likely that crop prediction systems will become more important in commercial vegetable production Any future developments of scheduling and prediction techniques for field vegetables will require a better understanding of the physiology of the species concerned and how this is affected by environmental factors This information will need to be incorporated within decision-support systems to help growers meet market demands for delivering precise quantities of product at specific times REFERENCES Anderson, W K., Smith, R C G., and McWilliam, J R (1978) A systems approach to the adaptation of sunflower to new environments I Phenology and development Field Crops Res 1, 141– 152 Arnold, C Y (1959) The determination and significance of the base temperature in a linear heat unit system Proc Am Soc Hortic Sci 74, 430–445 Arnold, C Y (1974) Predicting stages of sweet corn (Zea mays L.) development J Am Soc Hortic Sci 99, 501–505 Arnold, C Y (1978) Time patterns for sweet corn development Proceedings of 1978 Illinois vegetable growers school, University of Illinois at Urbana-Champaign, pp 24–27 Atkinson, D., and Porter, J R (1996) Temperature, plant development and crop yields Trends in Plant Sci 1(4), 119–124 Biddle, A J., Knott, C M., and Gent, G P (1988) The PGRO Pea Growing Handbook Processors and Growers Research Organisation, Peterborough, England Bierhuizen, J F., and Feddes, R A (1973) Use of temperature and short wave radiation to predict the rate of seedling emergence and harvest date Acta Hortic 27, 269–277 Booij, R (1987) Environmental factors in curd initiation and curd growth of cauliflower in the field Neth J Agric Sci 35, 435–445 Brewster, J L., Salter, P J., and Darby, R J (1977) Analysis of the growth and yield of overwintered onions J Hortic Sci 52, 335–346 Brown, D M (1960) Soybean ecology I Development—temperature relationships from controlled environment studies Agron J 52, 493–496 Brown, D M (1969) Heat units for corn in southern Ontario Ontario Department of Agriculture and Food Information leaflet Agdex 111/31 Brown, D M., and Bootsma, A (1993) Crop heat units for corn and other warm-season crops in Ontario Ontario Ministry of Agriculture and Food Factsheet Agdex 111/31 Caprio, J M (1966) A statistical procedure for determining the association between weather and non-measurement biological data Agric Meterol 3, 55–72 Carr, M K V (1977) The influence of temperature on the development and yield of maize in Britain Ann Appl Biol 87, 261–266 Cross, H Z., and Zuber, M S (1972) Prediction of flowering dates in maize based on different methods of estimating thermal units Agron J 64, 351–355 Edey, S N (1977) Growing degree-days and crop production in Canada Publication 1635 Canada Department of Agriculture, Ottawa 232 D C E WURR ET AL Everaarts, A P (1999) Harvest date prediction for field vegetables A review Gartenbauwissenschaft 64, 20–25 Fellows, J R., Reader, R J., and Wurr, D C E (1997) A model for leaf production and apex development in calabrese J Hortic Sci 72, 327–337 Ferguson, J H A (1958) Empirical estimation of thermoreaction curves for the rate of development Euphytica 7, 140–146 Finch-Savage, W E., and Phelps, K (1993) Onion (Allium cepa L.) seedling emergence patterns can be explained by the influence of soil temperature and water potential on seed germination J Exp Bot 44, 407–414 Finch-Savage, W E., Steckel, J R A., and Phelps, K (1998) Germination and post-germination growth to carrot seedling emergence: Predictive threshold models and sources of variation between sowing occasions New Phytol 139, 505–516 Garcia-Huidobro, J., Monteith, J L., and Squire, G R (1982) Time, temperature and germination of pearl millet (Pennisetum typhoides S & H.) I Constant temperature J Exp Bot 33, 288– 296 Geslin, H (1958) Les plantes et le climat (quelques aspects particuliers des buts et des m´ethodes de la bioclimatologie agricole) International Society of Bioclimatology and Biometeorology First Bioclimatological Congress Vienna Part II, Section B1, 1–15 Gilmore, E C., and Rogers, J S (1958) Heat units as a method of measuring maturity in corn Agron J 50, 611–615 Gray, D., Tuckwell, M E., and Hargreaves, G (1979) Timing of harvests in summer lettuce: Comparison of methods of scheduling sowings in drilled and transplanted crops Expl Hortic 31, 35–44 Greenwood, D J., Cleaver, T J., Loquens, S M H., and Niendorf, K B (1977) Relationship between plant weight and growing period for vegetable crops in the United Kingdom Ann Bot 41, 987– 997 Grevsen, K., and Olesen, J E (1994) Modelling cauliflower development from transplanting to curd initiation J Hortic Sci 69, 755–766 Hiron, R W., and Symonds, W (1985) Vegetable propagation in cellular trays Ministry of Agriculture Fisheries and Food leaflet 909 Hortik, H J., and Arnold, C Y (1965) Temperature and the rate of development of sweet corn Proc Am Soc Hortic Sci 87, 303–312 Johnson, I R., and Thornley, J H M (1985) Temperature dependence of plant and crop processes Ann Bot 55, 1–24 Kish, A J., and Ogle, W L (1980) Improving the heat unit system in predicting maturity date of snap beans HortScience 15, 140–141 Krug, H., and Liebig, H P (1978) Effect of light and temperature on the growth of radish as a base for timing Acta Hortic 72, 211–215 Lana, E P., and Haber, E S (1952) Seasonal variability as indicated by cumulative degree hours with sweet corn Proc Am Soc Hortic Sci 59, 389–392 Lehenbauer, P A (1914) Growth of maize seedlings in relation to temperature Physiol Res 1, 247–288 Liu, D L., Kingston, G., and Bull, T A (1998) A new technique for determining the thermal parameters of phenological development in sugarcane, including suboptimum and supra-optimum temperature regimes Agric For Meterol 90, 119–139 Madariaga, F J., and Knott, J E (1951) Temperature summations in relation to lettuce growth Proc Am Soc Hortic Sci 58, 147–152 Major, D J (1980) Photoperiod response characteristics controlling flowering of nine crop species Can J Plant Sci 60, 777–784 Martin, M D (1985) A programme for continuity Grower 103(2), 15–19 CROP SCHEDULING AND PREDICTION 233 Meteorological Office (1946) Tables for the evaluation of daily values of accumulated temperature above and below 42◦ F from daily values of maximum and minimum temperature Meteorological Office Leaflet No 10 Meteorological Office, Bracknell, Berkshire, UK Mikkelsen, S A (1981) Predicting the date of harvest of vining peas by means of growing-degree-days models Acta Hortic 122, 211–221 Monteith, J L (1981) Climatic variation and the growth of crops Q J R Meteorol Soc 107, 749–774 Nuttonson, M Y (1948) Some preliminary observations of phenological data as a tool in the study of photoperiodic and thermal requirements of various plant material “Vernalization and photoperiodism A symposium,” pp 129–143 Waltham, Mass Chronica Botanica Parton, W J., and Logan, J A (1981) A model for diurnal variation in soil and air temperature Agric Meteorol 23, 205–216 Pearson, S., Hadley, P., and Wheldon, A E (1994) A model of the effects of temperature on the growth and development of cauliflower (Brassica oleracea L botrytis) Sci Hortic 59, 91–106 Pearson, S., Hadley, P., and LeMiere, P (1998) Crop forecasting in horticulture The Horticulturist 7(2), 2–8 Perry, K B., Sanders, D C., Granberry, D M., Garrett, J T., Decoteau, D R., Nagata, R T., Dufault, R J., Batal, K D., and McLaurin, W J (1993) Heat units, solar radiation and daylength as pepper harvest predictors Agric For Meteorol 65, 197–205 Phelps, K., Collier, R H., Reader, R J., and Finch, S (1993) Monte Carlo simulation method for forecasting the timing of pest insect attacks Crop Protection 12, 335–342 Reader, R J., and Phelps, K (1992) Modelling the development of temperature-dependent processes Genstat Newsletter 28, 27–32 Numerical Algorithms Group Ltd., Oxford, UK Reeves, J., Fellows, J R., Phelps, K., and Wurr, D C E (2001) Development and validation of a model describing the curd induction of winter cauliflower J Hortic Sci Biotech 76, 714–720 Reicosky, D C., Winkelman, L J., Baker, J M., and Baker, D G (1989) Accuracy of hourly air temperatures calculated from daily minima and maxima Agric For Meteorol 46, 193–209 Robertson, G W (1973) Development of simplified agroclimatic procedures for assessing temperature effects on crop development In “Plant Response to Climatic Factors” (R O Slatyer, Ed.) Proceedings of Uppsala Symposium Unesco, Paris Rubatzky, V E., and Yamaguchi, M (1997) “World vegetables: Principles, production and Nutritive Values, 2nd ed.” Chapman & Hall, New York Salter, P J (1972) An adjustable drilling sequence to compensate for adverse weather conditions and to obtain continuous production of vegetable crops Hortic Res 12, 57–63 Salter, P J (1982) Planning continuity of supply In “Know and Grow Vegetables 2” (J K A Bleasdale and P J Salter, Eds.), pp 41–84 Oxford University Press, Oxford, UK Salter, P J., Ward, R J., and Whitwell, J D (1972) Studies on methods of obtaining continuity of production of summer and autumn cauliflowers I Kirton, 1963–1969 Expl Hortic 23, 1–22 Scaife, A., Cox, E F., and Morris, G E L (1987) The relationship between shoot weight, plant density and time during the propagation of four vegetable species Ann Bot 59, 325–334 Schoolfield, R M., Sharpe, P J H., and Magnuson, C E (1981) Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory J Theor Biol 88, 719–731 Sharpe, P J H., and deMichele, D W (1977) Reaction kinetics of poikilotherm development J Theor Biol 64, 649–670 Strandberg, J O (1979) Growth and phenology of cabbage in a winter production area Proc Fl State Hortic Soc 92, 93–96 Summerfield, R J., Roberts, E H., Ellis, R H., and Lawn, R J (1991) Towards the reliable prediction of time to flowering in six annual crops I The development of simple models for fluctuating field environments Expl Agric 27, 11–31 234 D C E WURR ET AL Summerfield, R J., Ellis, R H., Craufurd, P Q., Aiming, Q., Roberts, E H., and Wheeler, T R (1997) Environmental and genetic regulation of flowering of tropical annual crops Euphytica 96, 83–91 Thornley, J H M., and Johnson, I R (2000) Plant and crop modelling A mathematical approach to plant and crop physiology Clarendon Press, Oxford Thornthwaite, C W (1948) An approach toward a rational classification of climate Geogr Rev 38(1), 55–94 Thornthwaite, C W., and Mather, J R (1954) Climate in relation to crops Met Monogr (8), 1–10 Tisserand, F (1875) M´emoire sur la v´eg´etation dans les hautes latitudes M´em Soc Cent Agric Cited by Abbe 1905 Titley, M E (1985) Crop scheduling in broccoli M.S thesis, University of Sydney, Australia Wang, J Y (1960) A critique of the heat unit approach to plant response studies Ecology 41, 785–790 Wurr, D C E (1990) Prediction of the time of maturity in cauliflowers Acta Hortic 267, 387–394 Wurr, D C E (1992) Maturity prediction in calabrese Proceedings of the Second Congress of the European Society for Agronomy pp 214–215 Wurr, D C E., and Fellows, J R (1984) The growth of three crisp lettuce varieties from different sowing dates J Agric Sci Camb 102, 733–745 Wurr, D C E., and Fellows, J R (1998) Leaf production and curd initiation of winter cauliflower in response to temperature J Hortic Sci Biotech 73, 691–697 Wurr, D C E., Fellows, J R., and Pittam, A J (1987) The influence of plant raising conditions and transplant age on the growth and development of crisp lettuce J Agric Sci Camb 109, 573–581 Wurr, D C E., Fellows, J R., and Suckling, R F (1988) Crop continuity and prediction of maturity in the crisp lettuce variety Saladin J Agric Sci Camb 111, 481–486 Wurr, D C E., Fellows, J R., Sutherland, R A., and Elphinstone, E D (1990) A model of cauliflower curd growth to predict when curds reach a specified size J Hortic Sci 65, 555–564 Wurr, D C E., Fellows, J R., and Hambidge, A J (1991) The influence of field environmental conditions on calabrese growth and development J Hortic Sci 66, 495–504 Wurr, D C E., Fellows, J R., Phelps, K., and Reader, R J (1993) Vernalization in summer/autumn cauliflower (Brassica oleracea var botrytis L.) J Exp Bot 44, 1507–1514 Wurr, D C E., Fellows, J R., Phelps, K., and Reader, R J (1994) Testing a vernalization model on field-grown crops of four cauliflower cultivars J Hortic Sci 69, 251–255 Wurr, D C E., Fellows, J R., and Hambidge, A J (1995a) The potential impact of global warming on summer/autumn cauliflower growth in the UK Agric For Meteorol 72, 181–193 Wurr, D C E., Fellows, J R., Phelps, K., and Reader, R J (1995b) Vernalization in calabrese (Brassica oleracea var italica.)—A model for apex development J Exp Bot 46, 1487–1496 Wurr, D C E., Fellows, J R., and Phelps, K (1996) Investigating trends in vegetable crop response to increasing temperature associated with climate change Sci Hortic 66, 255–263 Wurr, D C E., Fellows, J R., and Walton, S B (1999) BROCCOLI: MORPH Models Program Manual Horticultural Development Council, East Malling Yan, W., and Hunt, L A (1999) An equation for modelling the temperature response of plants using only the cardinal temperatures Ann Bot 84, 607–614 Yan, W., and Wallace, D H (1998) Simulation and prediction of plant phenology for five crops based on photoperiod × temperature interaction Ann Bot 81, 705–716 Yin, X., Kropff, M J., McLaren, G., and Visperas, R M (1995) A nonlinear model for crop development as a function of temperature Agric For Meteorol 77, 1–16 Zink, F W., and Yamaguchi, M (1962) Studies on the growth rate and nutrient absorption of head lettuce Hilgardia 32, 471–500 Index A Abiotic factors, crop development evaporative water loss, 216 local factor adjustment, 220–222 measurement frequency, 219–220 photoperiod and temperature, 215–216 solar radiation, 213 solar radiation and temperature, 214–215 temperature sensor, 222 Adaptive models, farm systems, 97 Adhesion, microbial, DLVO theory application, 187–188 AEGIS model, see Agricultural and Environmental Geographic Information system model Affix+ development, 150 field tests, 150–152 Aggregation, microbial, DLVO theory application, 187–188 Agricultural and Environmental Geographic Information system model, 84 ALMANAC crop growth model, land use planning, 84–85 APSIM model cropping system sustainability, 78–79 farming systems, 82 Arid tropics, soil salinity, Atmosphere, greenhouse gases from soil degradation, 7–10 B BCWA, see Biofilm–cell wall assembly BEANGRO model, land use planning, 85 Biofilm–cell wall assembly colloidal stability, 185–189 ion adsorption, 179–183 modeling, 175–177 surface charge, 179–183 surface wettability, 183–185 Biofilms microbial, see Microbial biofilms nitrifying, see Nitrifying biofilms surface charge, 179–183 surfaces, functional group composition, 177–179 Biomass productivity, soils in tropics, 3–7 C Cell wall, microbial biofilm solution equilibrium cell wall–ion exchange, 172–175 overview, 170–172 CENTURY soil organic matter model, 107 CERES-Maize model crop forecasting, 87 crop management, 50–51, 58–60 CERES-Millet model, 87 CERES-Rice model crop management, 47–48, 50 nutrient management, 60 pest and disease management, 63–64 Climate change, 104–105 crop production impact, 89–92 Colloidal stability, biofilm–cell wall assembly, 185–189 Computer programs crop scheduling, 228–229 WIRROPT7, 55 Continuity curves, crop production planning, 223–224 Cool humid subtropics, Cool subtropics with summer rainfall, with winter rainfall, Cool tropics, Crop development abiotic factors evaporative water loss, 216 local factor adjustment, 220–222 measurement frequency, 219–220 photoperiod and temperature, 215–216 solar radiation, 213 solar radiation and temperature, 214–215 temperature sensor, 222 235 236 INDEX Crop development (continued ) base, ceiling, and optimum temperature, 208–211 growth phases, 205 models base temperature determination, 217–219 experimental techniques, 216–217 thermal time estimation, 219 weather associations, 219 nonlinear response functions, 211–213 temperature role, 230 thermal time, 206–208 Crop forecasting, crop simulation models, 86–87 Crop management harvesting, 71–72 multiple options, 72–73 nutrients, 57–63 pest and disease, 63–68 planting, 49–53 soil surface, 49 water, 53–57 weeds, 68–71 yield gap analysis, 46–49 Crop maturity, prediction, 227 Cropping systems new crops, 73–76 sustainability evaluation, 76–79 Crop prediction models base temperature determination, 217–219 experimental techniques, 216–217 thermal time estimation, 219 weather associations, 219 Crop production planning climate change impact, 89–92 continuity curves, 223–224 manipulation by culture, 224 maturity prediction, 227 sowing–planting intervals, 223 variety manipulation, 224 Crop product supply function models, 229 Crop scheduling models base temperature determination, 217–219 experimental techniques, 216–217 personalized information, 229–230 software framework, 228–229 thermal time estimation, 219 weather associations, 219 Crop–soil simulation models cropping systems new crops, 73–76 sustainability evaluation, 76–79 decision-support systems, 34, 102–103 development, 33–34 environmental research climate change, 89–92, 104–105 greenhouse gas production, 92–95 nitrogen, 105 farming systems, 79–82 genotype improvement desirable plant characteristics, 36–40 environmental characterization, 40–42 G × E interactions, 42–46 overview, 35–36 improvement program contributions, 103–104 management options harvesting, 71–72 multiple options, 72–73 nutrients, 57–63 pest and disease, 63–68 planting, 49–53 soil surface, 49 water, 53–57 weeds, 68–71 yield gap analysis, 46–49 pests, diseases, and weeds, 106 regional and national planning crop forecasting, 86–87 emergency relief, 88–89 irrigation planning, 88 land use, 82–86 research and extension projects, 101–102 soil processes, 106–107 Crop variety, manipulation, 224 CROPWAT model, 56 Culture, crop production planning, 224 CUPPA-Tea model, 53 D Decision-support systems, crop simulation models, 34, 102–103 Diseases crop management, 63–68 crop–soil simulation models, 106 DLVO theory, biofilm–cell wall assembly colloidal stability, 185–189 DSSs, see Decision-support systems 237 INDEX E Ecosystems, degraded, restoration, 18–19 Electrochemistry, nitrifying biofilm effects, 189–191 Emergency relief, crop simulation models, 88–89 Environment crop genotype characteristics, 37 crop genotype improvement, 40–42 legume nodulation, 146 Environmental research climate change impact on crops, 89–92, 104–105 greenhouse gas production, 92–95 nitrogen, 105 Epidemic prevention system, pest and disease management, 66–67 EPIPRE system, see Epidemic prevention system EPS, see Excreted exopolymeric substance Evaporative water loss, crop development, 216 Excreted exopolymeric substance biofilm contributions, 167–169 nitrifying biofilm effect on nitrification, 191 Genotype–environment interactions, crop genotype improvement, 42–46 Genotypes, crop desirable plant characteristics, 36–40 environmental characterization, 40–42 G × E interactions, 42–46 overview, 35–36 Geographical Information System, crop genotype improvement, 41–42 GHGs, see Greenhouse gases GIS, see Geographical Information System Global carbon cycle, soil carbon pool role, 10–13 GOSSYM–COMAX system, crop management, 72–73 GOSSYM model, weed management, 70 Greenhouse gases environmental research, 92–95 soil carbon sequestration, 15–17 soil degradation, 7–10 G × E interactions, see Genotype–environment interactions H Harvesting, crop management, 71–72 Humans, farm household models, 98 F I Farmers Advisors and Researchers Monitoring Simulation Communication and Performance Evaluation, 81–82 Farm households, modeling, 96–101 Farming systems, crop simulation models, 79–82 FARMSCAPE, see Farmers Advisors and Researchers Monitoring Simulation Communication and Performance Evaluation Finances, farm household models, 99 Fleece, crop production planning, 224 Functional groups, composition, biofilm surface, 177–179 G GCMs, see General circulation models General circulation models, climate impact on crop production, 90–92 Genes, nod, 130–132, 135–137 IMGLP techniques, see Interactive Multiple Goal Linear Programming techniques IMS, see Irrigation management services Industrial processes, microbial biofilms, 164–166 Interactive Multiple Goal Linear Programming techniques, 83–84 INTERCOM model, weed management, 68–69 Ion adsorption, biofilm surfaces, 179–183 Ion specificity, biofilm surfaces, 178–179 Ion uptake, microbial biofilm solution equilibrium, 170–172 Irrigation management services, crop management, 56–57 Irrigation planning, crop simulation models, 88 IWR model, irrigation planning, 88 K KYNO–CANE model, nutrient management, 61 238 INDEX L Land use planning, crop simulation models, 82–86 LCOs, see Lipo-chitooligosaccharides Legumes, nodulation high soil nitrogen, 142–144 nitrogen fixation, 137–138, 146 Nod factor, 132–135 nod genes, 131–132, 135–137 plant-to-bacteria signals, 130–131 preactivated Rhizobium, 146–149 root zone temperatures, 139–140 soil acidity, 144–145 soil flooding, 140–142 soil moisture deficiency, 140–142 soil nutrient deficiency, 142–144 soil salinity, 145 SoyaSignal and Affix+, 150–152 symbiotic process, 128–130 Lipo-chitooligosaccharides, nodulation, 129 LORA model, farming system, 81 M MATH test, see Microbial adhesion to hydrocarbon test Methane, environmental research, 92–94 METs, see Multienvironment trials Microbial adhesion, DLVO theory application, 187–188 Microbial adhesion to hydrocarbon test, surface wettability, 183 Microbial aggregation, DLVO theory application, 187–188 Microbial biofilms excreted exopolymeric substance contributions, 167–169 industrial and natural processes, 164–166 methods of formation, 166–167 solution equilibrium biofilm–cell wall assembly surface role, 175–177 cell wall description, 170–172 ion uptake, 170–172 nitrifying systems, 170 osmotic potential vs cell wall ion exchange, 172–175 Models AEGIS model, 84 ALMANAC crop growth model, 84–85 APSIM model, 78–79, 82 BEANGRO model, 85 biofilm–cell wall assembly surface roles, 175–177 CENTURY soil organic matter model, 107 CERES-Maize model, 50–51, 58–60, 87 CERES-Millet model, 87 CERES-Rice model, 47–48, 60, 63–64 crop prediction and scheduling, 216–219 crop product supply function, 229 crop–soil simulation, see Crop–soil simulation models CROPWAT model, 56 CUPPA-Tea model, 53 farm household livelihoods, 96–101 farm systems, 97 general climate circulation models, 90–92 GOSSYM model, 70 INTERCOM model, 68–69 IWR model, 88 KYNO–CANE model, 61 LORA model, 81 ORYZA model, 59 ORYZA1 model, 39, 90–91 ORYZA W model, 53–54 OZCOT cotton model, 88 PARCHED-THIRST model, 69 PARCH model, 54 PNUTGRO model, 46–47 POLYCROP model, 85 resource-constrained models, 97 SIMRIW model, 87, 90–91 SIRATAC model, 65–67 SORKAM sorghum model, 52 WOFOST model, 85, 87 WTGROWS model, 47 Multienvironment trials crop genotype evaluation, 40 G × E interactions, 42–44 N Nature, farm household models, 99 Nitrification systems microbial biofilm solution equilibrium cell wall description, 170–172 ion uptake, 170–172 osmotic potential vs cell wall ion exchange, 172–175 239 INDEX nitrifying biofilm effect electrochemical considerations, 189–191 EPS functionality, 191 film thickness, 193 substratum considerations, 192–193 Nitrifying biofilms, nitrification effect electrochemical considerations, 189–191 EPS functionality, 191 film thickness, 193 substratum considerations, 192–193 Nitrogen fixation legume nodulation, 137–138, 146 soil processes, 107 symbiotic, soil fertility, 127–128 Nod factor, legume nodulation, 132–135 Nod genes legume nodulation, 135–137 rhizobia, 130–132 Nodulation, legumes high soil nitrogen, 142–144 nitrogen fixation, 137–138, 146 Nod factor, 132–135 nod genes, 131–132, 135–137 plant-to-bacteria signals, 130–131 preactivated Rhizobium, 146–149 root zone temperatures, 139–140 soil acidity, 144–145 soil flooding, 140–142 soil moisture deficiency, 140–142 soil nutrient deficiency, 142–144 soil salinity, 145 SoyaSignal and Affix+, 150–152 symbiotic process, 128–130 Nonlinear response functions, crop development, 211–213 Nonwoven polypropylene, crop production planning, 224 Nutrients, crop management, 57–63 O ORYZA model, 59 ORYZA1 model climate impact on crop production, 90–91 crop gentotype improvement, 39 ORYZA W model, crop management, 53–54 Osmotic potential, microbial biofilm solution equilibrium, 172–175 OZCOT cotton model, irrigation planning, 88 P PARCHED-THIRST model, weed management, 69 PARCH model, crop management, 54 Perforated polyethylene, crop production planning, 224 Pests crop management, 63–68 crop–soil simulation models, 106 Phosphorus, soil processes, 107 Photoperiod–temperature, crop development, 215–216 Planting crop management, 49–53 crop production planning, 223 Plants, desirable characteristic identification, 36–40 Plant-to-bacteria signals, rhizobia nod gene induction, 130–131 PNUTGRO model, crop management, 46–47 Points of zero charge biofilm surfaces, 180–182 surface wettability, 184–185 POLYCROP model, land use planning, 85 Polyethylene, crop production planning, 224 Polypropylene, crop production planning, 224 Potassium, soil processes, 107 Product supply, crops, modeling, 229 PZC, see Points of zero charge R RAPs, see Recommended agricultural practices Recommended agricultural practices, agricultural intensification, 19–22 Resource-constrained models, farm systems, 97 Rhizobia nod genes, 130–132 preactivated, legume nodulation, 146–149 Root zone, temperatures, legume nodulation, 139–140 S Salinity, soil processes, 107 SIC pool, see Soil inorganic carbon pool 240 INDEX SIMRIW model climate change impact on crop production, 90–91 crop forecasting, 87 SIRATAC model, pest and disease management, 65–67 Social relationships, farm household models, 98 SOC pool, see Soil organic carbon pool Software, crop scheduling, 228–229 Soil acidity legume nodulation, 144–145 soil processes, 107 Soil carbon pool, global carbon cycle role, 10–13 Soil carbon sequestration, greenhouse effect mitigation, 15–17 Soil degradation greenhouse gases to atmosphere, 7–10 soil restoration, 18–19 sub-Saharan Africa and South Asia, 4–5 Soil fertility, symbiotic nitrogen fixation, 127–128 Soil flooding, legume nodulation, 140–142 Soil inorganic carbon pool dynamics, 23–24 global carbon cycle rol, 10–13 Soil moisture, legume nodulation, 140–142 Soil nitrogen, legume nodulation, 142–144 Soil nutrients, legume nodulation, 142–144 Soil organic carbon pool global carbon cycle role, 10–13 loss from tropics soils, 13–14 sequestration in tropics overview, 17–18 RAPs adoption, 19–22 soil and ecosystem restoration, 18–19 soil degradation, 8–10 Soil processes, crop–soil simulation models, 106–107 Soil restoration, necessity, 14–15 Soils, tropics biomass productivity, 3–7 soil organic carbon pool loss, 13–14 Soil salinity arid tropics, legume nodulation, 145 Soil surface, crop management, 49 Solar radiation crop development, 213 –temperature, crop development, 214–215 Solution equilibrium, microbial biofilms biofilm–cell wall assembly surface role, 175–177 cell description, 170–172 ion uptake, 170–172 nitrifying systems, 170 osmotic potential vs cell wall ion exchange, 172–175 SORKAM sorghum model, crop management, 52 South Asia, soil degradation, 4–5 Sowing, crop production planning, 223 SoyaSignal development, 150 field tests, 150–152 SSA, see Sub-Saharan Africa Sub-Saharan Africa, soil degradation, 4–5 Surface wettability, biofilm–cell wall assembly, 183–185 T Temperature crop development base, ceiling, and optimum temperature, 208–211 basic information, 230 models, 217–219 photoperiod and temperature, 215–216 solar radiation and temperature, 214–215 thermal time, 206–208 root zone, legume nodulation, 139–140 Temperature sensor, crop development studies, 222 Thermal time, crop development models, 219 Tropics soil organic carbon sequestration overview, 17–18 RAPs adoption, 19–22 soil and ecosystem restoration, 18–19 soils biomass productivity, 3–7 soil organic carbon pool loss, 13–14 U Unconstrained models, farm systems, 97 U.S Agency for International Development, crop emergency relief, 88–89 241 INDEX USAID, see U.S Agency for International Development V Variable-charge surface, biofilms, 177 W WARDA, see West African Rice Development Association Warm humid subtropics, characteristics, Warm humid tropics, characteristics, Warm seasonally dry tropics, characteristics, Water, crop management, 53–57 Water use efficiency, crop gentotype improvement, 38 Weather, crop associations, 219 Weeds crop management, 68–71 crop–soil simulation models, 106 West African Rice Development Association, weed management, 69 WIRROPT7, crop management, 55 WOFOST model crop forecasting, 87 land use planning, 85 WTGROWS model, crop management, 47 WUE, see Water use efficiency Y Yield gap analysis, crop management, 46–49 This Page Intentionally Left Blank ... Processes leading to SOC depletion Deforestation Erosion, mineralization, leaching Biomass burning Mineralization, volatilization Subsistence farming Mineralization, leaching Plowing Erosion, mineralization... deforestation, controlling biomass burning, increasing efficiency of energy-based inputs (e.g., nitrogen fertilizer, pumping irrigation, tillage, grain drying), and decreasing erosion-induced emission... 1941) However, linking agricultural intensification to enhancing production and mitigating the greenhouse effect is an innovative strategy Minimizing risks of soil degradation and restoring degraded
- Xem thêm -

Xem thêm: Advances in agronomy volume 76 , Advances in agronomy volume 76