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Winds of Change: A Century of Agroclimate Research

^a USDA-ARS, Grazinglands Research Lab., 7207 West Cheyenne St., El Reno, OK 73036
^b USDA-ARS, National Soil Tilth Lab., 2110 University Blvd., Ames, IA 50011. Contribution of the U.S. Department of Agriculture, Agricultural Research Service. Use of product name is for information purposes only and does not imply an endorsement by the authors or USDA

^* Corresponding author (jean.steiner{at}ars.usda.gov).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 
Climate has been of primary concern from the beginning of agricultural research. Early in the 20th century, climatology and agronomy evolved separately, focusing primarily on production agriculture and crop adaptation. Concepts developed include thermal units and water use efficiency. The integrated discipline of agroclimatology developed in the mid-20th century. As theoretical understanding evolved, numerous papers related to agroclimatology were named Citation Classics. Spectral properties of plants and soils were identified that underpin today's remote sensing technologies. Commercialization of instrumentation enhanced our ability to efficiently collect data using standardized methods. Private and public-sector partnerships advanced research capacity. Later in the 20th century, research focus shifted toward integrating knowledge into crop growth and agronomic models. Remote sensing provided capacity to gain theoretical and practical understanding of regional scale processes. In the early 21st century, recognition of earth as a system along with inter-related human systems is driving research and political agendas. There is a pressing need to change our data-rich to an information-rich environment. The emerging cyberinformatics field along with natural resource and agricultural system models allow us to apply climate information to assessments and decision support related to water supply, production, environmental management, and other issues. Solutions to today's problems require interdisciplinary and multi-sectoral teams. While needs have never been greater, fewer universities maintain critical mass required to offer advance degrees in agroclimatology. It will be increasingly important that agrclimatology attract top students and provide training and practical experience in conducting integrated systems research, communications, and team skills.

Abbreviations: CWSI, crop water stress index • ENSO, El Niño–Southern Oscillation • ET, evapotranspiration • FACE, free air CO₂ enrichment • FAO, United Nations Food and Agriculture Organization • GIS, geographic information systems • SDD, stress degree day • SOI, Southern Oscillation Index • WDI, water deficit index • WUE, water use efficiency

	NOTES

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Received for publication December 30, 2006.

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 
From the earliest writings about agriculture, climate and weather have been a major focus. In 29 B.C., Virgil wrote a lengthy poem about farming, "The Georgics," which included considerable discussions of soils and what we now call agronomy and agroclimatology (Virgil, 29 B.C.E.).

"...An unknown surface, heed we to forelearn
The winds and varying temper of the sky,
The lineal tilth and habits of the spot,
What every region yields, and what denies..."
Virgil, The Georgics, 29 B.C.E.

Certainly, "the winds and varying temper of the sky,...the habits of the spot, what every region yields, and what denies..." captures the essence of agroclimatological characterization. Virgil also clearly highlighted the vulnerability of agricultural to seasonal weather patterns.

"...And he, who having plowed the fallow plain
And heaved its furrowy ridges, turns once more
Cross-wise his shattering share, with stroke on stroke
The earth assails, and makes the field his thrall.
Pray for wet summers and for winters fine..."
Virgil, The Georgics, 29 B.C.E.

From ancient times to the foreseeable future, the farmer is always vulnerable to vagaries of the weather, be it wet summers and fine winters providing for a bounteous harvest, or dry summers and harsh winters leading to hard times.

Since the development of agricultural and natural resources research, climate and weather have been of primary concern because of their impact on food, feed, and fiber production. Interactions between weather or climate and agriculture are complex because of the spatial and temporal variation in the physical environment and the biological response. Agroclimatology spans a wide range of spatial and temporal scales. Figure 1 presents the general spatial and temporal scales of agroclimatology and related fields of study; the arrows (Fig. 1) indicate that the boundaries between the scales are fuzzy and each level extends into larger or smaller scales. Weather is experienced on a relatively local to regional scale for periods up to 1 or 2 wk. While weather is absolutely critical to agriculture and there are many important advances in the development and application of knowledge about weather to management of agricultural systems, that is not the primary focus of this article. Climate is realized at seasonal to decadal scales and generally is discussed at county to regional scales. Agricultural meteorology and micrometeorology focus on short time scales and small spatial scales up to field scale. The term environmental physics is sometimes applied to studies of soil–plant–animal–atmosphere continuum that include but extend beyond meteorological processes. Agroclimatology addresses issues from field to roughly county scale and generally at weekly to seasonal scales. The purpose of this article is to review the progress in and status of the science of agricultural meteorology and agroclimatology. However, these will be discussed in the context of the broader regional climate, and particularly in terms of the implications of climate change. Climate change is generally focused on subcontinental to global spatial scales and decadal to millennial time scales. However, adaptation to and mitigation of climate change often must be addressed at local and regional scales that are relevant to agroclimatology.

View larger version (16K): [in this window] [in a new window]   Fig. 1. Generalized temporal and spatial scaling of agricultural meteorology, agroclimatology, climatology, and climate change.

This paper was developed to highlight the history, contributions, and future directions of the field of agroclimatology. Key themes are presented in this article as follows:

· U.S. weather and climate infrastructure for agriculture

· Agroclimatological characterization

· Energy balance

· Soil–plant–atmosphere interactions

· Flux measurement and mass balance

· Incorporating climate information into decision making

For each of these thematic areas, a historical overview of scientific advances, highlights of seminal work, discussion of changes in research focus and application over time, and research focus for the coming decade are presented.

Table 1 summarizes progress in the agroclimatology field through four major periods over the past century, along with key issues facing science during each period, the scientific focus in agroclimatology, and advances in methods and concepts. As the discipline of agroclimatology and agricultural meteorology developed, numerous papers extending the knowledge base (Table 2 ) were named as Citation Classics (http://garfield.library.upenn.edu/classics.html; verified 11 Dec. 2007). Each of these citation classic papers contributed to conceptual advances made during the given time period and a large portion of them were written by early career scientists. Another contribution to the agroclimatology field was publication of several books authored by ASA members. Books such as Principles of Environmental Physics (Monteith, 1973, later revised as Monteith and Unsworth, 1990), Microclimate: The Biological Environment (Rosenberg, 1974, later revised as Rosenberg et al., 1983), Hillel, (1971); and Brutsaert (1982) served as textbooks or key references for multiple generations of students.

	HISTORICAL OVERVIEW AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 
U.S. Weather and Climate Infrastructure for Agriculture
Federal Infrastructure
The first weather measurements supported by the U.S. government were under the direction of the Surgeon General in the early 19th century. In the mid-19th century, the Smithsonian Institution established a volunteer weather observer network. In 1890, the Cooperative Weather Observer Network was established and in 1891, the weather service was transferred to the Department of Agriculture where it remained until 1940. The National Oceanic and Atmospheric Administration and National Weather Service now reside within the Department of Commerce, but there continues to be a multi-agency, Joint Agricultural Weather Facility housed at the USDA, World Agricultural Outlook Board. The evolution of the U.S. weather monitoring infrastructure is comparable with similar developments in other nations and was paralleled by development of meteorological and agronomic societies and organizations (Table 3 ).

Public–Private Partnerships
Research progress and efficiency have been greatly advanced in agroclimatology, as well as other disciplines, by partnerships between the private and public sectors. Through the middle of 20th century, relatively simple equipment was used for agroclimatology research. In the era following World War II, many seminal studies were undertaken, often with specialized equipment designed and constructed by researchers and technical support staff. As the science and technology matured, key advances in measurement technology were led from the private sector, frequently in partnership with a public sector scientist. An early example was the establishment of Soilmoisture Equipment to commercially produce pressure membrane and ceramic plate extractors developed by P.E. Skaling at the USDA Salinity Laboratory in Riverside, CA (www.soilmoisture.com/about.html; verified 11 Dec. 2007). Availability of these standardized extractor plates greatly advanced the study of soil physics and soil–plant–water relations. In the early 1970s, LiCor Bioscience was established by W. Biggs, who had developed a silicon sensor for photosynthetically active radiation while on the faculty at University of Nebraska (www.licor.com/corp/history.jsp; verified 11 Dec. 2007). Since that time, LiCor has developed or commercialized a wide range of scientific instruments for agroclimatology and soil–plant–water relations research. The Heinz Walz GmbH company (http://walz.com; verified 18 Dec. 2007) was established in 1972 and have developed a range of scientific instrumentation in close collaboration with Dr. A.E. Hall, Dr. O.L. Lange, Dr. E.D. Schulze, and other prominent scientists. Campbell Scientific, established in 1974, provided some of the earliest rugged, battery powered data loggers that greatly expanded the capacity to conduct environmental research in remote locations. G. Campbell, formerly with Washington State University, has provided scientific input to the product development throughout the history of the company (www.campbellsci.com/history; verified 11 Dec. 2007). Dr. M.A. Dixon, University of Guelph, and Dr. I. Grierson, University of Adelaide, have served as research partners to ICT International (www.ictinternational.com.au; verified 11 Dec. 2007), which has provided monitoring solutions for soil, plant, and environmental research since 1982. Additional examples are commercialization of sapflow measurement devices by M. and C.H.M. van Bavel (Dynamax) and J. Kucera (EMS Brno); of net radiometers and other instrumentation by L. and C. Fritschen, of close system canopy chambers for gas-exchange measurements (Steduto et al. (2002), www.tecno-el.it; verified 11 Dec. 2007), and many others. Such public–private partnerships have greatly contributed to the advancement of science through standardization of measurement technologies, reduced cost, improved reliability, and expanded functionality of instrumentation. Additional information about widely used measurement technologies and methodologies is provided by Pearcy et al. (1989).

Agroclimatological Characterization
As climate monitoring networks were established, research during the early part of the 20th century focused on describing basic climate characteristics such as mean and extreme values of temperature and precipitation on a monthly and annual basis, delineating frost-free periods, quantifying solar radiation, or sunshine hours. The first worldwide climate classification system (Köppen and Geiger, 1928) remains in use today. The important role of water to agriculture and human activities has led to development of several indices related to precipitation patterns. The aridity index, defined as the ratio of annual precipitation to annual potential evaporation, was defined in UNESCO (1977). Dregne (1982) described four key precipitation patterns, winter, summer, continental, and bimodal, which are critical determinants of ecological and agricultural potential. Mediterranean, monsoonal, and continental precipitation and evaporation patterns are illustrated in Steiner et al. (1988). Precipitation indices have been particularly important for agroclimatic analyses of dryland regions of the world (Hatfield, 1990; Stewart and Steiner, 1990).

Extensions in the later portion of the 20th century include development of ecoregion maps that blend climatological characteristics with other biophysical characteristics (Omernik, 1987, 2004). The development of geographic information systems (GIS) as a discipline has dramatically transformed agroclimatic and many other types of natural resource analyses. Wratt et al. (2006) provide an excellent description of GIS-based climatic mapping techniques to develop locally applicable information to help farmers and others identify opportunities and risks associated with new land uses.

Many methods have been developed to characterize agro-climatic potential in a systematic way for the earth's lands. One example of such a system is the Agro-ecological Zones of the United Nations Food and Agriculture Organization (FAO). Such delineations are useful to determine general cropping or agricultural systems that will likely be successful for a particular location, but there is a great deal of variability of climate, soils, and topography within an agro-ecological region that is of significance to particular organisms. In agriculture, it is also essential to work at a finer scale of microclimate to understand the environment as it affects a particular organism or community of organisms.

Changing water supply because of climate variation will continue to challenge agriculture in both rainfed and irrigation regions. Variation in rainfall patterns and drought cycles, as well as decreased fresh water supply for irrigation, will increase the demand for a better understanding of soil–plant–water relationships and how this information can be incorporated into crop selection and management decisions. In an era of global climate change, climatologists and agroclimatologists need to develop a system to periodically re-evaluate climate means, extremes, and probability distributions and revise maps of agroecological zones.

Energy Balance
Quantifying energy exchanges in the soil–plant–atmosphere continuum has been the subject of research throughout the past century. Net radiation is the total energy input into the system that is partitioned at the earth's surface into sensible, latent, and soil heat fluxes. Study of this partitioning is termed the energy balance, and it has been the subject of many investigations. Geiger (1973) in the fourth printing of his original 1927 work described the ‘heat budget’ of the earth's surface as the basis for micrometeorology.

A basic description of the energy balance that underpins a large body of research and practice is:

[1]

where R_n is net radiation, G is soil heat flux, LE is latent heat flux, and H is sensible heat flux, all in the same units (e.g., W m^–2) and with fluxes toward the soil–atmosphere surface being positive and fluxes away from the surface being negative. The energy balance varies with time, space, and type of surface. Additional terms can be included in the energy balance (Eq. [1]) to account for advected energy, physical storage of energy within the canopy, and biochemical energy storage in the photosynthetic process of vegetation. However, these terms are generally minor and are usually considered in special circumstances where understanding these component fractions is required to understand the dynamics of the vegetative layer. In the remainder of this section the components of the energy balance will be discussed to show the advances and challenges in quantifying these components.

Net Radiation
The driving force for energy input is the solar and longwave radiation from the atmosphere. Net radiation can be directly measured or estimated through physical relationships governed by sun angle, atmospheric depletion of sunlight, and emission of thermal radiation from the atmosphere and the surface. Many early researchers (e.g., Szeicz et al., 1964; Gates et al., 1965; Stanhill et al., 1966; Linacre, 1968; McKree, 1972, 1973; Idso, 1981; and many others) contributed to a quantitative understanding of radiation in agricultural environments. Net radiometers were developed in the 1960s and 1970s (Fritschen, 1962; Idso, 1970; and others) and remain a widely used type of instrument. However, with the beginning of large multi-institutional field energy and C balance campaigns in the mid 1980s, problems with design, calibration, and operational procedures became obvious when substantial differences were observed in net radiation measurements by different researchers (Fritschen, 1992; Kustas et al., 1998). Research continues to address calibration (e.g., Fritschen and Fritschen, 2007), design (e.g., Cobos and Baker, 2003), and cross comparison of net radiometers (e.g., Kohsiek et al., 2007).

Soil Heat Flux
The partitioning of energy into the soil, G, is a relatively small fraction of the energy balance but it is critical in terms of quantifying changes in soil temperature throughout the year (e.g., relative to modifications in the soil surface through mulches and tillage). The soil heat flux can be calculated using the temperature gradient method (Kimball et al., 1976a) or measured directly using soil heat flux plates, but they have the potential for large errors (Kimball et al., 1976b). To obtain good measurements, depth of measurement and accounting for heat storage above the plate must be considered (Ochsner et al., 2006, 2007). Soil heat flux is sometimes estimated as a fraction of net radiation, but this may introduce excessive error into the energy balance during periods of soil drying (Idso et al., 1975), when weather fronts cause major air temperature changes, or for daily or shorter time periods.

Sensible Heat Flux
The sensible heat component (H) is the energy that is available to heat the air surrounding the earth's surface. Sensible heat flux can be measured using aerodynamic methods (discussed below) or may be determined as a residual of the energy balance equations when all other terms are measured (e.g., when using weighing lysimeters to directly measure latent heat flux). Begg et al. (1964) reported some of the early diurnal energy budgets for high radiation environments that showed the sensible heat flux term can be quite large in the absence of adequate water to meet the evaporative demand. Raschke (1960) reviewed the literature on heat transfer between the plant and the environment. Tolk et al. (2006) reported sensible heat flux into the canopy accounted for 45% of LE for irrigated alfalfa (Medicago sativa L.) grown in a semiarid climate for selected days with high ET, high vapor pressure deficit, and high windspeed.

Latent Heat Flux
Latent heat flux is more generally known as evaporation or evapotranspiration (ET). Water loss via the soil and crop surfaces to the atmosphere has been one of the most studied areas in agroclimatology. The amplification of the original model of Penman (1948) by Monteith (1964) led to one of the most widely used ET equations which describes fluxes from a number of vegetative surfaces. The Penman–Monteith equation is currently used as a worldwide standard for reference ET by the Food and Agricultural Organization (FAO) as described by Allen et al. (1998). Several forms of the energy balance equation that range in complexity are used for ET estimation. Some early ET models that focused on limited requirements for input data (Thornthwaite, 1948; Blaney and Criddle, 1950; Priestley and Taylor, 1972) are still used today. Estimating ET under water-limiting conditions is more difficult than under well-watered conditions, and limitations that must be considered when using ET models are discussed by Hatfield and Allen (1996). The ET models are currently used at scales ranging from fields to large regions. As data availability and computational capacity become less limiting, there is increasing interest and progress in integration of remote sensing observations into surface energy balance models to produce regional (e.g., Anderson et al., 2007) and global estimates of water use and to provide feedback to global circulation models.

Partitioning of energy at the earth's surface and separation of ET into soil (E) and plant (T) components were the focus of studies by Ritchie (1971) and Ritchie and Burnett (1971) that improved understanding of linkages between crop development, precipitation patterns, and soil on the components of ET. These concepts are used today and are critical to understanding of impacts of soil management on E and the development of cropping systems with increased WUE.

Significant advances and application of evapotranspiration theory has been made in engineering disciplines, particularly to improve irrigation scheduling (Jensen et al., 1970). Significant publications in the engineering literature include Advances in Evapotranspiration (American Society of Agricultural Engineers, 1985), Lysimeters for Evapotranspiration and Environmental Measurement (Allen et al., 1991) and The ASCE Standardized Reference Evapotranspiration Equation (Allen et al., 2005).

In moist systems, latent energy dominates the energy balance, while in drier systems, sensible heat accounts for a large portion of available energy. Figure 2 illustrates some of the interactive effects between the plant and atmosphere by contrasting the daily energy balance components for irrigated and rainfed wheat (Triticum aestivum L.) under high and moderate evaporative conditions. First, the lower net radiation in the stressed wheat compared to the well-watered wheat illustrates the impact of the surface conditions on outgoing radiation. Both the reflected shortwave and outgoing longwave radiation can be affected by the crop canopy condition in which a water stressed canopy may be brighter, rougher, and warmer than the well watered canopy. On the day with extremely high evaporative conditions (very low dewpoint temperature and high windspeed compared to the more moderate day), the LE in the well-watered crop exceeded the net radiation by 40% with the additional energy coming into the canopy in the form of sensible heat. The impact of soil water availability on LE of the water stressed crop is illustrated by the LE component, which was 44% of R_n at 6 d after precipitation, compared with 21% of R_n at 11 d after precipitation.

View larger version (18K): [in this window] [in a new window]   Fig. 2. Energy balance of well-watered and water-stressed winter wheat under (A) high and (B) moderate evaporative demand at Bushland Texas. Evaporation (LE) was measured by weighing lysimeters, Rn by net radiometer, G by heat flux plates corrected for heat storage above the plates, and sensible heat (H) by solution of Eq. [1]. Last rainfall of 12.2 mm (29.9 MJ m–2 equivalent) was May 2. Data source: Collaborative research of J.L. Steiner, T.A. Howell, A.D. Schneider, S.R. Evett, and J.A. Tolk, USDA-ARS, Bushland, TX.

[2]

where R_n and G are positive toward the surface and

[3]

where is the psychrometric constant, h_h and h_v are transfer coefficients for heat and vapor, respectively (h_h/h_v is assumed to be equal to 1), T_z and T₀ are temperature at height z and at the surface, respectively, and e_z and e₀ are vapor pressure at height z and at the surface, respectively. Bowen's (1926) method continues to underpin flux measurements in a wide range of research.

A seminal paper by Penman (1948) described the equation given below:

[4]

where is the slope of the saturation vapor curve, Cp is the volumetric heat capacity, h is a transfer coefficient, e*_z is the saturation vapor pressure at height z and e_z is the vapor pressure at height z. This equation combined energy and atmospheric terms and is often called the combination equation. Penman derived an empirical term for the aerodynamic portion of the equation that included the vapor pressure deficit and a linear windspeed function. This equation and later extensions have been used widely to calculate "potential evaporation" or "potential evapotranspiration" and sometimes is referred to a "big leaf" model because it treats the evaporation process of a grass surface as similar to transpiration from a single leaf.

The Penman combination equation was developed for daily evaporation estimates for a short grass surface that was not limited by water supply. Tanner (1960) presented a more detailed energy balance approach to describing ET from a cropped surface and proposed micrometeorological methods that would permit measurement of fluxes at intervals of less than 1 h, important in advancing understanding of the processes. In this study, Tanner began to describe the problems of obtaining energy fluxes over small areas and the need for larger areas (later termed fetch) to account for the dynamics of the vertical energy exchanges.

Monteith (1964) advanced our understanding of coupling of the plant with the atmosphere, expressed in the expanded form of the energy balance below:

[5]

where T_s and T_a are the surface and air temperatures (°C), respectively, r_a is the aerodynamic resistance (s m^–1) and r_c is the canopy resistance (s m^–1), e_s(T_s) is the saturation vapor pressure at the T_s (kPa), e_a the actual vapor pressure, and the psychometric constant (kPa °C^–1). This expression of the energy balance has permitted a more rigorous description of the coupling of surface characteristics (surface or canopy resistance) with surface temperature as part of the energy balance and it has been explored by numerous research groups in attempts to quantify the changing response of plants to available soil water.

It is not possible to mention all of the researchers who have worked in this area over the past century; however, their contributions have advanced our understanding of the complexity and dynamics of the microclimate in which we manage agricultural ecosystems. A recent product from the American Society of Agronomy, Micrometeorology in Agricultural Systems (Hatfield and Baker, 2005), reviews in detail the current state of knowledge on the exchange processes in the soil–plant–atmosphere continuum and measurement of the components required to understand the dynamics of this region of the earth's surface.

Soil–Plant–Atmosphere Interactions
Radiation
Radiation has many effects beyond the energy balance. Light interception by plants, as well as the spectral characteristics of the light impinging on plants, must be known to understand plant physiological responses. The utilization of Beer's law to describe the absorption of solar radiation as it penetrated into a plant canopy was first described by Monsi and Saeki (1953). This simple relationship opened the path for many studies that described how the extinction coefficient is affected by leaf angle, leaf area distribution, and plant spacing. A review by Lemeur and Blad (1975) assembled the current information on light models according to whether they treated the foliage as a geometrical or statistical problem. A summary of processes involved with understanding radiative transfer in plant communities was provided by Ross (1975).

Monteith (1965) related light distribution within crop canopies to photosynthesis rates. In that same time period, a linear relationship between accumulated biomass and the accumulated amount of intercepted solar radiation was demonstrated for maize (Zea mays L.) (Williams et al., 1965) and soybean [Glycine max (L.) Merr.] (Shibles and Weber, 1966). Radiative transfer in plant communities is routinely used in crop growth simulation models to estimate the photosynthetic rate and overall plant growth, and agricultural meteorologists have contributed significantly to the refinement of these models. Research on plant responses to light has been conducted at the interface of plant physiology and agricultural meteorology, with much of the work reported in the plant physiology literature.

Leaves, as objects, reflect, absorb, or transmit light. The reflectance properties of leaves have been used throughout the past century to evaluate plant responses to stress and for a variety of predictive purposes. Gates et al. (1965) were the first to describe the spectral properties of plants, setting the basis for later development of remote sensing technologies that are widely used today. Their work provided a foundation for a number of research studies that began to define the spectral differences among species and changes in the spectral components in response to age, nutrient stress, or disease (Gausman and Hart, 1974; Gausman et al., 1975, 1976; Pinter et al., 1979). Asrar et al. (1984) developed a method to estimate absorbed photosynthetically active radiation and leaf area index from spectral reflectance. Goel and Norman (1990) provided information about optical and thermal infrared approaches to study vegetation canopies that summarizes the utility of different waveband combinations for agronomic assessment. Details about the advances in the use of remote sensing for agronomic applications are described in Hatfield et al. (2008).

Temperature
Temperature responses of biological systems can be characterized by the minimum and maximum temperatures at which biological activity stops. This range and the optimum temperature are species specific and characterize the role of temperature in many biological processes, for example, vernalization, breaking dormancy of seeds, changing from vegetative to reproductive growth. For many responses, the temperature of a particular plant part (root, tuber, bud) is critical to obtain the desired response. In some cases, there is a thermoperiod response in which alternating day and night temperatures are required to trigger a process, for example, breaking dormancy in potatoes (Solanum tuberosum L.) requires cool night temperature.

Temperature is one of the most easily observed parameters in the lower atmosphere. The development of observational networks for air temperature created a database that has been extensively used for agriculture. The simple observation that air temperature was related to phenological development of plants provided one of the early tools for managing crops (Madariaga and Knott, 1951; Katz, 1952; Lana and Haber, 1952). These initial observations prompted a series of studies that continue today to use temperature-phenological relationships in crop development models. Many different thermal models have been developed and several were compared by Aspiazu and Shaw (1972). There have been amplifications of thermal models to include daylength to account for photoperiod in photoperiod sensitive plants (Coligado and Brown, 1975) and vernalization requirements for winter wheat (Streck et al., 2003). Insect and disease models have used either air or soil temperature as driving variables for insect or disease development. An example of this type of model to predict insect emergence is given by Rummel and Hatfield (1989). Thermal models are routinely used in integrated pest management models for a variety of crops and reported in the entomological literature. Nocturnal temperature and the relationship to relative humidity and dew formation are important aspects of the temperature complex, particularly for pest management and for many horticultural crops.

Water Stress
A key concept developed from energy balance studies was that of potential evaporation as described by van Bavel (1966). This spurred a range of studies to evaluate ET in context of what a full-canopy could potentially transpire under given weather conditions; these concepts remain at the core of much research and are embedded in many current plant growth models. The energy balance model shown in Eq. [5] has been used for evaluation of water use by plant canopies and has quantified reductions of LE associated with inadequate soil water.

Understanding water stress onset, intensity, and impact has probably been the largest area of agroclimatic research. The development of the porometer for field measurement of stomatal resistance by Kanemasu et al. (1969) launched a series of studies that began to develop an improved understanding of crop water relations. Another advance at this time was the "pressure bomb" or Scholander chamber by Scholander et al. (1965). Both of these instruments provided a method for quantifying plant water relations under field conditions and required measurements on individual leaves. Application on individual leaves was considered a limitation for some purposes. For example, a comparison of eight different methods on rice (Oryza sativa L.) showed that more rapid measurement methods, e.g., canopy temperature methods, were most useful in screening large number of plants (O'Toole et al., 1984). However, the insights provided through application of the pressure bomb and porometry have been invaluable in advancing understanding of plant physiology and soil–plant–water relations. For example, Evenson and Rose (1976) quantified the seasonal variation in stomatal resistance in cotton (Gossypium hirsutum L.) and identified changes associated with factors in addition to water stress.

Development of sap flow measurement devices (Crmaìk et al., 1973; Crmaìk and Kucìera, 1981; Dixon and Tyree, 1984; Granier, 1985; Baker and van Bavel, 1987; Baker and Nieber, 1989) provided greater insight into root water uptake, translocation of water to the leaves, and photosynthesis. The role of roots in soil has long been an area where quantitative understanding is sparse and ability to predict growth and function is limited. As we gain understanding in limitations posed by the soil environment to roots, it may be possible to identify plants with root systems that better sustain plant growth and productivity. An example of this that has shown promise is selection of plants with aerenchymous root systems for use in soils that have limited aeration due to compacted layers or high soil water content during some seasons (Huang et al., 1997a, 1997b).

Canopy resistance and aerodynamic resistance are critical terms in Eq. [5]. Canopy resistance is an extension of stomatal resistance and is related to crop water stress. A study on alfalfa (Medicago sativa L.) showed that ET proceeded at the potential rate and canopy resistance remained below 20 s m^–1 when soil water was adequate, but that canopy resistance began to rapidly increase and ET to decrease when soil water became limited (van Bavel, 1967). Hatfield (1985) showed that canopy resistance could be calculated from application of the energy balance (Eq. [5]) for wheat crops and quantified a linear relationship of canopy resistance and soil water content. This method was extended to potatoes by Amer and Hatfield (2004) to evaluate irrigation management.

Water stress is a common occurrence in agronomic crops, and at some time during the growing season, water deficits impact crop growth or yield in almost all climates. Crop stress has been quantified using the thermal portion of the radiative spectrum. One of the first definitive reports describing the relationship among plant water stress, solar radiation, air temperature, and leaf temperature was by Wiegand and Namken (1966). Their research built on the finding by Tanner (1963) that plant temperature varied from air temperature and could be measured with thermocouples attached to the leaves. Wiegand and Namken (1966) and Ehler et al. (1978) found that leaf temperature was related to plant moisture status. Later, leaf thermocouples were replaced by infrared thermometers and the quantification of crop stress and estimates of water use have been based on observations of canopy temperature. Stress degree day, crop water stress index, non-water stressed baselines, thermal kinetic windows, crop specific temperatures, and water deficit index are terms that have been used to describe plant stress and that have been developed for a number of different agronomic crops to evaluate crop water stress.

The canopy resistance approach provided the foundation for development of the crop water stress index (CWSI) by Jackson et al. (1981), who used infrared measurements of canopy temperature as a measure of crop water status in wheat. A linear relationship was developed for red kidney bean (Phaseolus vulgaris L.) between crop water use and the accumulation of stress degree days (SDDs, defined as canopy–air temperature) during the growing season (Walker and Hatfield, 1979). Patel et al. (2001) expanded these original studies to demonstrate that water use in pigeonpea [Cajanus cajan (L.) Millsp.] decreased with increasing SDD and seed yield decreased exponentially with increasing SDD. As additional crop and climate factors were shown to affect the canopy–air temperature difference (T_c – T_a), Idso et al. (1981) derived an empirical model for canopy stress that was based on observations of T_c – T_a for the crop of interest combined with T_c – T_a for well-watered and completely stressed plots of the same crop and same atmospheric conditions.

The theoretical approach developed by Jackson et al. (1981) shows the utility of the energy balance model (Eq. [5]) to derive other relationship as follows:

[6]

where the quantity r_c/r_a is expressed as:

[7]

and E is actual evaporation, E_p potential evaporation, r_cp the canopy resistance of a well-watered canopy, r_c the actual canopy resistance, r_a the aerodynamic resistance to sensible heat transfer, e*_a the saturation vapor pressure, and e_a the actual vapor pressure of the air. Jackson et al. (1981) derived the theoretical shape of Eq. [6] from the assumptions about a well-watered and a completely stressed or nontranspiring canopy. The CWSI has become one of the more widely used methods for quantifying crop stress. Gardner et al. (1992a, 1992b) summarized the theory underlying the CWSI and the potential measurement problems in obtaining accurate values for parameters needed to estimate CWSI. Wanjura and Upchurch (2000) compared the empirical (Idso et al., 1981) and theoretical (Jackson et al., 1981) forms of CWSI for corn and cotton on the High Plains of Texas and found the empirical approach was more accurate than the theoretical approach because of the bounds of 0 to 1.0 placed by the empirical method on stress levels.

One problem in the application of infrared temperature measurements in crops has been incomplete ground cover where the infrared temperature reflects a mixture of crop and soil temperature. Heilman et al. (1981) demonstrated that incomplete groundcover caused a significant bias in estimating the plant canopy temperature. Moran et al. (1994) developed a relationship to extend the CWSI theory to partial vegetative cover using a spectral approach. They developed the water deficit index (WDI) that covers the range of well-watered to completely stressed vegetation for a range of canopy sizes based on the ratio of actual to potential evaporation, the same foundation as the CWSI in Eq. [6]. The WDI approach offers potential as a method for quantifying water stress under conditions of partial cover.

Canopy temperatures have been incorporated directly into the forms of the energy balance to estimate evaporation as:

[8]

Hatfield et al. (1984) showed this model provided a sound approach for measuring crop water requirements by comparing direct measurements of LE from lysimeters to LE from Eq. [8] for number of locations and crops.

Evapotranspiration is a critical energy balance component in crop growth models and being able to estimate LE over large areas would help in regional plant growth or crop yield estimation. Bausch and Neale (1989) showed that crop coefficients, required for many ET models, could be obtained from remotely sensed data. This is an indirect approach that uses a vegetative index to derive a crop coefficient that would allow the use of standard meteorological data with less frequent remotely sensed observations of the canopy to provide regional estimates of evaporation. Zhang et al. (1995) developed regional estimates of LE using Eq. [8] and found the model produced acceptable agreement to area averages determined by ground-based measurement.

The extension of point measurements collected over fields into regional scale estimates is one of the current challenges facing agroclimatologists. Regional scale models require integration of remote sensing and ground-based observations. One problem facing regional scale studies is that vegetation is not distributed uniformly. The nonrandom effects of vegetative cover on the regional scale energy exchanges have been studied using a scaling method called DisALEXI (Disaggregation Atmosphere–Land Exchange Inverse) that disaggregates 5-km regional output to the Landsat TM resolution (Anderson et al., 2005). Anderson et al. (2007) further developed a multiscale approach that uses thermal, visible, and near-infrared imagery from multiple satellites to partition the fluxes between the soil and canopy. Their approach produced fluxes at a range of scales from 1 m to 10 km with the potential of being able to assess the representativeness of sensor placement across complex landscapes. Further refinement in the use remote sensing as an assessment tool coupled with ground-based observations will advance our understanding of the linkages among the scales shown in Fig. 1.

Canopy temperatures have proven useful to quantify crop stress in agronomic crops and there is expanding interest in the coupling of thermal measurements with spectral reflectance to provide a robust method of quantifying crop stress and development. The approaches developed by Hatfield (1983) and Moran et al. (1994) are examples of integrating measures of radiative emission and reflection from canopies to estimate crop growth and yield. The development of simple two-source models by Norman et al. (1995) and the Dual Temperature Difference method developed by Norman et al. (2000) to consider explicit contributions of the soil and vegetation to the radiometric temperature and energy exchanges have great potential to improve evapotranspiration predictions from crop canopies.

Water Use Efficiency
Water availability for agricultural systems is critical for optimum production. Throughout the past century there have been a wide range of studies conducted on water stress, water use rates by crops, water balance in different cropping systems, and methods to assess each of these using a variety of techniques. In the early 20th century Briggs and Shantz (1912, 1914), introduced the concept of water use efficiency (WUE), defined as the ratio of plant biomass produced relative to the quantity of water consumed. They derived WUE values for numerous crop species using pot lysimetry, where water use was determined by measuring the water added through the growing season. Denmead and Shaw (1960, 1962) advanced our understanding of interactive effects of soil water content and meteorological factors in availability of water to plants and impacts of water stress on productivity. The concept of WUE remains central to evaluation of agricultural systems.

The impact of atmospheric humidity on WUE was elucidated by Tanner and Sinclair (1983), who suggested that agroclimatologists and agronomists reconsider the role that WUE has in crop production efficiency. They proposed that for full cover crops, atmospheric vapor pressure deficit was a key determinant of dry matter (yield) produced per unit of transpiration (water use). Monteith (1994) suggested that WUE should be linked to broader resource capture efficiency and argued that capture and efficient use of CO₂, water, light, and nutrients be linked as part of the analyses of crop growth. He summarized the development of these concepts from their basis in two key assumptions about plant growth proposed by Blackman (1919). First, the rate at which leaves capture light energy is proportional to the total biomass and second, the rate of biomass accumulation is proportional to the rate of capture. Although WUE receives the most attention as a measure of crop response to water, linkage with other limiting resources is critical to understand the dynamics of plant response to the environment.

Soil Microclimate
Soil provides the environment for plant roots and a diverse array of organisms and important nutrient cycling processes. Soil microclimate is predominately determined by the radiation and water balances, soil properties (texture, structure), and the physical site (slope, aspect), but management practices can influence soil microclimate to produce conditions more favorable for processes of interest. In cold areas, it might be beneficial to apply practices to increase soil temperature to extend the growing seasons for crops. In dry areas, it would be beneficial to apply practices to conserve soil water content. In windy areas, it is beneficial to modify the wind regime near the soil surface to reduce soil erosion and plant damage from moving soil particles. Early soil microclimate research focused on seedling establishment and nutrient cycling from a production perspective. Today, such studies are equally focused on environmental and ecological aspects of agricultural systems. The root environment and soil–root interactions remains one of the least understood aspects of plant physiology and remains a focus of active research. Soil and climate are discussed in detail in Steiner (2002).

Over the past few decades, conservation tillage systems have been developed and implemented worldwide to provide soil and water conservation in cropping systems. The impacts of retaining more residues on the soil surface through reduced tillage are complex and interactive (Steiner, 1994). Improved measurement systems such as time domain reflectometry (TDR) and heat pulse methods allow precise measurements in space and time that can provide for better understanding of heat and water flow processes and of the environment encountered by organisms in different parts of the soil. Evett (1999) summarized key processes that can be manipulated to improve agronomic management outcomes.

In many models, effects of microclimate on nutrient cycling, seed germination, root elongation, and other processes are calculated. However, few models address impacts that organisms may have in shaping their own environment, e.g., modification of total soil porosity, pore size distribution, and pore continuity. Improved understanding of interactions of soil organisms with their microclimate will be essential to understand contributions of agriculture and natural ecosystems to net emissions of greenhouse gases such as nitrous oxides and methane.

Flux Measurement and Mass Balance
Movement of oxygen, CO₂, and water vapor to and from the soil and plants is essential to sustain biological and ecological functioning. Water vapor concentration would accumulate and suppress ongoing evaporation if the vapor were not moved from near the evaporating surfaces to other parts of the atmosphere. Similarly, as plants photosynthesize and deplete the air around them of CO₂, photosynthesis would decrease if the supply of CO₂ were not replenished from other parts of the atmosphere. Continual movement in the atmosphere is driven by energy from the sun that causes heating, changes in air density, and movement from regions of higher to lower pressure. A number of methods developed over the past 50 yr to quantify atmospheric fluxes remain viable for research today.

Aerodynamic Fluxes
Fluxes of energy, momentum, or gases in the lower atmosphere have been described by the flux gradient adaptation of Fick's Law of Diffusion (Fick, 1855). Flux gradient equations for momentum, heat, and water vapor are:

[9]

[10]

[11]

where is the surface shear stress (kg m^–1 s^–2), H is the sensible heat flux (W m^–2), E is the water vapor flux (W m^–2), _a is air density (kg m^–3), C_p is specific heat of air (J kg^–1 K^–1), and k_m, k_h, and k_q are eddy diffusivities for momentum, heat, and water vapor, respectively. The assumption that k_m = k_h = k_q has been the foundation for the development various forms of the energy exchanges. Monin and Obukhov (1954) described some of the basic relationships of turbulent mixing in the lower atmosphere that are still being used today. Businger et al. (1971) further refined the understanding of the impacts of stable and unstable atmospheric conditions on the eddy diffusivity terms. Quantification of the roughness height and displacement lengths for crop canopies was a critical theoretical advance to which the worldwide community of agricultural meteorologists contributed (Inoue, 1963). Early studies often used strip chart recorders to obtain data from field studies; use of digital recorders has dramatically increased the magnitude of data collection for atmospheric flux analyses.

The eddy covariance method was proposed by Swinbank (1951) as a direct measure of flux based on correlation between variation in an entity of interest to variation in the vertical vector of wind flux in fully turbulent systems. This method requires very rapid response and very precise instrumentation, and application of the method expanded rapidly in the latter portion of the 20th century. The eddy covariance method was adopted as the standard flux measurement method by FLUXNET, a global network, and the measurements and analyses from this network have contributed greatly to understanding of the C cycle and earth–atmosphere processes (Baldocchi et al., 2001; Law et al., 2002).

Challenges remain in application of all aerodynamic methods. Baker (2003) identified surface-flux exchange measurement as one of the "recalcitrant problems in environmental measurement," in particular difficulties in closing the energy balance and in achieving the assumptions of stationarity and surface homogeneity required for the eddy covariance method. Flux relationships for different surfaces were recently reviewed by Prueger and Kustas (2005), who summarized the current use of these approaches in soil–plant–atmosphere studies.

Carbon Dioxide Fluxes
One of the first studies combining CO₂ measurements with aerodynamic flux measurement was reported by Lemon (1960). This study, combining wind velocity gradients with CO₂ gradients over a corn canopy, was one of several studies conducted in Ellis Hollow, New York, that quantified exchanges above and within the canopy (Wright and Lemon, 1966). These studies set the pathway for subsequent research over the course of the next decades. In a recent overview, Welles and McDermitt (2005) traced the history of CO₂ measurements including the development of the fast response CO₂ sensors that are being used today as part of several large-scale C exchange studies. The combination of the fast response CO₂ and H₂O vapor sensors have begun to provide new insights into the dynamics of crop surfaces that are being used to estimate regional scale fluxes of water and C.

Carbon dioxide fluxes from agricultural systems have been studied intensively as part of the Ameriflux program and the North American Carbon Program. These programs have attempted to document C fluxes in different systems. Verma et al. (2005) provide an example of comprehensive flux measurement for irrigated and rainfed agricultural systems. Baker and Griffis (2005) illustrate how eddy covariance and mass balance techniques can be linked to provide an analysis of the energy and C exchanges for corn and soybean canopies. Meyers and Hollinger (2004) showed it was possible to combine energy and C flux measurement to document storage terms within plant canopies. Such studies show that once elusive parameters in the expanded energy balance can be quantified through the use of the newer techniques.

Evaluation of the potential impact of increasing atmospheric CO₂ on plants has been addressed in free air CO₂ enrichment (FACE) experiments. The FACE methodology was developed to extend understanding of impacts of elevated CO₂ on plants from controlled environments to more natural field environments through complete growing seasons (Hendrey et al., 1993; Hendrey and Kimball, 1994). Kimball (1983) summarized the information available from small chamber studies and suggested that doubling of CO₂ from 330 to 660 µmol mol^–1) would increase C-3 plant yield by 33% and C-4 plants by 10% without any other limitations to plant growth. In a later summary of responses under free-air enrichment, Kimball et al. (2002) concluded that plant responses may not be as large as previously predicted. Leakey et al. (2006) recently reported a 50% increase in CO₂ levels (from 376 to 542 µmol mol^–1) produced no significant response in a well-watered maize crop. Allen et al. (2003) showed for soybean that doubling of CO₂ increased the water use efficiency. Further application of the FACE technology coupled with crop simulation models will continue to provide information about how crops and other ecosystems may respond to climate change scenarios. Long et al. (2004) and Ainsworth and Long (2005) synthesized findings from numerous FACE experiments over the last two decades and found that crop yields increased less than had been anticipated based on earlier controlled environment studies.

Remote Sensing
Remote sensing has provided a method to quantify spatial and temporal dynamics of crops and the response of crops to various management scenarios. Development of remote sensing methods over the last century is detailed by Hatfield et al. (2008) as part of this series of papers. The applications of remote sensing technology to agroclimatological problems have focused on methods that quantify biomass (living or dead) or leaf area present on the soil surface for input into energy balance models, direct estimation of ET for regional scale water use models, or the quantification of crop stress or water requirements. A review of these approaches for dryland crops was prepared by Hatfield et al. (2004) and a special issue of Photogrammetic Engineering and Remote Sensing, Volume 69 (Hatfield and Hart, 2003) evaluates progress in applying remote sensing techniques to various agronomic and natural resource problems.

Remote sensing technologies are providing better spatial resolution that is allowing researchers to develop relationships of spectral signals to specific land areas, rather than an average signal across multiple land uses. Similarly, technologies are being developed and applied within fields that allow managers to address factors that are spatially distributed within the field that affect plant growth and yield. Some of the limiting factors being identified and managed include soil micro-climate, including variability of soil texture and the soil water balance or spatial patterns of soil compaction and associated problems with poor aeration. Integration of multiple platforms to provide a more comprehensive "view" of the agricultural system and linkage of this information into assessment tools offers potential for improved efficiency of agricultural production. While most agricultural remote sensing research has focused on use of broad band or hyperspectral reflectance data, inclusion of fluorescence might provide more information about vegetation and plant stress (Corp et al., 2003; Campbell et al., 2007).

Emerging Techniques
Advances continue in the development of techniques to quantify the energy and gas exchanges in the soil–plant–atmosphere continuum. Methods such as relaxed eddy accumulation, scalar fluxes, inverse Lagrangian fluxes, or surface renewal have begun to appear in the literature. These are summarized in recent reviews by Denmead et al. (2005), McInnes and Heilman (2005), Meyers and Baldocchi (2005), and Paw U et al. (2005). Zhang et al. (2006) combined continuous stable isotope measurements with micrometeorological measures to partition net CO₂ exchange into photosynthesis and respiration components.

Development and application of Light Detection and Ranging (lidar) systems with expanded spatial and temporal resolution have allowed more detailed characterization of the vertical and horizontal structure of the atmosphere. Cooper et al. (2006) determined the mass exchange in the stable boundary layer using a high resolution Raman lidar to measure water vapor fluxes in the lower 75 m of the atmosphere. Lidar has been combined with eddy covariance and footprint models to evaluate three-dimensional fields of moisture movement in the lower atmosphere (Cooper et al., 2003). Eichinger and Cooper (2007) utilized lidar measurements to calculate spatially resolved LE, H, and virtual potential heat flux over agricultural fields. The ability to quantify the fluxes of water and heat in three dimensions above a surface will continue to provide new insights into the dynamics of the coupling between the surface and the atmosphere. As emerging methods increase our ability to quantify energy and mass exchanges at the earth's surface, the challenge for agronomists will be to link with plant physiologists, agricultural meteorologists, and others to effectively apply these and other approaches to understanding complex interactions of plants with the soil and atmosphere.

Incorporating Climate Information into Decision Making
Agronomic Models
The WUE approaches pioneered by Briggs and Shantz (1912, 1914) evolved into regression approaches to crop yield forecasting. By the 1960s the knowledge base within agricultural meteorology and plant physiology were maturing along with the evolution of computer technologies. This led to development of mathematical descriptions of plant growth (Radford, 1967; Duncan et al., 1967; France and Thornley, 1984) that were later incorporated into process-oriented crop growth models.

A soil water balance model that partitioned energy into transpiration and soil evaporation based on leaf area index of the crop (Ritchie, 1972) was incorporated into several early crop models developed at the USDA and Texas Agricultural Experiment Station in Temple, TX, and that approach remains a key component of many crop, range, and natural resource models used today. Another major center of crop model development was at Wageningen University (de Wit and Penning de Vries, 1985; Penning de Vries, 1982). A comprehensive overview of models developed to support a wide range of production, management, and economic analyses and decision-support applications is beyond the scope of this article and readers are referred to Ahuja et al. (2002) for further information.

As the focus of agricultural research and agroclimatology broadened from a production focus to incorporate a range of environmental concerns, modelers incorporated functions for nutrient cycling, soil C dynamics, tillage systems, and other management practices. Under the Decision Support System for Agrotechnology Transfer framework (www.mic.hawaii.edu/dev_tech/software/dssat.html; verified 11 Dec. 2007), a suite of models along with default data bases were compiled allowing new users to efficiently begin crop modeling efforts (Jones et al., 2003). Formal and informal networks of modelers (e.g., International Consortium for Agricultural Systems Applications, www.icasa.net; verified 11 Dec. 2007) result in rapid exchange of new modules and exchange of development and validation data sets.

Weather Generators
Development of synthetic weather generators (e.g., Nicks and Harp, 1980; Richardson, 1981) in parallel with development of diverse crop and natural resource models, was an important advance that facilitated scenario analysis (Semenov, 2006). Garbrecht and Zhang (2003) showed that because of inherent characteristics of random number generators, screening the generated precipitation to ensure representation of the climate of interest allows for shorter simulation duration and greater ability to simulate subtle changes in precipitation such as those associated with seasonal forecasts. Carlini et al. (2006) developed a library to generate synthetic precipitation data for future crop modeling applications.

Seasonal Climate Forecasts
While knowing the next season's climate has long been a dream of agriculturalists, today there is reason for optimism that our ability to predict future seasonal climates is improving. The El Niño phenomenon was observed in the 19th century, and the Southern Oscillation Index (SOI) was quantified in the late 19th century. Sivakumar (2006) provided an overview of early studies of climate anomalies and development of climate forecasting, particularly for developing regions of the world.

Operational forecasts are being made by various groups around the world. The International Research Institute for Climate and Society produces widely used seasonal climate forecasts (http://iri.columbia.edu/climate; verified 11 Dec. 2007). The Queensland Department of Primary Industry and the Commonwealth Bureau of Meteorology produces seasonal climate forecasts based on ENSO and SOI signals for Australia (www.bom.gov.au/climate/ahead; verified 11 Dec. 2007), or worldwide (www.longpaddock.qld.gov.au/index.html; verified 11 Dec. 2007). The U.S. National Oceanic and Atmospheric Administration's Climate Prediction Center (www.noaa.gov/climate.html; verified 11 Dec. 2007) releases seasonal climate forecasts covering the coming year for the United States and are developing North American forecast products.

Because forecasts are a relatively new product, and the forecasts are being released to user groups outside the traditional meteorology community, new methods for evaluation are needed. Schneider and Garbrecht (2003a, 2003b) developed indices to evaluate seasonal forecasts for agricultural applications. Their recent analyses of forecasts from the U.S. National Weather Service (Schneider and Garbrecht, 2006) indicate the results depend on forecast variable, direction of forecast (wetter/drier, warmer/cooler), season, and forecast lead time. Overall, in the Desert Southwest, southern and eastern Texas, the Gulf Coast, Florida, and parts of the Pacific Northwest, temperature forecasts have relatively high effectiveness, primarily in November through July and precipitation forecasts have moderate effectiveness in October through February, for lead times up to about half a year. For the rest of the United States, only temperature forecasts show some effectiveness at longer lead times. Lack of skill in seasonal climate forecasts was also identified as a limitation for DEMETER forecasts for Europe and New Zealand (Semenov and Doblas-Reyes, 2007) and for many developing countries (Sivakumar, 2006).

For the regions with high "effectiveness," seasonal climate forecasts may have considerable water resource and agricultural implications, but questions remains of how to downscale and interpret the impact of forecasts for applications at a local level (Steiner et al., 2004). Hansen (2002) discussed many challenges in matching appropriate forecast information to climate sensitive responses that are important to particular decision makers. Applications involving crop models are impeded by divergence in the spatial and temporal scales of the forecasts relative to the input requirements of models, as well as the nonlinear response of plants to climate (Hansen et al., 2006a).

Applications of Seasonal Climate Forecasts
Research conducted in the 1970s by J.I. Stewart and others (Stewart and Hash, 1982; Stewart and Kashasha, 1984; Stewart and Faught, 1984; Stewart, 1988) pioneered the concept of response farming. Response farming was based on identification of correlations between date of onset of the rainy season with length of the growing season and total seasonal precipitation. Such relationships gave an early indication of the type of season to be expected. With early onset, and in anticipation of a good rainy season, longer growing season crops could be planted and higher level of inputs could be purchased. With late onset indicating higher probability of low rainfall, a conservative management system could be followed to ensure food security and minimize economic risks. Response farming is generally most applicable to Mediterranean and monsoonal climates, where virtually all of the annual precipitation comes in the rainy season; it is less applicable to continental climates.

Stewart's work provided the basis for later research by Sadras et al. (2003), who developed systems for the southeast Australia Mallee region to adjust seasonal management based on April precipitation. Phillips and McIntyre (2000) identified significant correlations of ENSO to rainfall variability in unimodal and bimodal regions of east Africa, particularly relating to length of season. Phillips et al. (2002) later analyzed national records of planted area for grain in Zimbabwe, and found that farmers, in aggregate, reduced planted area during an El Niño year when a poor season was forecasted, compared with increased planted area during a La Niña year when a favorable season was forecasted.

Comprehensive summaries of research and applications related to seasonal climate forecasts with application to agriculture and natural resource management were reported in Muchow and Bellamy (1991) and Hammer et al. (2000). Hammer et al. (1996) reported that tactical management based on five phases of the SOI increased profit and reduced risk compared with fixed management in Australian wheat regions. Another approach uses analog climate years, based on a climate indicator. For instance, the Queensland Center for Climate Applications contrasted scenarios for the five phases of the SOI index (Stone et al., 1996) by selecting all years in the historical record that match the current phase of the SOI as analogs for the probable climate for the upcoming season.

Operational climate forecasts offer potential to guide production decisions, such as crop species or cultivar selection, fertility management, area to be planted, pest management, intensity and timing of grazing and purchase, sale, or movement of animals. Management decisions related to marketing, labor, and diversification, and regional decisions relating to input supply, markets, transportation, storage, community health (e.g., Bi et al., 1998) or drought preparedness (Dilley, 2000; Finan and Nelson, 2001) could also be guided by climate forecasts. To move forward, continued improvement and evaluation of forecasts skill are needed. Forecasting tools for regions that gain little from current forecasts and forecasts of extreme events should be a focus for further work in the climatology and agroclimatology communities. Uncertainty analysis for scenario simulation will be required as we develop tools to assess tradeoffs among multiple objectives and as we scale up to whole farm or landscape context. A key limitation that must be addressed is methods to communicate probabilistic outcomes and engaging farmers or other end users as partners in development of tools to support decision-making.

Because soil water depletion is a major component of crop water use and soil water is highly variable at planting time in many regions, opportunities to integrate measurement of soil water content at planting with use of climate forecasts should be investigated. Robinson and Butler (2002) found that preplant soil water content provided the best forecast of dryland crop yields in the northern Australian grainbelt, but relatively few farmers accurately measured soil water content before planting. Before turning to seasonal climate forecast to reduce risks, there usually will be greater return to first analyzing risks associated with the current management system, adopting good agronomic practices, and implementing relatively straightforward monitoring (such as soil water or soil nutrient contents) into decision-making processes. Carberry et al. (2002) have worked with Australian farmers who have had some successes in using of seasonal climate forecasts in farm level decision-making, FARMSCAPE. Their system combined soil monitoring and crop simulation with the climate forecasts, and involved farmers, advisors, and researchers working together closely.

	FUTURE DIRECTIONS

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 
Driving Forces
As our understanding increases and societal priorities change, our knowledge of agroclimatology is being applied to new issues. There is rapidly increasing knowledge of and attention to the science of climatology and increasing pressure to redefine the science of agronomy to provide a balance of production, economic, environmental, and social criteria for evaluation at the field and farm scales as well as for the agricultural sector as a whole. Although the trend over past decades has been toward large-scale production of standardized commodities, many of today's consumers are demanding more information about their food products and expressing preferences for particular production practices within the marketplace as well as the regulatory environment. With vast and growing human populations on the earth, there is increased pressure on water resources, food, energy, and environmental services. The recent rapid development of biofuels as a political, economic, and social priority, along with the rapid increase in prices of petroleum and other fossil fuels, has intensified the debate about tradeoffs between food, feed, fuel, fiber, and environmental function of agricultural systems.

Increasingly agronomy must be able to address multiple objectives and tradeoffs at farm to global scales (Hatfield, 2005). Stigter (2007) set forth a framework that spans from basic agrometeorological sciences to agrometeorological services to support decision making. He emphasized the need for better dispersion of knowledge to the farm level and applications that use existing information better through improved determination of the decision-maker needs, training of agrometeorological extensionists, evaluation of the policy environment needed to foster success, and explicit evaluation of the agrometeorological knowledge base for technologies and areas of scientific knowledge that need to be moved to operational status. Reynolds et al. (2007) identified key lessons in developing a new framework for science for dryland development that are equally relevant to advancing the field of agroclimatology. Researchers and practitioners must: (i) adopt an integrated approach, (ii) be aware of slowly evolving conditions, (iii) recognize nonlinear processes, (iv) anticipate cross-scale interactions, and (v) value local knowledge.

One of America's early environmentalists, George Perkins Marsh, wrote Man and Nature (originally in 1864), a sobering account of the multitude of unintended consequences of man's actions on nature, including the impacts of agriculture.

The felling of the woods has been attended with momentous consequences to the drainage of the soil, to the external configuration of its surface, and probably, also, to local climate; ...
George Perkins Marsh, 1865, Man and Nature

A significant effort for future agroclimatologist will be on development of strategies and technologies to restore nature's function impaired by past practices, mitigate unintended consequences of current practices, and develop methods to better evaluate the range of likely outcomes associated with proposed alternative technologies and practices.

Research Challenges
Adapting to and Mitigating Climate Change
The Mauna Loa observation of increasing CO₂ in the atmosphere was first published in 1976 (Keeling et al., 1976). Although the possible linkage between atmospheric CO₂ concentration and the energy balance (now called the greenhouse effect) was raised by Arrhenius (1903), the Mauna Loa data were the first to raise widespread scientific and public awareness of increasing concentrations of CO₂ in the atmosphere. Keeling et al.'s work triggered concerns about the greenhouse effect and potential global climate change, leading to a tremendous research focus on the earth–ocean–atmosphere system from the 1970s to the present. The recent reports from the International Panel on Climate Change have indicated that the evidence for climate change is strong and that anthropogenic sources are a likely causal factor (IPCC, 2007a), that likely impacts on temperature and precipitation will have significant impacts on agriculture, including the most negative impacts on the poorest populations (reduced food security, inadequate potable water, increased health risks) (IPCC, 2007b), and that agriculture can play a significant role in mitigating climate change, particularly through reduced emissions of methane and nitrous oxide gases (IPCC, 2007c). Unlike prior IPCC reports, which focused on C sequestration in forestry, increased soil C sequestration was identified as a viable mitigation strategy.

Many adaptation strategies proposed for agriculture also have potential to contribute to mitigation of greenhouse gas emissions (Olesen, 2006). Additionally, many of the promising mitigation strategies for agriculture would provide important cobenefits such as increased water and nutrient holding capacity and enhanced soil biodiversity with increased soil C levels, reduced risk of erosion with increased crop residues, and improved N use efficiency and reduced nutrient contamination with improved N management (Rice, 2006). Bonan (2002) described in detail the processes at multiple scales by which landscapes affect and are affected by climate. Understanding these processes provides the scientific basis for adaptation and mitigation strategies.

The potential for managing agriculture, forestry, and ecosystems to "sequester" C from the atmosphere has greatly influenced agronomic research and will continue to be a focus of scientific, policy, and private sector attention. The practicality of C sequestration in the soil remains controversial because of the difficulty in compiling quantitative inventories and monitoring changes in soil C. The role of soil processes in the state and flux of other greenhouse gases is even less understood. Lokupitiya and Paustian (2006) reviewed national inventory methods for soil greenhouse gas emissions and discussed challenges and complexities that must be a high priority research area in coming years.

While a great deal of attention has been given through the years to effects of soil microclimate on N transformations in the soil, particularly nitrification and denitrification, far less attention has been paid to soil organisms and processes that produce methane and nitrous oxide. Because of the importance of these gases in the global atmospheric processes, they will continue to receive increased attention (Duxbury, 1994, Kroeze et al., 1999). Comprehensive studies that address the spatial and temporal interactions in the C and N cycles for major land uses, including cropping systems, range and pastures, and forests will be one of the challenges ahead for agroclimatologists and their collaborators.

Climate change impacts on agricultural production have serious implications for food security on a global basis (Parry et al., 1999). Increasing CO₂ levels have been shown to impact competitiveness of invasive species (Ziska, 2003), weed response to glyphosate (Ziska et al., 1999), and many other processes. Changing CO₂ concentration also impacts species composition and forage quality in rangelands (Morgan et al., 2004; 2007). Such studies represent some of the many challenges that agroclimatologists should address as part of future scenarios analyses.

Enhancing Resilience to Extreme Events
Numerous scientists have documented changes in annual and seasonal mean precipitation and temperature during the 20th century, and also in precipitation and temperature extremes (Easterling et al., 2000; Frei et al., 1999; Groisman et al., 2001; Karl and Knight, 1998; Kunkel et al., 1999). The SWCS (2003, 2007) convened panels who determined that the historic record exhibits increased frequency of intense precipitation at the end of the 20th century and determined that the magnitude was such that changes in agricultural conservation planning and practice may be required to protect soil and water resources. The SWCS (2003) assessment summarized major observed climate changes for the contiguous United States to include many factors of great concern to agriculture and natural resource management, including higher minimum temperature, decreased spring snow cover in the West, increased mean precipitation, increased heavy rains, and increased high streamflow events in the eastern United States. Changes in extreme weather are likely related to interdecadal oscillations in the oceans (e.g., Greene et al., 2007) as well as by longer-term global change (IPCC, 2007a).

Most studies focus on changes of extreme precipitation events, but changes in temperature extremes also have serious implications for ecosystems, agriculture, and human well being. Warmer minimum temperatures may cause problems with vernalization of some crops. Absence of freezing temperatures in some mid-latitude regions may significantly change the insect and disease populations for agriculture as well as for human populations and natural ecosystems. Increased warm temperatures and heat waves will require adaptive practices to reduce wildfire risks in grassland and forest systems and will increase the need for more drought-tolerant crops. Warming temperatures and the related decline of glaciers in the many regions of the world that rely on snowmelt to meet year-round water requirements will leave these regions more vulnerable during persistent drought periods.

Monitoring and Assessing Agriculture in the Environment
Agriculture increasingly faces trade-offs among different food–fiber–fuel and ecosystem enterprises, but lacks the tools to comprehensively assess short-term and long-term costs and benefits of alternative strategies. Agriculturalists at all levels need to identify new production, marketing, and policy approaches to simultaneously sustain the resource base and support economic viability of rural households and communities. In the policy arena at international, national, state, and local levels, market mechanisms are being explored to address short- and long-term environmental concerns including water quantity and quality, greenhouse gas mitigation, air quality, farmland protection and green space preservation, wildlife habitat and species protection, and others. In developing and implementing such market based instruments, great challenges exist in inventory of existing condition, monitoring of change in condition, and estimating desired benefits provided by particular management practices.

Remote sensing technologies and a wide range of agricultural, ecological, and hydrologic models have an important role to play in development and implementation of environmental markets as they evolve over the next decades. A special issue of Agricultural Systems (Perez et al., 2007) focused on how agroclimatology researchers and crop modelers need to interact with social scientists to address challenges to help make C sequestration markets work for Africa's rural poor. Schlenker et al. (2007) quantified the impacts of water availability and degree days on agricultural land values in California. As pressure on water resources increases, analyses such as this can play a role in determining the value of water rights that might be transferred from agriculture to other sectors through rental, lease, or sale.

Informing Agricultural Decision Making
Since the 1970s and 1980s, support of agricultural decision making through the application of crop models has been a goal of researchers. McCown et al. (2002) developed a special issue of Agricultural Systems that probed the enigma of lack of adoption of crop models by farmers and other agricultural decision makers. Many of the papers in that issue identified the need for more focus on the interactions of social and technical issues and for participatory approaches where both the researcher and the decision maker are engaged in learning and exchange of information. In a special issue on applying climate prediction to agriculture (Hansen et al., 2006b), Sivakumar (2006) identified several areas that need attention to advance the use of climate information particularly to smallholder farmers. These include improved forecast accuracy, quantifying the evidence of forecast benefit, enhanced stakeholder participation, assessing adoption failures for lessons learned, exploring regional market and storage applications, and addressing institutional and policy issues. Vogel and O'Brien (2006) emphasized that application of climate information must consider the diverse multiple stressors that farmers face in addition to climate uncertainty and the resources and coping mechanisms available to respond. Cabrera et al. (2007) emphasized that the value of climate information is impacted by farm policy and the risk aversion or tolerance of individuals. In their assessment for peanut–cotton–corn systems in Florida, current farm policies in the United States decreased the value of climate forecasts because other policy-related considerations were driving decision making. For risk adverse farmers, the highest benefit of a climate forecast was realized because of taking better advantage of favorable forecast years.

The Discipline of Agroclimatology
There has never been a greater need for the development and application of agroclimatological information to solving diverse agricultural and environmental problems. The solutions to today's problems require that agroclimatologists work in collaboration with broad interdisciplinary and multi-sectoral teams. While the needs are large, the availability of training in agroclimatology is diminishing, as fewer and fewer universities maintain critical mass required to offer an advance degree program in agroclimatology. Programs that are offered may be located in soils, agronomy, geography, or other academic departments and are difficult to identify through graduate studies websites. The loss of critical mass in academic agroclimatology programs was raised by Decker (1994) and remains a concern. For the universities that maintain advanced agroclimatology and agricultural meteorology programs, it will be increasingly important to train students not only in mathematics and biophysical sciences, but also to provide training and practical experiences in conducting integrated systems research, communications, and team skills. Given the high level of creativity and productivity indicated by the high proportion of "citation classics" (Table 2) that were published by early career scientists, it is urgent that a continued stream of high caliber students be attracted into advanced studies in agroclimatology.

	ACKNOWLEDGMENTS

 
The article was greatly improved by the critical review of Dr. James Heilman and other journal reviewers. The authors dedicate this manuscript to their colleagues in the Agroclimatology and Agronomic Modeling Division in recognition of the stimulating ideas and critical information produced by that group through the years, and in particular to the pioneers who developed the theoretical underpinnings of agricultural meteorology and agroclimatology, as well as the next generation whose scientific contributions will be essential in developing a more sustainable agriculture under complex and dynamic social and biophysical environments.

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION HISTORICAL OVERVIEW AND... FUTURE DIRECTIONS REFERENCES

 

• Ainsworth, E.A., and S.P. Long. 2005. What have we learned from 15 years of free-air CO₂ enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO₂. New Phytol. 165:351–372.[CrossRef][Web of Science][Medline]
• Ahuja, L.R., L. Ma, and T.A. Howell. 2002. Agricultural system models in field research and technology transfer. Lewis Publishers, Boca Raton, FL.
• Allen, L.H., Jr., D. Pan, K.J. Boote, N.B. Pickering, and J.W. Jones. 2003. Carbon dioxide and temperature effects on evapotranspiration and water-use efficiency of soybean. Agron. J. 95:1071–1081.[Abstract/Free Full Text]
• Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop requirements. Irrigation and Drainage Pap. 566. FAO, Rome, Italy.
• Allen, R.G., T.A. Howell, W.O. Pruitt, I.A. Walter, and M.E. Jensen. 1991. Lysimeters for evapotranspiration and environmental measurements. Am. Soc. Civ. Eng., New York.
• Allen, R.G., I.A. Walter, R.L. Elliot, and T.A. Howell. 2005. The ASCE standardized reference evapotranspiration equation. Am. Soc. Civ. Eng., New York.
• Amer, K., and J.L. Hatfield. 2004. Canopy resistance in potato as affected by soil and meteorological parameters. Agron. J. 96:978–985.[Abstract/Free Full Text]
• American Society of Agricultural Engineers. 1985. Advances in evapotranspiration. ASAE, St. Joseph, MI.
• Anderson, M.C., J.M. Norman, W.P. Kustas, F. Li, J.H. Prueger, and J.R. Mecikalski. 2005. Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX. J. Hydrometeorol. 6:892–909.
• Anderson, M.C., W.P. Kustas, and J.M. Norman. 2007. Upscaling flux observations from local to continental scales using thermal remote sensing. Agron. J. 99:240–254.[Abstract/Free Full Text]
• Arrhenius, S.A. 1903. Lehrbuch der Kosmischen Physik 2:477–481 Hirzel, Leipzig.
• Aspiazu, C., and R.H. Shaw. 1972. Comparison of several methods of growing degree-unit calculations for corn (Zea mays L.). Iowa State J. Sci. 46:435–442.
• Asrar, G., M. Fuchs, E.T. Kanemasu, and J.L. Hatfield. 1984. Estimating absorbed photosynthetically active radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76:300–306.[Abstract/Free Full Text]
• Baker, J.M. 2003. Recalcitrant problems in environmental instrumentation. Agron. J. 95:1404–1407.[Abstract/Free Full Text]
• Baker, J.M., and T.J. Griffis. 2005. Examining strategies to improve the carbon balance of corn/soybean agriculture using eddy covariance and mass balance techniques. Agric. For. Meteorol. 128:163–177.
• Baker, J.M., and J.L. Nieber. 1989. An analysis of the steady-state heat balance method for measuring sap flow in plants. Agric. For. Meteorol. 48:93–109.
• Baker, J.M., and C.H.M. van Bavel. 1987. Measurement of mass flow of water in the stems of herbaceous plants. Plant Cell Environ. 10:777–782.
• Baldocchi, D.D., E. Falge, L. Gu, R. Olson, D. Hollinger, S. Running, P. Anthoni, K. Ch. Bernhofer, R. Davis, J. Evans, A. Fuentes, G. Goldstein, B. Katul, X. Law, Y. Lee, T. Malhi, W. Meyers, W. Munger, K.T. Oechel, U. Paw, K. Pilegaard, H.P. Schmid, R. Valentini, S. Verma, T. Vesala, K. Wilson, and S. Wofsy. 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82:2415–2434.[CrossRef]
• Bausch, W.C., and C.M.U. Neale. 1989. Spectral inputs improve corn crop coefficients and irrigation scheduling. Trans. ASAE 32:1901–1908.[Web of Science]
• Begg, J.E., J.F. Bierhuizen, E.R. Lemon, D.K. Misra, R.O. Slatyer, and W.R. Stern. 1964. Diurnal energy and water exchanges in bulrush millet in an area of high solar radiation. Agric. For. Meteorol. 1:294–312.
• Begg, J.E., and N.C. Turner. 1976. Crop water deficits. Adv. Agron. 28:161–217.
• Bi, P., X.K. Wu, K.A. Parton, and S.L. Tong. 1998. Seasonal rainfall variability, the incidence of hemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. Am. J. Epidemiol. 148:276–281.[Abstract/Free Full Text]
• Blackman, V.H. 1919. The compound interest law and plant growth. Ann. Bot. (Lond.) 33:353–360.
• Blaney, H.F., and W.D. Criddle. 1950. Determining water requirements in irrigated areas from climatological and irrigation data. SCS-TP 96. USDA-SCS, Washington, DC.
• Bonan, G. 2002. Ecological climatology: Concepts and applications. Cambridge Univ. Press.
• Bowen, I.S. 1926. The ratio of heat losses by conduction and by evaporation from any water surface. Phys. Rev. 27:779–787.[Web of Science]
• Briggs, L.J., and H.L. Shantz. 1912. The wilting coefficient for different plants and its indirect determination. USDA Bureau of Plant Industry Bull. 230. U.S. Gov. Print. Office, Washington, DC.
• Briggs, L.J., and H.L. Shantz. 1914. Relative water requirements of plants. J. Agric. Res. 3:1–63.
• Brix, H. 1962. The effect of water stress on the rates of photosynthesis and respiration in tomato plants and loblolly pine seedlings. Physiol. Plant. 15:10–20.[CrossRef]
• Brutsaert, W. 1982. Evaporation into the atmosphere. Kluwer Academic Publishers, Dordrecht, the Netherlands.
• Businger, J.A., J.C. Wyngaard, Y. Izumi, and E.F. Bradley. 1971. Flux-profile relationships in the atmospheric surface layer. J. Atmos. Sci. 28:181–189.[CrossRef]
• Cabrera, V.E., D. Letson, and G. Podestá. 2007. The value of climate information when farm programs matter. Agric. Syst. 93:25–42.[Web of Science]
• Campbell, P.K.E., E.M. Middleton, J.E. McMurtrey, L.A. Crop, and E.W. Chappelle. 2007. Assessment of vegetation stress using reflectance or fluorescence measurements. J. Environ. Qual. 36:832–845.[Abstract/Free Full Text]
• Carberry, P.S., A. Hochman, R.L. McCown, N.P. Dalgliesh, M.A. Foale, P.L. Poulton, J.N.G. Hargreaves, D.M.G. Hargreaves, S. Cawthray, N. Hillcoat, and M.L. Robertson. 2002. The FARMSCAPE approach to decision support: Farmers', advisers', researchers' monitoring, simulation, communication, and performance evaluation. Agric. Syst. 74:179–220.[CrossRef][Web of Science]
• Carlini, L., G. Bellocchi, and M. Donatelli. 2006. A library to generate synthetic precipitation data. Agron. J. 98:1312–1317.[Abstract/Free Full Text]
• Crmaìk, J., J. Deml, and M. Penka. 1973. A new method of sap-flow rate determination in trees. Biol. Plant. 15:171–178.
• Crmaìk, J., and J. Kucìera. 1981. The compensation of natural temperature gradient in the measuring point during the sap flow rate determination in trees. Biol. Plant. 23:469–471.
• Cobos, D.R., and J.M. Baker. 2003. Evaluation and modification of a domeless net radiometer. Agron. J. 95:177–183.[Abstract/Free Full Text]
• Coligado, M.C., and D.M. Brown. 1975. A bio-photo-thermal model to predict tassel-initiation time in corn (Zea mays L.). Agric. For. Meteorol. 15:11–31.
• Cooper, D.I., W.E. Eichinger, J. Archuleta, L. Hipps, J. Kao, M.Y. Leclerc, C.M. Neale, and J. Prueger. 2003. Spatial and temporal footprint analysis of three dimensional moisture fields from lidar, eddy covariance, and a footprint model. Agric. For. Meteorol. 114:213–234.
• Cooper, D.I., M.Y. Leclerc, J. Archuleta, R. Coulter, W.E. Eichinger, C.Y.J. Kao, and C.J. Nappo. 2006. Mass exchange in the stable boundary layer by coherent structures. Agric. For. Meteorol. 136:114–131.
• Corp, L.A., J.E. McMurtrey, E.M. Middleton, C.L. Mulchi, E.W. Chappelle, and C.S.T. Daughtry. 2003. Fluorescence sensing systems: In vivo detection of biophysical variations in field corn due to nitrogen supply. Remote Sens. Environ. 86:470–479.[CrossRef]
• Decker, W.L. 1994. Developments in agricultural meteorology as a guide to its potential for the twenty-first century. Agric. For. Meteorol. 69:9–25.
• Denmead, O.T., M.R. Raupach, R. Leuning, F.X. Dunin, and J.R. Freney. 2005. Inverse Lagrangian analysis of heat, vapor, and gas exchange in plant canopies. p. 485–511. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. ASA Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Denmead, O.T., and R.H. Shaw. 1960. The effects of soil moisture at different growth stages of growth on the development and yield of corn. Agron. J. 52:272–274.[Abstract/Free Full Text]
• Denmead, O.T., and R.H. Shaw. 1962. Availability of soil water to plants as affected by soil moisture content and meteorological conditions. Agron. J. 54:385–390.[Abstract/Free Full Text]
• de Wit, C.T., and F.W.T. Penning de Vries. 1985. Predictive models in agricultural production. Phil. Trans. R. Soc. London Ser. B 310:309–315.[Abstract/Free Full Text]
• Dilley, J. 2000. Reducing vulnerability to climate variability in Southern Africa: The growing role of climate information. Clim. Change 45:63–73.[CrossRef]
• Dixon, M.A., and M.T. Tyree. 1984. A new stem hygrometer corrected for temperature gradients and calibrated against the pressure bomb. Plant Cell Environ. 7:693–697.
• Dregne, H.E. 1982. Dryland soil resources. Sci. and Technol. Agric. Rep., Agency for Int. Dev., Washington, DC.
• Duncan, W.G., R.S. Loomis, W.A. Williams, and R. Hanau. 1967. A model for simulating photosynthesis in plant communities. Hilgardia 38:181–205.[Web of Science]
• Duxbury, J.M. 1994. The significance of agricultural sources of greenhouse gases. Fert. Res. 38:151–163.[CrossRef]
• Easterling, D.R., G.A. Meehl, C. Parmesan, S.A. Changnon, T.R. Karl, and L.O. Mearns. 2000. Climate extremes: Observations, modeling, and impacts. Science 289:2068–2074.[Abstract/Free Full Text]
• Ehler, W.L., S.B. Idso, R.D. Jackson, and R.J. Reginato. 1978. Wheat canopy temperatures: Relation to plant water potential. Agron. J. 70:251–256.[Abstract/Free Full Text]
• Eichinger, W.E., and D.I. Cooper. 2007. Using lidar remote sensing for spatially resolved measurements of evaporation and other meteorological parameters. Agron. J. 99:255–271.[Abstract/Free Full Text]
• El-Sharkawy, M., and J. Hesketh. 1965. Photosynthesis among species in relation to characteristics of leaf anatomy and CO₂ diffusion resistances. Crop Sci. 5:517–521.[Free Full Text]
• Evenson, J.P., and C.P. Rose. 1976. Seasonal variations in stomatal resistance in cotton. Agric. For. Meteorol. 17:381–386.
• Evett, S.R. 1999. Energy and water balances at soil–plant–atmosphere interfaces. p. A129–A185 In M.E. Sumner (ed.) CRC Handbook of Soil Science, CRC Press, Boca Raton, FL.
• Fick, A. 1855. Uber diffusion. Phil. Mag. 10:30–39. (reprinted in 1995 as On diffusion. J. Membr. Sci. 100:33–38.
• Finan, T.J., and D.R. Nelson. 2001. Making rain, making roads, making do: Public and private adaptations to drought in Ceara, Northeast Brazil. Clim. Res. 19:97–108.
• Fischer, R.A., and T.C. Hsiao. 1968. Stomatal opening in isolated epidermal strips of Vicia faba: II. Responses of KCl concentration and the role of potassium absorption. Plant Physiol. 43:1953–1958.
• France, J., and J.H.M. Thornley. 1984. Mathematical models in agriculture. Butterworth, London.
• Frei, A., D.A. Robinson, and M.G. Hughes. 1999. North American snow extent, 1910–1994. Int. J. Clim. 19:1517–1534.
• Fritschen, L.J. 1962. Construction and evaluation of a miniature net radiometer. J. Appl. Meteorol. 2:165–172.
• Fritschen, L.J. 1992. Comparisons of surface flux measurement systems used in FIFE 1989. J. Geophys. Res. 97(D17):18,697–18,713.
• Fritschen, L.J., and C.L. Fritschen. 2007. Calibration of shielded net radiometers. Agron. J. 99:297–303.[Abstract/Free Full Text]
• Garbrecht, J.D., and J.X. Zhang. 2003. Generating representative sequences of daily precipitation for agricultural simulations. Appl. Eng. Agric. 19:423–429.[Web of Science]
• Gardner, B.R., D.C. Nielsen, and C.C. Shock. 1992a. Infrared thermometry and the Crop Water Stress Index: I. History, theory, and baselines. J. Prod. Agric. 5:462–466.
• Gardner, B.R., D.C. Nielsen, and C.C. Shock. 1992b. Infrared thermometry and the Crop Water Stress Index: II. Sampling procedures and interpretation. J. Prod. Agric. 5:466–475.
• Gardner, W.R. 1960. Dynamic aspects of water availability to plants. Soil Sci. 89:63–73.
• Gates, D.M., H.J. Keegan, J.C. Schleter, and V.R. Weidner. 1965. Spectral properties of plants. Appl. Opt. 4:11–20.
• Gausman, H.W., A.H. Gerbermann, and C.L. Wiegand. 1975. Use of ERTS-1 data to detect chlorotic grain-sorghum. Photogramm. Eng. Remote Sens. 41:177–179.
• Gausman, H.W., and W.G. Hart. 1974. Reflectance of sooty mold fungus on citrus leaves over 2.5 to 40-micrometer wavelength interval. J. Econ. Entomol. 67:479–480.[Web of Science]
• Gausman, H.W., R.R. Rodriguez, and A.J. Richardson. 1976. Infinite reflectance of dead compared with live vegetation. Agron. J. 68:295–296.[Abstract/Free Full Text]
• Geiger, R. 1973. The climate near the ground. Revised ed. Harvard Univ. Press, Cambridge. MA.
• Goel, N.S., and J.M. Norman (ed.). 1990. Instrumentation for studying vegetation canopies for remote sensing in optical and thermal infrared regions. Harwood Acad. Publ., London.
• Granier, A. 1985. Une nouvelle meìthode pour la'mesure du flux de seÌve brute dans le tronc des arbres. Ann. Sci. For. 42:193–200.[CrossRef]
• Greene, J.S., B. Paris, and M. Morrissey. 2007. Historical changes in extreme precipitation events in the tropical Pacific region. Clim. Res. 34:1–14.
• Groisman, P.Y., R.W. Knight, and T.R. Karl. 2001. Heavy precipitation and high streamflow in the contiguous United States: Trends in the twentieth century. Bull. Am. Meteorol. Soc. 82:219–246.[CrossRef]
• Hammer, G.L., D.P. Holzworth, and R. Stone. 1996. The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Aust. J. Agric. Res. 47:717–737.[CrossRef][Web of Science]
• Hammer, G.L., N. Nicholls, and C. Mitchell. 2000. Applications of seasonal climate forecasting in agricultural and natural ecosystems. Kluwer Academic Publishers, Dordrecht, the Netherlands.
• Hansen, J.W. 2002. Realizing the potential benefits of climate prediction to agriculture: Issues, approaches, challenges. Agric. Syst. 74:309–330.[CrossRef][Web of Science]
• Hansen, J.W., A. Challinor, A. Ines, T. Wheeler, and V. Moron. 2006a. Translating climate forecasts into agricultural terms: Advances and challenges. Clim. Res. 33:27–41.
• Hansen, J.W., M.V.K. Sivakumar, and B.C. Bates (ed.). 2006b. Advances in applying climate prediction to agriculture. CR Special 16. Clim. Res. 33(1)1–122.
• Hatfield, J.L. 1983. Remote sensing estimators of potential and actual crop yield. Remote Sens. Environ. 13:301–311.
• Hatfield, J.L. 1985. Wheat canopy resistance determined by energy balance techniques. Agron. J. 77:279–283.[Abstract/Free Full Text]
• Hatfield, J.L. 1990. Agroclimatology of semiarid lands. Adv. Soil Sci. 13:9–26.
• Hatfield, J.L. 2005. The farmer's decision: Balancing economic successful agricultural production with environmental quality. Soil and Water Conserv. Soc., Ankeny, IA.
• Hatfield, J.L., and R.G. Allen. 1996. Evapotranspiration estimates under deficient water supplies. J. Irrig. Drain. Eng. 122:301–308.[CrossRef]
• Hatfield, J.L., and J.M. Baker (ed.). 2005. Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Hatfield, J.L., A.A. Gitelson, J.S. Schepers, and C.L. Walthall. 2008. Remote sensing of agronomic parameters: Advances in science. Agron. J. 100 (suppl):xxx–xxx.
• Hatfield, J.L., and G.F. Hart. 2003. Agricultural Research Service Contributions to Remote Sensing, Special Issue. Photogram. Eng. Remote Sens. 69(6)613–718.
• Hatfield, J.L., J.H. Prueger, and W.P. Kustas. 2004. Remote sensing of dryland crops. p. 531–568. In S.L. Ustin (ed.) Remote sensing for natural resource management and environmental monitoring, manual of remote sensing. Vol. 4. 3rd ed. John Wiley, Hoboken, NJ.
• Hatfield, J.L., R.J. Reginato, and S.B. Idso. 1984. Evaluation of canopy temperature– evapotranspiration models over various crops. Agric. For. Meteorol. 32:41–53.
• Heilman, J.L., W.E. Heilman, and D.G. Moore. 1981. Remote sensing of canopy temperature at incomplete cover. Agron. J. 73:403–406.[Abstract/Free Full Text]
• Hendrey, G.R., K.E. Lewin, and J. Nagy. 1993. Free air carbon dioxide enrichment: Development, progress, results. Vegetatio 104–105:17–31.
• Hendrey, G.R., and B.A. Kimball. 1994. The FACE program. Agric. For. Meteorol. 70:3–14.
• Hillel, D. 1971. Soil and water. Physical principles and processes. Academic Press, New York.
• Huang, B., J.W. Johnson, J.E. Box, and D.S. NeSmith. 1997a. Root characteristics and hormone activity of wheat in response to hypoxia and ethylene. Crop Sci. 37:812–818.[Abstract/Free Full Text]
• Huang, B., J.W. Johnson, and D.S. NeSmith. 1997b. Responses to root-zone CO₂ enrichment and hypoxia of wheat genotypes differing in waterlogging tolerance. Crop Sci. 37:464–468.[Abstract/Free Full Text]
• Idso, S.B. 1970. The relative sensitivities of polyethylene shielded net radiometers for short and long wave radiation. Rev. Sci. Instrum. 41:939–943.[Web of Science]
• Idso, S.B. 1981. A set of equations for full spectrum and 8- to 14-micrometre and 10.5- to 12.5-micrometre thermal radiation from cloudless skies (Phoenix, Arizona). Water Resour. Res. 17:295–304.[CrossRef]
• Idso, S.B., J.K. Aase, and R.D. Jackson. 1975. Net radiation- soil heat flux relations as influenced by soil water content variations. Boundary-Layer Meteorol. 9:113–122.
• Idso, S.B., R.D. Jackson, P.J. Pinter, Jr., R.J. Reginato, and J.L. Hatfield. 1981. Normalizing the stress-degree-day parameter for environmental variability. Agric. For. Meteorol. 24:45–55.
• Inoue, E. 1963. On the turbulent structure of air flow within crop canopies. J. Meteorol. Soc. Jpn. 41:317–325.
• IPCC. 2007a. Summary for policymakers. In S. Solomon et al. (ed.) Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, UK, and New York.
• IPCC. 2007b. Summary for policymakers. In Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, UK, and New York.
• IPCC. 2007c. Summary for policymakers. In B. Metz et al. (ed.) Climate change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, UK, and New York.
• Itai, C., and Y. Vaadia. 1965. Kinetin-like activity in root exudate of water-stressed sunflower plants. Physiol. Plant. 18:941–944.
• Jackson, R.D., S.B. Idso, R.J. Reginato, and P.J. Pinter, Jr. 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17:1133–1138.[CrossRef]
• Jensen, M.E., D.C.N. Robb, and C.E. Franzoy. 1970. Scheduling irrigation using climate-crop-soil data. J. Irrig. Drain. Div. ASCE 96:25–28.
• Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT cropping system model. Eur. J. Agron. 18:235–265.[CrossRef]
• Kanemasu, E.T., G.W. Thurtell, and C.B. Tanner. 1969. The design, calibration, and field use of a stomatal diffusion porometer. Plant Physiol. 44:881–885.[Abstract/Free Full Text]
• Karl, T.R., and R.W. Knight. 1998. Secular trends of precipitation amount, frequency, and intensity in the USA. Bull. Am. Meteorol. Soc. 79:231–241.[CrossRef]
• Katz, Y.H. 1952. The relationship between heat unit accumulation and planting and harvesting of canning peas. Agron. J. 44:74–78.[Free Full Text]
• Keeling, C.D., R.B. Bacastow, A.E. Bainbridge, C.A. Ekdahl, Jr., P.R. Guenther, L.S. Waterman, and J.F.S. Chin. 1976. Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus 28:538–551.
• Kimball, B.A. 1983. Carbon dioxide and agricultural yield. An assemblage of 430 prior observations. Agron. J. 75:779–788.
• Kimball, B.A., R.D. Jackson, F.S. Nakayama, S.B. Idso, and R.J. Reginato. 1976a. Soil-heat-flux determination: Temperature gradient method with computed thermal conductivities. Soil Sci. Soc. Am. J. 40:25–28.[Abstract/Free Full Text]
• Kimball, B.A., R.D. Jackson, R.J. Reginato, F.S. Nakayama, and S.B. Idso. 1976b. Comparison of field-measured and calculated soil-heat-fluxes. Soil Sci. Soc. Am. J. 40:18–25.[Abstract/Free Full Text]
• Kimball, B.A., K. Kobayashi, and M. Bindi. 2002. Responses of agricultural crops to free-air CO₂ enrichment. Adv. Agron. 77:293–368.[CrossRef]
• Kohsiek, W., C. Liebethal, T. Foken, R. Vogt, S.P. Oncley, Ch. Bernhofer, and H.A.R. Debruin. 2007. The energy balance experiment EBEX-2000: III. Behavior and quality of the radiation measurements. Boundary-Layer Meteorol. 123:55–75.
• Köppen, W., and R. Geiger. 1928. Klimakarte der Erde, Wall-map 150 cm x 200 cm. Gotha, Verlag Justus Perthes.
• Kroeze, C., A. Mosier, and L. Bouwman. 1999. Closing the global N₂O budget: A retrospective analysis 1500–1994. Global Biogeochem. Cycles 13:1–8.[Medline]
• Kunkel, K., K. Andsager, and D.R. Easterling. 1999. Long-term trends in heavy precipitation events over the continental United States. Jaclyn. 12:2515–2527.
• Kustas, W.P., J.H. Prueger, L.E. Hipps, J.L. Hatfield, and D. Meck. 1998. Inconsistencies in net radiation estimates from use of several models of instruments in a desert environment. Agric. For. Meteorol. 90:257–263.
• Lana, E.P., and E.S. Haber. 1952. Seasonal variability as indicated by cumulative degree-hours with sweet corn. J. Am. Soc. Hortic. Sci. Proc. 59:389–392.
• Law, B.E., E. Falge, L. Gu, D.D. Baldocchi, P. Bakwin, P. Berbigier, K. Davis, A.J. Dolman, M. Falk, J.D. Fuentes, A. Goldstein, A. Granier, A. Grelle, D. Hollinger, I.A. Janssens, P. Jarvis, N.O. Jensen, G. Katul, Y. Mahli, G. Matteucci, T. Meyers, R. Monson, W. Munger, W. Oechel, R. Olson, K. Pilegaard, K.T. Paw U, H. Thorgeirsson, R. Valentini, S. Verma, T. Vesala, K. Wilson, S. Wofsy. 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 113:97–120.[CrossRef]
• Leakey, A.D.B., M. Uribelarrea, E.A. Ainsworth, S.L. Naidu, A. Rogers, D.R. Ort, and S.P. Long. 2006. Photosynthesis, productivity, and yield of maize are not affected by open-air elevation of CO₂ concentration in the absence of drought. Plant Physiol. 140:779–790.[Abstract/Free Full Text]
• Lemeur, R., and B.L. Blad. 1975. A critical review of light models for estimating the shortwave radiation regime of plant canopies. p. 255–286. In J.F. Stone et al. (ed.) Plant modification for more efficient water use. Elsevier, Amsterdam.
• Lemon, E.R. 1960. Photosynthesis under field conditions: II. An aerodynamic method for determining the turbulent carbon dioxide exchange between the atmosphere and a corn field. Agron. J. 52:697–703.
• Linacre, E.T. 1968. Estimating net radiation flux. Agric. For. Meteorol. 5:49–63.
• Livingston, G.J. 1908. An annotated bibliography of evaporation. Mon. Weather Rev. 36:181–186.
• Lokupitiya, E., and K. Paustian. 2006. Agricultural soil greenhouse gas emissions: A review of national inventory methods. J. Environ. Qual. 35:1413–1427.[Abstract/Free Full Text]
• Long, S.P., E.A. Ainsworth, A. Rogers, and D.R. Ort. 2004. Rising atmospheric carbon dioxide: Plants FACE the future. Ann. Rev. Plant Biol. 55:591–628.
• Madariaga, F.J., and J.E. Knott. 1951. Temperature summation in relation to lettuce growth. J. Am. Soc. Hortic. Sci. Proc. 58:147–152.
• Manabe, S., and R.T. Wetherald. 1967. Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci. 24:241–259.[CrossRef]
• Marsh, G.P. 1865. Man and nature: On physical geography as modified by human action. Charles Schribner, New York.
• McCown, R.L., Z. Hochman, and P.S. Carberry. 2002. Probing the enigma of the decision support system for farmers. Special Issue. Agric. Syst. 74(1)1–220.
• McInnes, K.J., and J.L. Heilman. 2005. Relaxed eddy accumulation. p. 437–454. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• McKree, K.J. 1972. The action spectrum, absorptance and quantum yield of photosynthesis in crop plants. Agric. For. Meteorol. 10:443–453.
• McKree, K.J. 1973. A rational approach to light measurements in plant ecology. Curr. Adv. Plant Sci. 3:39–43.
• Meyers, T.P., and D.D. Baldocchi. 2005. Current micrometeorological flux methodologies with application in agriculture. p. 381–396. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Meyers, T.P., and S.E. Hollinger. 2004. An assessment of storage terms in the surface energy balance of maize and soybean. Agric. For. Meteorol. 125:105–115.[CrossRef]
• Monin, A.S., and A.M. Obukhov. 1954. Basic laws of turbulent mixing in the ground layer of the atmosphere. Akud. Nauk SSSR Geofiz. Inst. Tr. 151:163–187.
• Monsi, M., and T. Saeki. 1953. Uber der licthfactor in den pfalnzengellschaften and seine bedeutung fur die stoffproduktion. Jpn. J. Bot. 14:22–52.
• Monteith, J.L. 1964. Evaporation and the environment. In The State and Movement of Water in Living Organisms. Symp. Soc. Exp. Biol. 19:205–234.
• Monteith, J.L. 1965. Light distribution and photosynthesis in field crops. Ann. Bot. N.S. 29:17–37.
• Monteith, J.L. 1973. Principles of environmental physics. Edward Arnold Limited, London.
• Monteith, J.L. 1994. Principles of resource capture by crop stands. p. 1–15. In J.L. Monteith et al. (ed.) Resource capture by crops. Nottingham Univ. Press, Leicestershire, England.
• Monteith, J.L., and M.H. Unsworth. 1990. Principles of environmental physics. 2nd ed. Edward Arnold Limited, London.
• Moran, M.S., T.R. Clarke, Y. Inoue, and A. Vidal. 1994. Estimating crop water deficit using the relation between surface–air temperature and spectral vegetation index. Remote Sens. Environ. 49:246–263.[CrossRef]
• Morgan, J.A., A.R. Mosier, D.G. Milchunas, D.R. LeCain, J.A. Nelson, and W.J. Parton. 2004. CO₂ enhances productivity, alters species composition, and reduces digestibility of shortgrass steppe vegetation. Ecol. Appl. 14:208–219.[CrossRef]
• Morgan, A.J., D.G. Milchunas, D.R. LeCain, M. West, and A.R. Mosier. 2007. Carbon dioxide enrichment alters plant community structure and accelerates shrub growth in the shortgrass steppe. Proc. Natl. Acad. Sci. USA 104:14,724–14,729.
• Muchow, R.C., and J.A. Bellamy (ed.). 1991. Climatic risk in crop production: Models and management for semiarid tropics and subtropics. CAB International, Wallingford, UK.
• Nicks, A.D., and J.F. Harp. 1980. Stochastic generation of temperature and solar radiation data. J. Hydrol. 48:1–7.
• Norman, J.M., W.P. Kustas, and K.S. Humes. 1995. A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Meteorol. 77:263–293.
• Norman, J.M., W.P. Kustas, J.H. Prueger, and G.R. Diak. 2000. Surface flux estimation using radiometric temperature: A dual-temperature-difference method to minimize measurement errors. Water Resour. Res. 36:2263–2274.
• Ochsner, T.E., T.J. Sauer, and R. Horton. 2006. Field tests of the soil heat flux plate method and some alternatives. Agron. J. 98:1005–1014.[Abstract/Free Full Text]
• Ochsner, T.E., T.J. Sauer, and R. Horton. 2007. Soil heat storage measurements in energy balance studies. Agron. J. 99:311–319.[Abstract/Free Full Text]
• Olesen, J.E. 2006. Reconciling adaptation and mitigation to climate change in agriculture. J. Physique. IV: JP 139:403–411.
• Omernik, J.M. 1987. Ecoregions of the conterminous United States. Map (scale 1:7,500,000). Ann. Assoc. Am. Geograph. 77(1):118-
• Omernik, J.M. 2004. Perspectives on the nature and definition of ecological regions. Environ. Manage. 34(Suppl. 1):s27–s38.
• O'Toole, J.C., N.C. Turner, O.P. Namuco, M. Dingkuhn, and K.A. Gomez. 1984. Comparison of some crop water stress measurement methods. Crop Sci. 24:1121–1128.[Abstract/Free Full Text]
• Parry, M., C. Rosenzweig, A. Iglesias, G. Fischer, and M. Livermore. 1999. Climate change and world food security: A new assessment. Glob. Environ. Change 9(Suppl.):S51–S67.
• Patel, N.R., A.N. Mehta, and A.M. Shekh. 2001. Canopy temperature and water stress quantification in rainfed pigeonpea (Cajanus cajan (L.) Millsp.). Agric. For. Meteorol. 109:223–232.
• Paw U, K.T., R.L. Snyder, D. Spano, and H.B. Su. 2005. Surface renewal estimates of scalar exchange. p. 455–483. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Pearcy, R.W., J. Ehleringer, H.A. Mooney, and P.W. Rundel. 1989. Plant physiological ecology. Chapman & Hall, London.
• Penman, H.L. 1948. Natural evaporation from open water, bare soil, and grass. Proc. R. Soc. London Ser. A 193:120–145.[Abstract/Free Full Text]
• Penning de Vries, F.W.T. (ed.). 1982. Simulation of plant growth and crop production. Center Agric. Publ. and Document, Wageningen, the Netherlands.
• Perez, C.A., C. Neely, C. Roncoli, and J. Steiner (ed.). 2007. Making carbon sequestration work for Africa's rural poor: Opportunities and constraints. Special Issue. Agric. Syst. 94(1)1–109.
• Phillips, J.G., D. Deane, L. Unganai, and A. Chimeli. 2002. Implications of farm-level response to seasonal climate forecasts for aggregate grain production in Zimbabwe. Agric. Syst. 74:351–369.[CrossRef][Web of Science]
• Phillips, J., and B. McIntyre. 2000. ENSO and interannual rainfall variability in Uganda: Implications for agricultural management. Int. J. Clim. 20:171–182.
• Pinter, P.J., Jr., M.E. Stenghellini, R.J. Reginato, S.B. Idso, A.D. Jenkins, and R.D. Jackson. 1979. Remote detection of biological stresses in plants with infrared thermometry. Science 205:585–587.[Abstract/Free Full Text]
• Priestley, C.H.B., and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mo. Weather Rev. 100:81–92.
• Prueger, J.H., and W.P. Kustas. 2005. Aerodynamic methods for estimating turbulent fluxes. p. 407–436. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Radford, P.J. 1967. Growth analysis formulae: Their use and abuse. Crop Sci. 7:171–175.[Abstract/Free Full Text]
• Raschke, K. 1960. Heat transfer between the plant and the environment. Annu. Rev. Plant Physiol. 11:111–126.[Medline]
• Reynolds, J.F., D.M. Stafford Smith, E.F. Lambin, B.L. Turner, II, M. Mortimore, S.P.J. Batterbury, T.E. Downing, H. Dowlatabadi, R.J. Fernández, J.E. Herrick, E. Huber-Sannwald, H. Jiang, R. Leemans, T. Lynam, F.T. Maestre, M. Ayarza, and B. Walker. 2007. Global desertification: Building a science for dryland development. Science 316:847–851.[Abstract/Free Full Text]
• Rice, C.W. 2006. Introduction to special section on greenhouse gases and carbon sequestration in agriculture and forestry. J. Environ. Qual. 35:1338–1340.[Free Full Text]
• Richardson, C.W. 1981. Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resour. Res. 17:182–190.
• Ritchie, G.A., and T.M. Hinckley. 1975. The pressure chamber as an instrument for ecological research. Adv. Ecol. Res. 9:165–253.[CrossRef]
• Ritchie, J.T. 1971. Dryland evaporative flux in a subhumid climate: I. Micrometeorological influences. Agron. J. 63:51–55.
• Ritchie, J.T., and E. Burnett. 1971. Dryland evaporative flux in a subhumid climate: II. Plant influences. Agron. J. 63:56–62.
• Ritchie, J.T. 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resour. Res. 8:12–4-1213.
• Robinson, J.B., and D.G. Butler. 2002. An alternative method for assessing the value of the Southern Oscillation Index (SOI), including case studies of its value for crop management in the northern grainbelt of Australia. Austr. J. Agric. Res. 53:423–428.
• Rosenberg, N.J. 1974. Microclimate, the biological environment. John Wiley & Sons, New York.
• Rosenberg, N.J., B.L. Blad, and S.B. Verma. 1983. Microclimate, the biological environment. John Wiley & Sons, New York.
• Ross, J. 1975. Radiative transfer in plant communities. p. 13–55. In J.L. Monteith (ed.) Vegetation and the atmosphere. Vol. 1. Principles. Academic Press, New York.
• Rummel, D.R., and J.L. Hatfield. 1989. A thermal-based emergence model for Heliothis zea (Boddie) (Lepidoptera: Noctuidae) in the Texas High Plains. J. Econ. Entomol. 81:1620–1623.[Web of Science]
• Sadras, V., D. Roget, and M. Krause. 2003. Dynamic cropping strategies for risk management in dry-land farming systems. Agric. Syst. 76:929–948.[Web of Science]
• Schlenker, W., W.M. Hanemann, and A.C. Fisher. 2007. Water availability, degree days, and the potential impact of climate change on irrigated agriculture in California. Clim. Change 81:19–38.
• Schneider, J.M., and J.D. Garbrecht. 2003a. A measure of the usefulness of seasonal precipitation forecasts for agricultural applications. Trans. ASAE 46:257–267.[Web of Science]
• Schneider, J.M., and J.D. Garbrecht. 2003b. Regional utility of NOAA/CPC seasonal climate precipitation forecasts. Proc. Symp. on Watershed Management and Restoration, World Water and Environmental Resources Congress. Environ. and Water Res. Inst., Am. Soc. Civ. Eng., Reston, VA.
• Schneider, J.M., and J.D. Garbrecht. 2006. Dependability and effectiveness of seasonal forecasts for agricultural applications. Trans. ASAE 49:1737–1753.
• Scholander, P.F., H.T. Hammel, E.D. Broadstreet, and E.A. Hemmingsen. 1965. Sap pressure in vascular plants. Science 148:339–346.[Abstract/Free Full Text]
• Semenov, M.A. 2006. Using weather generators in crop modelling. Acta Hortic. 707:93–100.
• Semenov, J.A., and F.J. Doblas-Reyes. 2007. Utility of dynamical seasonal forecasts in predicting crop yield. Clim. Res. 34:71–81.
• Shibles, R.M., and C.R. Weber. 1966. Interception of solar radiation and dry matter production by various soybean planting patterns. Crop Sci. 5:53–56.[Medline]
• Shimshi, D. 1969. A rapid field method for measuring photosynthesis with labelled carbon dioxide. J. Exp. Bot. 20:381–401.[Abstract/Free Full Text]
• Sivakumar, M.V.K. 2006. Climate prediction and agriculture: Current status and future challenges. Clim. Res. 33:3–17.
• Stanhill, G., G.J. Hofstede, and J.D. Kalma. 1966. Radiation balance of natural and agricultural vegetation. Q. J. R. Meteorol. Soc. 92:128–140.
• Steduto, P., O. Cetinkoku, R. Albrizio, and R. Kanber. 2002. Automated closed-system canopy-chamber for continuous field-crop monitoring of CO₂ and H₂O fluxes. Agric. For. Meteorol. 111:171–186.
• Steiner, J.L. 1994. Crop residue effects on water conservation. p. 41–76. In P.W. Unger (ed.) Managing agricultural residues. Lewis Publ., Boca Raton, FL.
• Steiner, J.L. 2002. Soil climatology and meteorology. Entry 5.24.01.07 In R. Lal (ed.) Agricultural Sciences, in Encyclopedia of Life Support Systems (EOLSS). Developed under the Auspices of the UNESCO, EOLSS Publishers, Oxford, UK. Available at www.eolss.net (verified 7 Dec. 2007).
• Steiner, J.L., J.C. Day, R.I. Papendick, R.E. Meyer, and A.R. Bertrand. 1988. Improving and sustaining productivity in dryland regions of developing countries. Adv. Soil Sci. 8:79–122.
• Steiner, J.L., J.M. Schneider, J.D. Garbrecht, and X.J. Zhang. 2004. Climate forecasts: Emerging potential to reduce dryland farmers' risk. p. 47–65. In S.C. Rao and J. Ryan (ed.) Challenges and strategies of dryland agriculture. CSSA Spec. Publ. 32. CSSA, Madison, WI.
• Stewart, B.A., and J.L. Steiner. 1990. Water use efficiency. Adv. Soil Sci. 13:151–173.
• Stewart, J.I., and C.T. Hash. 1982. Impact of weather analysis on agricultural production and planning decisions for the semiarid areas of Kenya. J. Appl. Meteorol. 21:477–494.
• Stewart, J.I., and D.A.R. Kashasha. 1984. Rainfall criteria to enable response farming through crop-based climate analysis. East Afr. Agric. For. J. 44:58–79.
• Stewart, J.I., and W.A. Faught. 1984. Response farming of maize and beans at Katumani, Machakos District, Kenya: Recommendations, yield expectations, and economic benefits. East Afr. Agric. For. J. 44:29–51.
• Stewart, J.I. 1988. Response farming in rainfed agriculture. The Wharf Foundation Press, Davis, CA.
• Stigter, C.J. 2007. Guest editorial. From basic agrometeorological science to agrometeorological services and information for agricultural decision makers: A simple conceptual an diagnostic framework. Agric. For. Meteorol. 142:91–95.
• Stone, R.C., G.L. Hammer, and T. Marcussen. 1996. Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384:252–255.[CrossRef][Web of Science]
• Streck, N.A., A. Weiss, and P.S. Baenziger. 2003. A generalized vernalization response function for winter wheat. Agron. J. 95:155–159.[Abstract/Free Full Text]
• SWCS. 2003. Conservation implications of climate change: Soil erosion and runoff from cropland. Soil and Water Conservation Society, Ankeny, IA. Available at www.swcs.org (verified 10 Dec. 2007).
• SWCS. 2007. Planning for extremes. Soil and Water Conservation Society, Ankeny, IA. Available at www.swcs.org (verified 10 Dec. 2007).
• Swinbank, W.C. 1951. The measurement of vertical transfer of heat and water vapor by eddies in the lower atmosphere. J. Meteorol. 8:135–145.
• Szeicz, G., J.L. Monteith, and J.M. Dos Santos. 1964. Tube solarimeter to measure radiation among plants. J. Appl. Ecol. 1:169–174.
• Tanner, C.B. 1960. Energy balance approach to evapotranspiration from crops. Soil Sci. Soc. Am. Proc. 24:1–9.
• Tanner, C.B. 1963. Plant temperature. Agron. J. 55:210–211.[Free Full Text]
• Tanner, C.B., and T.R. Sinclair. 1983. Efficient water use in crop production: Research or re-search. p. 1–27. In H.J. Taylor et al. (ed.) Limitations to efficient water use in crop production. ASA, CSSA, SSSA, Madison, WI.
• Taylor, H.M., W.R. Jordan, and T.R. Sinclair (ed.). 1983. Limitations to efficient water use in crop production. ASA, CSSA, SSSA, Madison, WI.
• Thornthwaite, C.W. 1948. An approach toward rational classification of climate. Geogr. Rev. 38:55–94.[CrossRef]
• Tolk, J.A., S.R. Evett, and T.A. Howell. 2006. Advection influences on evapotranspiration of alfalfa in a semiarid climate. Agron. J. 98:1646–1654.[Abstract/Free Full Text]
• UNESCO. 1977. World map of dessertification. United Nations Conf. on Desertification Rep. A/Con. 74/2. United Nations, New York.
• van Bavel, C.H.M. 1966. Potential evaporation: The combination concept and its experimental verification. Water Resour. Res. 2:455–467.
• van Bavel, C.H.M. 1967. Changes in canopy resistance to water loss from alfalfa induced by soil water depletion. Agric. For. Meteorol. 4:165–176.
• van Bavel, C.H.M., F.S. Nakayama, and W.L. Ehrler. 1965. Measuring transpiration resistance of leaves. Plant Physiol. 40:535–540.[Free Full Text]
• Verma, S.B., A. Doberman, K.G. Cassman, D.T. Walters, J.M. Knops, T.J. Arkebauer, A.E. Suyker, G.T.G. Burba, B. Amos, H. Yang, D. Ginting, K.G. Hubbard, A.A. Gitelson, and E.A. Walter-Shea. 2005. Annual carbon dioxide exchange in irrigated and rainfed maize-based ecosystems. Agric. For. Meteorol. 131:77–96.[CrossRef]
• Virgil. 29 B.C.E. The Georgics. Available at http://classics.mit.edu/Virgil/georgics.html (accessed 27 Sept. 2007; verified 10 Dec. 2007).
• Vogel, C., and K. O'Brien. 2006. Who can eat information? Examining the effectiveness of seasonal climate forecasts and regional climate-risk management strategies. Clim. Res. 33:111–122.
• Walker, G.L., and J.L. Hatfield. 1979. Test of the stress-degree-day concept using multiple planting dates of red kidney beans. Agron. J. 71:967–971.[Abstract/Free Full Text]
• Wanjura, D.F., and D.R. Upchurch. 2000. Canopy temperature characterizations of corn and cotton water status. Trans. ASAE 43:867–875.[Web of Science]
• Weatherley, P.E. 1950. Studies in the water relation of the cotton plant: I. The field measurement of water deficits in leaves. New Phytol. 49:81–97.
• Welles, J.M., and D.K. McDermitt. 2005. Measuring carbon dioxide in the atmosphere. p. 287–320. In J.L. Hatfield and J.M. Baker (ed.) Micrometeorology in agricultural systems. Agron. Monogr. 47. ASA, CSSA, SSSA, Madison, WI.
• Wiegand, C.L., and L.N. Namken. 1966. Influences of plant moisture stress, solar radiation, and air temperature on cotton leaf temperature. Agron. J. 58:582–586.[Abstract/Free Full Text]
• Williams, W.A., R.S. Loomis, and C.R. Lepley. 1965. Vegetative growth of corn as affected by population density: I. Productivity in relation to interception of solar radiation. Crop Sci. 5:211–215.
• Wratt, D.S., A. Tait, G. Griffiths, P. Espie, M. Jessen, J. Keys, M. Ladd, D. Lew, W. Lowther, N. Mitchell, J. Morton, J. Reid, S. Reid, A. Richardson, J. Sansom, and U. Shankar. 2006. Climate for crops: Integrating climate data with information about soils and crop requirements to reduce risks in agricultural decision-making. Meteorol. Applic. 13:305–315.
• Wright, J.L., and E.R. Lemon. 1966. Photosynthesis under field conditions: VIII. Analysis of windspeed fluctuation data to evaluate turbulent exchange within a corn crop. Agron. J. 58:255–261.
• Zhang, J., T.J. Griffis, and J.M. Baker. 2006. Using continuous stable isotope measurements to partition net ecosystem CO₂ exchange. Plant Cell Environ. 29:483–496.[Medline]
• Zhang, L., R. Lemeur, and J.P. Goutorbe. 1995. A one-layer resistance model for estimating regional evapotranspiration using remote sensing data. Agric. For. Meteorol. 77:241–261.
• Ziska, L.H. 2003. Evaluation of the growth response of six invasive species to past, present and future carbon dioxide concentrations. J. Exp. Bot. 54:395–404.[Abstract/Free Full Text]
• Ziska, L.H., J.R. Teasdale, and J.A. Bunce. 1999. Future atmospheric carbon dioxide may increase tolerance to glyphosate. Weed Sci. 47:608–615.

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