5. Agroclimatic indices to define corn production regions

Corn is produced around the world and within these areas there may be shifts in production areas due to the changing climate. Green et al. [43] have quantified the changes in the US Corn Belt and provided a geographic analysis to depict these shifts in distribution. Development and utilization of agroclimatic indices has value in being able to assess these shifts because they are related to temperature and precipitation. Neild and Richman [18] were among the first to use the GDD concept to define potential differences among corn hybrids. Development of tools to define where crops can be produced is critical to understand crop distribution and productivity [44]. Estimation of crop distribution within arable areas is necessary to determine whether a species can thrive in an agroclimatic zone and will become more critical with the projected increases in temperature. Zomer et al. [45] extended this concept to demonstrate how climate zones could be used to evaluate technologies that would enhance the ability of management practices to offset the impacts of climate change on crop production. There have continued to be advances in the development of agroclimatic indices to evaluate the suitability of a location for a particular crop since Neild and Richman [18]. Siddons et al. [46] cautioned that development of robust agroclimatic indices requires observations collected over long time periods and extensive observations from experimental locations. There has been an evolution in agroclimatic indices to include more factors affecting plant growth and development to derive values that characterize the environment and the potential for crop production. Typical factors are: average daily minimum temperatures below 0C; daily mean temperature to estimate crop development rates; average daily maximum temperature above 35�C to estimate exposure to heat stress, especially during pollination; average daily soil water availability (precipitation–reference evapotranspiration (ET)); and length of specific phenological periods to estimate the effects of changing phenological development on biomass accumulation and crop yield [47]. They found a positive relationship between productivity and their suitability index [47]. This approach is a refinement of the effort by Neild and Richman [18] and incorporated more factors to more link crop physiological responses with phenological development.

Agroclimatic zones are a combination of factors affecting plant growth to evaluate the potential for grain or forage crop production (e.g., [18, 44, 48–51]). The form of the index depends upon the assumption of the factors limiting growth. Soil water availability is often the determining factor in crop production in all ecosystems and the application has ranged from determination of irrigation water requirements or potential impacts on production caused by water deficits. Daccache et al. [49] incorporated soil water variability to evaluate the need for irrigation for potato (Solanum tuberosum L.) production in England and Wales. Their index was based on the potential soil moisture deficit (PSMD) index defined as:

$$PSMD\_i = PSMD\_{i-1} + ET\_i - P\_i \tag{1}$$

WRi ¼ PETi x kci (3)

Climate Change Impacts on Corn Phenology and Productivity

http://dx.doi.org/10.5772/intechopen.76933

105

WAi ¼ Preci � SWi�<sup>1</sup> (4)

<sup>i</sup>¼<sup>1</sup> WR (5)

where WRi is the water requirements for a decadal period during growing season, PETi is the potential ET during this decadal period, and kci the crop coefficient for this corresponding phenological period. For any decadal period during the growing season, the soil water balance

and Preci is the precipitation in a given decadal period and SWi-1 is the profile soil water content for the previous decadal period. Soil water holding capacity (WHC) becomes a critical component of this method because available SW is a function of WHC. They computed the

WRSIi <sup>¼</sup> WRSIi�<sup>1</sup> � WDi

Using this methodology, Moeletsi and Walker [51] were able to evaluate the suitability for maize production for various planting dates with a correlation of 0.8 between the PACSI and

Precipitation is changing in intensity and frequency, and directly affect WAi (Eq. 3). Precipitation patterns are projected to increase in annual totals, with decreasing summer precipitation amounts over the US [1, 53]. If we link these precipitation patterns with the PACSI (Eq. 2), then corn production could become more variable among years because of soil water availability. Utilization of agroclimatic indices as a tool for the assessment of climate impacts on corn production areas will provide a quantitative view of shifts in production areas but potential risks to production within areas where corn is currently produced. The continued development of these tools will benefit corn production because we can evaluate the potential role of

management and genetic resources on increasing yield stability over time.

Pend

WDi ¼ WRi � Preci � SWi�<sup>1</sup> when WRi > Preci þ SWi�<sup>1</sup> (6)

WDi ¼ 0 when WRi ¼ Preci þ SWi�<sup>1</sup> (7)

SWi ¼ Preci þ SWi�<sup>1</sup> � WRi (8)

SWi ¼ WHC when SWi ¼ WHC (9)

SWi ¼ 0 when SWi ¼ 0 (10)

can be used to estimate plant available water (WAi) as

with WDi the water deficit for decadal period i, defined as

In this process soil water in the profile is quantified as

WRSI as

Or

grain yields.

where PSMDi is the value in month i and PSMDi�<sup>1</sup> is the value for the previous month, ETi is the reference ET for the current month calculated with the Penman-Monteith equation formulated by Allen et al. [52], and Pi the precipitation in the current month. They found increased variation in precipitation decreased potato production in an area currently suited for production, unless supplemental water was provide through irrigation. This type of analyses could be utilized to determine the need for supplemental irrigation to ensure crop production.

Another form of this type of framework was developed by Moeletsi and Walker [51] to quantify climate risk for corn production in South Africa. They based their index, Poone AgroClimatic Suitability Index (PACSI), on three climatic parameters; onset of rains, frost risk, and drought risk utilized a weighed distribution of climate parameters as

$$PASCI = O \ge 0.3 + FF \ge 0.3 \ge WRSI \ge 0.4 \tag{2}$$

where O is the probability planting conditions are met, FF is the probability of a frost-free growing period, and the water requirements satisfaction index (WRSI). These indices require sufficient data over a long period of record to develop the probability of the different indices to develop reliable probability assessments [46]. An aspect of this index is the assessment of drought risk which is a complex interaction by soil water holding capacity and any change in the soil affecting water availability (Eq. 2).

Precipitation effects on crop productivity are defined by the occurrence of the water deficits in the soil profile which fail to meet the evaporative demand. Agroclimatic indices for arid and semiarid regions are often based on precipitation amounts adequate to exceed the ET rate at the time of planting in order to ensure crop establishment [18, 47–49, 51]. Moeletsi and Walker [51] evaluated soil water dynamics based on the WRSI to determine the potential to meet crop water requirements at any phenological stage as

$$\text{WR}\_{i} = \text{PET}\_{i} \ge k\_{ci} \tag{3}$$

where WRi is the water requirements for a decadal period during growing season, PETi is the potential ET during this decadal period, and kci the crop coefficient for this corresponding phenological period. For any decadal period during the growing season, the soil water balance can be used to estimate plant available water (WAi) as

$$WA\_i = Precc\_i - SW\_{i-1} \tag{4}$$

and Preci is the precipitation in a given decadal period and SWi-1 is the profile soil water content for the previous decadal period. Soil water holding capacity (WHC) becomes a critical component of this method because available SW is a function of WHC. They computed the WRSI as

$$\text{WRSI}\_{i} = \text{WRSI}\_{i-1} - \frac{\text{WD}\_{i}}{\sum\_{i=1}^{end} \text{WR}} \tag{5}$$

with WDi the water deficit for decadal period i, defined as

$$\text{W}D\_i = \text{WR}\_i - \text{Prec}\_i - \text{SW}\_{i-1} \text{ when } \text{WR}\_i > \text{Prec}\_i + \text{SW}\_{i-1} \tag{6}$$

Or

estimate crop development rates; average daily maximum temperature above 35�C to estimate exposure to heat stress, especially during pollination; average daily soil water availability (precipitation–reference evapotranspiration (ET)); and length of specific phenological periods to estimate the effects of changing phenological development on biomass accumulation and crop yield [47]. They found a positive relationship between productivity and their suitability index [47]. This approach is a refinement of the effort by Neild and Richman [18] and incorporated more factors to more link crop physiological responses with phenological development. Agroclimatic zones are a combination of factors affecting plant growth to evaluate the potential for grain or forage crop production (e.g., [18, 44, 48–51]). The form of the index depends upon the assumption of the factors limiting growth. Soil water availability is often the determining factor in crop production in all ecosystems and the application has ranged from determination of irrigation water requirements or potential impacts on production caused by water deficits. Daccache et al. [49] incorporated soil water variability to evaluate the need for irrigation for potato (Solanum tuberosum L.) production in England and Wales. Their index was

where PSMDi is the value in month i and PSMDi�<sup>1</sup> is the value for the previous month, ETi is the reference ET for the current month calculated with the Penman-Monteith equation formulated by Allen et al. [52], and Pi the precipitation in the current month. They found increased variation in precipitation decreased potato production in an area currently suited for production, unless supplemental water was provide through irrigation. This type of analyses could be

Another form of this type of framework was developed by Moeletsi and Walker [51] to quantify climate risk for corn production in South Africa. They based their index, Poone AgroClimatic Suitability Index (PACSI), on three climatic parameters; onset of rains, frost risk,

where O is the probability planting conditions are met, FF is the probability of a frost-free growing period, and the water requirements satisfaction index (WRSI). These indices require sufficient data over a long period of record to develop the probability of the different indices to develop reliable probability assessments [46]. An aspect of this index is the assessment of drought risk which is a complex interaction by soil water holding capacity and any change in

Precipitation effects on crop productivity are defined by the occurrence of the water deficits in the soil profile which fail to meet the evaporative demand. Agroclimatic indices for arid and semiarid regions are often based on precipitation amounts adequate to exceed the ET rate at the time of planting in order to ensure crop establishment [18, 47–49, 51]. Moeletsi and Walker [51] evaluated soil water dynamics based on the WRSI to determine the potential to meet crop

utilized to determine the need for supplemental irrigation to ensure crop production.

and drought risk utilized a weighed distribution of climate parameters as

the soil affecting water availability (Eq. 2).

water requirements at any phenological stage as

PSMDi ¼ PSMDi�<sup>1</sup> þ ETi � Pi (1)

PASCI ¼ O x 0:3 þ FF x 0:3 x WRSI x 0:4 (2)

based on the potential soil moisture deficit (PSMD) index defined as:

104 Corn - Production and Human Health in Changing Climate

$$\text{WMD}\_{i} = 0 \text{ when } \text{WR}\_{i} = \text{Prec}\_{i} + \text{SW}\_{i-1} \tag{7}$$

In this process soil water in the profile is quantified as

$$SW\_i = Prec\_i + SW\_{i-1} - WR\_i \tag{8}$$

$$\text{SW}\_{i} = \text{WHC} \,\text{when } \text{SW}\_{i} = \text{WHC} \tag{9}$$

$$SW\_i = 0 \text{ when } SW\_i = 0 \tag{10}$$

Using this methodology, Moeletsi and Walker [51] were able to evaluate the suitability for maize production for various planting dates with a correlation of 0.8 between the PACSI and grain yields.

Precipitation is changing in intensity and frequency, and directly affect WAi (Eq. 3). Precipitation patterns are projected to increase in annual totals, with decreasing summer precipitation amounts over the US [1, 53]. If we link these precipitation patterns with the PACSI (Eq. 2), then corn production could become more variable among years because of soil water availability.

Utilization of agroclimatic indices as a tool for the assessment of climate impacts on corn production areas will provide a quantitative view of shifts in production areas but potential risks to production within areas where corn is currently produced. The continued development of these tools will benefit corn production because we can evaluate the potential role of management and genetic resources on increasing yield stability over time.
