Seasonal Et

Figure 5.39. Normalized corn production functions: (—) [61], ( ) [62], and

modified with several correction factors, and ET is determined by FAO methods [65]. Table 5.18 gives some selected values of ¡3 for a few crops [64]. For further reading see [66-68].

Equation (5.182) is used widely because of its simplicity. It is not difficult in most instances to estimate a reasonable value for ¡3 for prediction purposes. In many situations, ¡3 is close to the same from year to year for the same crop.

An equation accounting for variable water supply at different crop growth stages has been developed. Each stage is weighted by a sensitivity factor. It is suggested that the productivity related to the end of a growth stage be determined in order to estimate the reduction of yield in the subsequent growth stages. Such a hypothesis generally is accepted for the majority of herbaceous crop species. The following multiplicative equation applies:

i=i where i is the generic growth stage; and n is the number of growth stages considered. This equation was found to give improved predictions in only a few instances compared to the simpler Eq. (5.182).

Hanks [68] and Howell etal. [66] reviewed many types of yield ET models. Advanced yield ET models mainly address the effects of severe water deficits during critical crop growth periods and are called multiperiod or dated models. The effects of water deficits at specific crop growth stages are most likely related to stress effects on HI and not necessarily on TR.

Table 5.18. Transpiration ratio (TR), water use efficiency (DM/T ), normalized water use efficiency (kj), and yield sensitivity ( ß) values for selected crops

TRa

DM/ Tb

kd C

kd d

Crop

(kg kg-1)

(kgm-3)

(kgkPam-3)

(kgkPam-3)

ße

Grains

Barley

518

1.9

2.9

Corn

350

2.9

9.5 (11.8)

1.25

Sorghum

304

3.3

8.3

13.8(11.8)

0.90

Oats

583

1.7

Millet

267

3.7

9.4

Rice

682

1.5

1.1

Rye

634

1.6

Sunflower

557

1.8

0.95

Wheat

557

1.8

3.1

1.10-1.15

Legumes

Alfalfa

844

1.2

4.3 (5.0)

0.7-1.1

Chickpeas

638

1.6

4.8

Cowpeas

569

1.8

Guar

523

1.9

Navy beans

656

1.5

Peanuts

3.9

0.7

Soybeans

715

1.4

4.0(4.1)

0.9

Root

Potatoes

575

1.7

6.5 (5.5)

1.1

Sugar beets

377

2.7

0.7-1.1

Fiber

Cotton

568

1.8

0.85

Flax

783

1.3

d [37]. Numbers in parentheses are theoretical estimates.

d [37]. Numbers in parentheses are theoretical estimates.

Crop management (cultivar selection, planting date, sowing date, spatial distribution of the crop stand, fertilizer applications, etc.) directly affect cropyields through effects on radiation interception, the partitioning of transpiration from total ET, and the partitioning of economic yield from total dry matter.

Crop selection is an important management decision for maximizing profits. The cultivars or hybrid chosen may affect yield in several ways [66]: crop cycle length; HI (partitioning of dry matter into economic yield); disease and pest insect resistance; harvesting influences (avoidance of lodging, fruit, grain, or lint retention, etc.); and harvesting quality (marketing and/or storage properties). Although small differences may exist for TR within a species, major differences in other yield factors exist.

Early sowing can allow the crop to grow under conditions of lower evaporative demand, resulting in increased water-use efficiency as analyzed by Fereres etal. [69].

HI is affected by sowing rate and row spacing of the crop. Sowing density and spatial distribution of the crop determine the resulting crop geometry and affect crop yield and water influencing the solar radiation interception and the partitioning of T from ET.

Crop-production evapotranspiration relations assume that crop nutrition is adequate and nonlimiting to production. Crop-fertility management can have both positive and negative effects on crop productivity when the water supply is fixed and/or limited. Fertilizer applications to a crop with limited available water could result in early depletion of the limited soil water and the development of severe water deficits during later critical crop development stages. With sufficient available soil water and a nutrient-deficient soil, nutrient additions can result in improved root development and soil water extraction from deeper soil layers, resulting in yield increases. Inadequate crop nutrition under irrigation most often affects crop yield through reductions in leaf area, dry matter, and HI.

Removal of crop residues likewise affects soil nutrient availability and water relations. Such losses of nitrogen over a period of years eventually would reduce soil, fertility levels and also reduce WUE.

Weeds compete with crop plants for light, water, and nutrients, and especially under conditions of water deficits, this leads to yield losses. Weeds can be controlled by tillage and herbicides, as well as by single or combination crop rotations. The increased WUE resulting from tillage and herbicide applications manipulates the field water balance to differentially increase the partitioning of T from ET. Reducing tillage reduces the exposure of wet soil to evaporation, thereby conserving soil water but requiring chemical weed control to minimize transpiration from weeds. Too many tillage operations may increase the risk of soil crusting, runoff, and erosion.

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