Environment

Environmental factors such as temperature, solar radiation, and [CO2] directly affect plant processes, and models can use the current value of a factor, such as from a daily weather record, with minimal modification. The effects of these factors have been discussed individually in relation to specific processes, but their roles are summarized in Table 4.2. It is constructive to compare responses across processes.

4.2.4.1 Temperature

The CSM-Cropsim-CERES-Wheat model specifies nine temperature responses affecting development, photosynthesis, and different aspects of growth. Three responses for grain formation and growth have identical responses, so in practice,

Table 4.2 Summary of the effects of selected environmental factors on plant processes, including a subjective assessment of how completely current models simulate a given process

Factor

Process

Modeled

Comments

Temperature

Phenology

Full

Warming usually decreases time to flowering and maturity, but high temperatures may delay development

Photosynthesis

Full

Heat stress is poorly understood and seldom explicitly modeled

Respiration

Full

Rates increase with temperature

Leaf development

Partial

Models differ greatly how temperature affects leaf expansion and thickness

Reproductive growth

Partial

Heat stress is poorly understood and seldom explicitly modeled

Root elongation

Full

Rate increases with soil temperature, but soil temperatures are poorly modeled, including for climate change conditions

Potential évapotranspiration

Full

Potential water loss increases with temperature, as accurately predicted by Penman-Monteith equation

Mineralization of soil

Full

Rates increase with soil temperature

organic matter

CO, concentration

Development

Not

Effects vary with species and are not well enough understood to be modeled

Leaf development

Not

Not well enough understood to be modeled

Photosynthesis

Full

Basic response to CO, is well described by the Farquhar model, but controversies remain

Respiration

Not

Not fully accepted as existing

Transpiration

Full

Physiological mechanisms are poorly understood. Cultivar differences are likely but not considered in current models

Solar radiation

Photosynthesis

Full

Leaf and canopy responses are well described by models

Leaf development

Partial

Few models consider effects on leaf expansion and thickening

Potential évapotranspiration

Full

Potential water loss increases with radiation, as accurately predicted by Penman-Monteith equation

Wind

Potential évapotranspiration

Partial

Potential water loss increases with wind, as accurately predicted by Penman-Monteith equation

Relative humidity

Leaf development

Not

Not well enough understood to be modeled

Potential évapotranspiration

Partial

Potential water loss decreases with humidity, as accurately predicted by Penman-Monteith equation

Transpiration

Partial

Direct plant responses to humidity, including cultivar differences, are poorly understood

seven unique responses are recognized. Each response is described with four cardinal temperatures using a trapezoidal response curve (Fig. 4.3). Vernalization (the requirement some crops exhibit for a cold period) is unique in that it only operates from -5°C to 10°C, reflecting that this process quantifies a specific low temperature response. Pre-anthesis development is shown as continuing up to 60°C, but since average temperatures never reach these levels, the real and practical result is that the development rate is maximal above 25°C.

The actual curves are estimated through diverse procedures. Cardinal temperatures for development can be estimated with non-linear optimization, using field or controlled environments as data sources (e.g., Grimm et al. 1993). Specific physiological responses may be estimated by compiling data across studies. To define a response of leaf photosynthesis to temperature in wheat, Bindraban (1999) examined data from six publications, the previously established response for SUCROS, and his own field measurements. The review of temperature responses for wheat by Porter and Gawith (1999) shows the diversity of values that may be found for a single crop.

When multiple temperature responses are applied in real world situations, the crop responses can be surprisingly complex. For example, to assess the impact of a temperature increase on irrigated sorghum production in Arizona, one might examine the base response of contrasting hybrids to planting date using historic weather data and a simple increase of +1.5°C for daytime temperatures and +3.0°C for the nighttime (Fig. 4.6). For both temperature regimes, the hybrid Cargill 877 is about 20 days later than Cargill 577 for planting dates up to mid-August, when the difference increases due to cooler temperatures (Fig. 4.6a). The warmer temperature regime accelerates development resulting in earlier flowering, although the difference is less than 5 days for plantings from April through August. The response of grain yield (Fig. 4.6b), however, is much more complex. Yields are similar for both hybrids and climate regimes with mid-February plantings, but by early June, warming is predicted to reduce yields by about 500 kg ha-1. The yield effect increases up to August when there is a change in response; the warming regime becomes advantageous relative to the historic regime because the warming extends the growing season.

Water, nitrogen, and other factors that involve uptake from the soil into the plant require consideration of the availability of the resource in the soil, demand by the plant for the resource, and the ability of the plant to take up the resource via the roots. Consideration is also required of alternate pathways such as evaporation of water from the soil surface and return of nitrogen to the soil from senesced and abscised leaves. For a given soil resource, the overall basic process is readily described with an equation that balances sources of the resource against losses.

For water, sources may be precipitation (P) and irrigation (I), and losses are through evaporation (E), transpiration (T), surface runoff (R), storage in the soil (S), and deep percolation (D). Thus, a

Planting {day of year) C 577: +1.5/+3.0 —C 577: Historic --■C 877: +1.5/+3.0 —C 877: Historic

Fig. 4.6 Simulations using the CSM-CERES Sorghum model for response of planting date in two sorghum hybrids, Cargill 577 and Cargill 877, at Maricopa, Arizona. Weather data are for 14 years of historic conditions and for the same years assuming a warming scenario of +1.5°C for the daily maximum and +3.0°C for the minimum. (a) Days to flowering. (b) Grain yield

The values of P, I and R are essentially predetermined.

To estimate E and T, models first calculate the potential atmospheric demand through evaporation and transpiration, termed potential evapotranspiration (PET), from weather variables and crop canopy conditions. PET increases with solar radiation, temperature, and wind but decreases with relative humidity. The adaptation of the Penman-Monteith equation for PET (Monteith and Unsworth 1990) that is described in the FAO Drainage and Irrigation Paper No. 56 (Allen et al. 1988) usually is the basis of the estimated values, but numerous variants exist depending on the weather data that are available as input (Allen 1986). The basic equations for PET describe the moisture lost from a crop canopy that completely covers the ground. Numerous assumptions are used to account for evaporation from the soil surface or a mulch layer, the portion of the ground covered by the crop, and the aerodynamic characteristics of the crop. The potential rate for evaporation from the soil surface must further be adjusted for the relative wetness of the surface and how freely moisture moves upward from lower in the soil. The storage component in the soil is positive when soil moisture increases and negative if moisture is lost. The calculations of the soil water balance are complex, in part because of the need to consider soil properties that vary with depth and management (Ritchie 1998), and the different assumptions used underlie important differences among models.

On the plant side, the potential transpiration (PET - E) establishes the upper limit for water uptake by the crop. Actual transpiration is less than the potential if insufficient soil water is readily available. The available water depends both on the amount of moisture available to the plant at different soil depths and the distribution of roots in the soil. Models typically consider a field soil described with discrete horizontal layers, which may vary in water holding capacity, moisture content, and root content, expressed in terms of mass and length. The processes of estimating available water and actual root uptake of water are again too complex for this review but are described by Ritchie (1998). Excess water in the profile or standing water may result in stress since anaerobic conditions disrupt root function. Elevated [CO2] also reduces transpiration due to the increase in stomatal resistance with [CO2]. In the CSM model, two response curves are used depending on whether the species is C3 or C4 (Fig. 4.7).

4.2.4.3 Nitrogen and Other Nutrients

Similar balance approaches are used for nitrogen and other nutrients, with modifications for conversion of the nutrient to a form in the soil solution that the roots may take up. In the case of nitrogen, this requires simulating mineralization and immobilization of nitrogen, which in turn, requires tracking levels of ammonium, nitrate and soil organic matter (Godwin and Singh 1998). For grain legumes, biological fixation of

Fig. 4.7 Response to atmospheric [CO2] assumed in the CSM-CERES and CSM-CROPGRP models for leaf stomatal resistance (which affects net transpiration) for C3 crops (e.g., common bean, peanut, soybean, barley, oats, rice and wheat) and C4 crops (e.g., maize, millet and sorghum)

Atmospheric CO2 (ppm)

Fig. 4.7 Response to atmospheric [CO2] assumed in the CSM-CERES and CSM-CROPGRP models for leaf stomatal resistance (which affects net transpiration) for C3 crops (e.g., common bean, peanut, soybean, barley, oats, rice and wheat) and C4 crops (e.g., maize, millet and sorghum)

nitrogen is also considered. These soil processes respond to soil temperature and moisture status.

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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