2. Literature

2.1. Classification and selection of maize varieties

▼ 4 

Due to the unequal maturity of cob and residual plant (leaf/stem) maize varieties fall in two major categories. Maize varieties whose cobs mature faster than residual plants (stay-green) and varieties whose residual mature faster than the cob. Until 1998 maturity classification of silage maize by the Federal Variety Authority (Bundessortenamt) was done solely through dry matter content of the cob (Hartmann & Geiger 2001). However with the introduction of stay-green varieties, the view on exclusive maturity assessment of maize silage varieties through cob has changed, because it was no more satisfactory. Former classification assessed energy density only through cob portion. The digestibility of residual plant part (leaf/stem) was not taken into account. Current classification system follows maturity grade of varieties after whole-plant dry matter content (Rath 2002).

In accordance with the FAO nomenclature all maize varieties fall within numbers 100-900 (Zscheischler et al. 1990). Maize varieties are divided into maturity groups according to the length of time required from sowing to maturity. These groups are labelled as early, mid-early, mid-late and late. Within each group varieties are once more sub-divided with the help of number 10. Under Germany conditions the difference of 10 FAO numbering gives approximately 1-2 days difference in maturity, that is, 1-2 % in dry matter content in corn maize at the time of harvest. A variety with FAO number 280 matures under Germany conditions approximately 5-8 days later than one with FAO number 230. That means by harvesting both varieties on the same day the dry matter content of corn varieties with FAO number 280 would be nearly 5-8 % lower. This also explains the fact that the same type of variety grown in other countries (under various environmental conditions) is differently grouped.

Climatic (weather) conditions seem to dictate on the selection of maize genotypes (varieties) for a given area temperature is one of the most limiting factors in maize production across locations as it affects the growth rate and development of the plant. On the other hand in Brandenburg region (north east plain of Germany), where this experiment was conducted, water deficit (drought), leads to low dry matter yield and low forage quality of silage corn (Schmaler et al. 2003), most crucial is the distribution of water during vegetation period and water deficit during silking (Schmaler & Richter 2002). Water stress occuring during vegetative and tasselling stages reduced plant height as well as leaf area development. Vegetative and yield parameters were significantly affected by water shortage in the soil profile due to omitted irrigation during the sensitive tasselling and cob formation stages (Çakir 2004). On soils with low available water capacity maize reached highest yields (120 dt ha-1 up to 129 dt ha-1) if the first N-application was applied at a plant height of 15 cm. A lower plant density stimulated yields on soils with low available water capacity also (Sticksel et al. 1996). Selection for drought tolerant varieties or varieties with faster rate of leaf development (rapid canopy closure) which would enable maximum earlier interception of light energy, photosynthesis, biomass production and dry matter accumulation would be appropriate (Westgate et al. 1997). Otherwise, if maintaining green leaf area in maize under drought (water deficit) condition should improve yield, then selection for stay-green varieties would be another alternative (Borrell et al. 2000 a, b). However to reduce the risk on yield and quality of forage maize that might be caused by adverse weather conditions and to utilise any technological advantages of the varieties a combination of maturity groups and maturity types are grown. Under Brandenburg condition it would be suitable to grow 2/3 mid-early stay-green varieties to 1/3 early synchronic maturity varieties (Barthelmes & Krüger 2002). In order to improve on whole plant (total) digestibility and resistance to fusarium ssp. of silage maize selection for varieties with varying maturity positions of generative and vegetative parts of silage maize (asynchronic maturity of corn and residual plant) is being intensified in recent years (Steinhöfel 2000). Because non grain portion of the plant may represent over 50 % of the total dry matter in corn silage, variety (hybrid) differences in chemical composition and ruminal fermentability of the stover portion of the plant may account for important nutritional differences in corn hybrids (Hunt et al. 1989).

▼ 5 

In considering maize varieties for silage purposes characteristics like yield, yield stability and above all, forage quality, which includes starch content, energy content and digestibility of the residual plant is very important. Deinum & Bakker (1981) found digestibility differences among corn hybrids. Hybrid differences in dry matter yield have been documented (Fairey 1980, Deinum 1988). Deinum (1988) concluded that yield and quality should be taken in consideration when selecting hybrids for forage. The introduction of Near-Infrared-Reflectance-Spectroscopy (NIRS) has enabled further analysis of contents of other forage quality components like crude fibre, crude protein, ADF, NDF or enzyme soluble carbohydrates (Shenk & Westerhaus 1994). Other parameters important in selection of varieties include resistance to parasitic diseases like Helminthosporium turicum, Ustilago maydis and pests like Oscinella frit, Ostrinia nubilalis that can cause negative effects on yield and quality (Hurle et al. 1996). Varieties susceptible to strong wind and heavy rainfall normally suffer stem bends and stem break off, a phenomenon referred to as ‘green-snapping’. At a period of plant growth nearing flowering this phenomenon could result in damage to plants, hence influencing overall yield and quality components (Eder & Widenbauer 2003). The selection of maize varieties and the timing of harvest are important management considerations for dairy and livestock operations. Adverse spring conditions often push planting dates for corn past the optimum for grain and sometimes silage production (Darby & Lauer 2002). Achieving high dry matter yield from whole-plant corn (WPC) and high milk production from cows fed WPC depends on the harvesting of the corn at the proper stage of maturity (Bal et al. 1997). Agronomic trials (Ganoe & Roth 1992) have shown that dry matter yields of whole-plant corn are maximized by harvesting at two-thirds milkline to black layer stages. At an immature stage of harvest, fiber oncentrations are highest, which lowers the energy density of whole-plant corn (Hunt et al. 1989). At a mature stage of harvest, digestibility of the stover is reduced (Wiersma et al. 1993), which may lower the energy density of whole-plant corn. Harvest of whole-plant corn at a mature stage may also increase whole kernel passage and lower starch digestibility (Harrison et al. 1996). Therefore stover and starch digestibility should be considered in most equations that predict energy value from whole-plant from ADF concentration (Mahanna 1995). Poor starch fill (and grain yield) can cause photosynthetic energy to remain as sugar in the stover and leaves, thus diluting fiber content but not yielding the expected net energy (Coors et al. 1997, Fairey 1983, Deinum & Knoppers 1979).

2.2. Leaf area

Leaf area and light distribution are important input parameters in canopy photosynthesis modeling. The ability to predict leaf area and leaf area index is crucial in crop simulation models that predict crop growth and yield (Hammer et al. 1998). The amount and vertical distribution of leaf area are essential for estimating radiation interception for canopy photosynthesis modeling (Boedhram et al. 2001, Sivakumar & Virmani 1984). Vertical distribution of leaf area has often been constructed from leaf areas per horizontal layers based on height (Acock et al. 1978), cumulative leaf area index (Norman 1978, Goudriaan 1986, Pattey et al. 1991) and leaf number (Connor et al. 1995). In studies of leaf area in maize, area of individual leaves is usually calculated from leaf length (LL) and leaf width (at the wid est point LW) as follows (Montgomery 1911):

Individual leaf area = 0.75*LL*LW[Eq. 1]

▼ 6 

Other workers on maize have used similar values of the coefficient in Eq. 1 for example 0.73 (McKee 1964, Dwyer & Stewart 1986) and 0.72 (Keating & Wafula 1992). Equation 1 has been reassessed due to the changes in genotypes since 1911 (Birch et al. 1999).

The expansion and duration of green leaf area determines the fraction of incident radiation intercepted by the crop. Leaf blades also provide the main path for transpiration and carbon harvesting. Kernel set in cereals such as maize and wheat is associated with intercepted radiation around anthesis (Andrade et al. 2000, Otegui & Andrade 2000). This relationship is being used to improve the prediction of kernel numbers (Lizaso et al. 2001). It is argued that maximum rates of photosynthesis are usually found in the top part of the canopy (Woodman 1971), therefore making it an advantage to have high leaf area proportions in the top portion of the canopy. However, the overall effect of the canopy architectures on growth and yield will also be modified by the overall canopy height, leaf shape and sizes (Taylor 1975). Dry matter accumulation is closely associated with leaf area development. The development of leaf area is a function of leaf numbers and leaf size these factors may change differently depending on the genetic material involved and the environment in which the plants are grown. Leaf number and leaf area development can help to elucidate plant dry matter production. Considerable variation in the amount and duration of green leaf area among genotypes has been reported (Dwyer et al. 1992, Elings 2000). In Elings 2000, area of the largest leaf relative to total leaf area was said to be constant. This constant was found to be linear related to total leaf number. The relationship helps directly to estimate total leaf area, when total leaf number and the area of the largest leaf are known. In a modified form this method can be applied over a wide range of enviromental conditions. Some authors studying a limited number of genotypes suggested that variations in leaf area development could be forecasted adequately using generalised equations whose parameters are defined as a function of total leaf number (Keating & Wafula 1992, Hammer et al. 1998). Dwyer et al. (1992) showed that cultivars with the same number of leaves could have very different patterns of leaf area development. These are due to genetic differences. El-Sharkawy et al. (1965) suggested that the 100-fold difference in dry matter production per plant between sunflower (Helianthus annuus L.) and cotton was associated with the rate of leaf area development. Ibrahim & Buxton (1981) obtained similar results with okra leaf vs. normal leaf cottons. In several studies differences in total leaf area were associated with changes in leaf size rather than differences in total leaf number. In McMichael et al. 1984, leaf area was directly correlated with dry matter production, development of and increase in leaf area was strain-specific and depended on either increased leaf numbers or increased leaf size. Leaf expansion rate varies with leaf temperature, photon flux density (PPFD), evaporative demand and soil water status. Genotypic differences were observed by Maddonni & Otegui 1996 in the leaf area of individual leaves maximum green leaf area index, green leaf area index above the ear, leaf angle and the progress of green leaf area index with time. These differences were reflected in fIPAR/ green leaf area index relationship.

Leaf area and light distribution are important input parameters in photosynthesis. The amount (leaf number) and distribution of leaf area are major factors determining light interception by plant canopy, which in turn, is essential in determining crop growth and yield (Norman 1978, Goudriaan 1986). Leaf dimensions show some variation across environments and cultivars. Substantial differences in leaf production exist among cultivars from different regions (Birch & van der Putten 2003). Predicting plant leaf area production can be studied using a framework based on radiation intercepted radiation use efficiency (RUE) and leaf area ratio (Lafarge & Hammer 2002).

2.3. Leaf area index

▼ 7 

The ratio of leaf surface to soil surface was termed leaf area index (LAI) by Watson (1947). The LAI is defined as the projected leaf surface area per unit ground surface. However, recently LAI has been defined as one-half the total green leaf area per unit ground area (Chen & Cihlar 1996, Chen et al. 1997). Estimates based on these two definitions can differ by a factor between 1.28 and 2.00 depending on the form of the object that is being described. The change in definition is related to the fact that optical instruments respond to half the total area of foliage elements rather than to the projected area. LAI = 0 means that no leaves or needles exist, LAI = 1 indicates that the leaf area equals the horizontal ground surface, LAI = 2 means that the leaf area is double the size as the ground surface area etc. In maize under conditions of optimal growth peak LAI ranged from 4.8 to 7.8 (Lindquist et al. 2005).

Leaf area index quantifies the amount of foliage per unit ground surface area. It is one of the “driving” biophysical variables and is therefore an important input parameter to many models, e.g. hydrological, ecological and climate models. LAI varies with plant/tree species as well as with mean annual temperature, length of the vegetation period, water supply (Wulder 1998) and stock age (Spanner et al. 1994). LAI also influences the photosynthesis as well as the amount of perspired water and both of absorbed CO2 and emitted O2 through the leaf surface area. It is therefore an important steering parameter of the plant water balance and of the energy and mass exchange between vegetation and atmosphere (Spanner et al. 1990, Wulder 1998). Growth and duration of green leaf area index of a crop determines the percentage of the incident solar radiation that will be intercepted by the crop canopy across time, thereby influencing canopy photosynthesis, photosynthate translocation and final yield (Dale et al. 1980).

Accurate measurements of LAI are laborious and time-consuming. Many methods of measuring LAI of corn (Zea mays L.) have been reported and vary greatly in their accuracy, precision, bias and ease of measurement. LAI can be quantified using direct or indirect field methods. A choice of any method used to measure leaf area depends largely on morphological features of leaves to be measured, accuracy required, amount of material to be measured and amount of time and equipment available (Daughtry & Hollinger 1984). Several methodologies have been used for measuring LAI in the field. These can be classified in four categories:

▼ 8 

Direct measurements by litterfall collection or destructive sampling

Allometric correlations with variables such as tree height or tree diameter

Gap-fraction assessment (e.g. with hemispherical photographs)

▼ 9 

Measurements of light transmission with optical sensors

Optical instruments measure light transmittance beneath/within a canopy, i. e. gap fraction over a range of zenith angles is measured and gives the effective LAI (Chen et al. 1997). The assumption for optical measurements is random distribution of foliage. This implies that LAI can be derived from the probability that a beam of direct radiation will pass unobstructed through a canopy. Light attenuation by successive leaf layers is related to LAI and is approximated by the Beer-Lambert Law (Eq. 2): where I is the irradiance at the ground level and Io is the irradiance above the canopy. The extinction coefficient k is related partly to the optical properties of the leaves and mainly to the structural properties of the canopy (height, stem density, leaf clustering and inclination etc.). It also depends on the radiation waveband that is considered. Simultaneous measures of I and Io yield a practical measurement of the LAI, provided that either an estimation of k or an adequate description of the foliage geometry is provided.

I = Io-kLAI[Eq. 2]

▼ 10 

Extinction properties and geometrical structure of the canopy are calculated from simultaneous measurements of light transmission under five different angles measured by five annular detectors, normalized to incident light values taken in the open.

2.4. Plant canopy analyser LAI 2000

This is a fast indirect method of measuring leaf area index (compared to the manual method) and other plant canopy structure attributes such as Mean Tip Angle (MTA). Measurements can be made under a variety of sky conditions and in canopies ranging in size from short grasses to forests. The LAI-2000 calculates LAI and other attributes from radiation measurements made with a “fish-eye” optical sensor - 148° field-of-view (Deblonde & Penner 1994, LI-COR 1992). Measurements made above and below the canopy are used to determine canopy light interception at five angles, from which LAI is computed using a model of radioactive transfer in vegetative canopies. Measurements made by positioning the optical sensor and pressing a button, data are automatically logged into the control unit for storage and LAI calculations. After collecting above-canopy and below-canopy measurements the control unit performs all calculations and the results are available for immediate on-site inspection. The LAI-2000 calculations include: Leaf area index (LAI), mean foliage inclination angle and the fraction of the sky visible from beneath the canopy. LAI calculations using this method assume that the below-canopy readings do not include radiation that was reflected or transmitted by foliage, the foliage elements are small compared to the area of view of each ring. Since the optical sensor has a broad field-of-view the size of the canopy or plot is an important consideration. If the plot is too small the sensor’s field-of-view will extend beyond the edge of the foliage being measured and LAI will be underestimated (or overestimated, if the plot is surrounded by denser foliage), the distribution of foliage elements are random the foliage is azimuthly randomly orientated, that is, it does not matter how the foliage is inclined, but the leaves should be facing all compass directions (Daughtry & Hollinger 1984).

2.5. Specific leaf area

Specific leaf area is the ratio of fresh foliage surface area to unit dry foliage mass or projected leaf area per dry mass. Gower et al. (1999) suggest its definition as half the total needle surface area referred to as hemisurface area. It has become an important variable in comparative plant ecology because it is associated with many critical aspects of plant growth and survival (Shipley & Vu 2002). SLA is often positively correlated with seedling potential relative growth rate (Muller & Garner 1990, Poorter & Remkes 1990) and leaf net photosynthetic rate (Field & Mooney 1986, Reich et al. 1997, Shipley & Lechowicz 2000), it is negatively correlated with leaf life span (Reich et al. 1992) and palatability to herbivores (Lucas & Pereira 1990). SLA provides the coefficient to convert foliage mass to leaf area, that is, by multiplying the amount of carbohydrate available to leaves by specific leaf area (SLA). In other research work (Tardieu et al. 1999, Wilson et al. 1999) SLA seems to suffer from a number of drawbacks. It is said to be very variable between the replicates and much influenced by leaf thickness. On the other hand, leaf expansion rate is considerably reduced by mild water deficits, which do not affect photosynthesis and is not affected by a reduction in the PPFD intercepted during rapid leaf expansion. SLA undergoes several - fold variability depending on the PPFD, soil water status and time of the day. It is increased when environmental conditions have a greater depressive effect on expansion rate than on photosynthesis and is decreased in the opposite case. It is reduced under drought conditions (marcelis et al. 1998). It is therefore appropriate to model leaf expansion independently of the plant carbon budget (Tardieu et al. 1999). SLA is species dependent. It ranges in values from a lower limit of 12 to the upper limit of 40. Decrease in SLA in droughted plants may be due to the different sensitivity of photosynthesis and leaf area expansion to soil drying. Drought stress affects leaf expansion earlier than photosynthesis (tardieu et al. 1999). Reduction of SLA is assumed a way to improve water use efficiency (Craufurd et al. 1999). This is because thicker leaves usually have a higher density of chlorophyll and protein per unit leaf area and hence, have a greater photosynthetic capacity than thinner leaves. However there are interspecific variations in photosynthetic nitrogen-use efficiency (PNUE, the ratio of CO2 assimilation rate to leaf organic content) in relation to SLA (Poorter & Evans 1998). For plants grown under low irradiance, ambient PNUE of high SLA species was higher primarily due to their lower N content per unit leaf area. Low SLA species clearly had an overinvestment in photosynthetic N under these conditions.

2.6. Leaf angle

▼ 11 

Interception of solar irradiation by leaf canopies is influenced by the canopy architecture of crops, which is a function of shape, distribution and orientation of the leaves that constitute the canopy (Girardin & Tollenaar 1994). The amount and distribution of leaf area and leaf angles in a crop canopy determine how photosynthetically active radiation (PAR) is intercepted and consequently influences canopy photosynthesis and yield. Factors such as plant shape, plant populations and row width will affect these leaf distributions and can occur in an almost infinite number of different combinations. Depending on row widths plants with upright leaves can have both the smallest and the largest daily canopy photosynthesis (Stewart et al. 2003). Plants are able to modify their foliage architecture in response to the environment. In maize (Zea mays L.) for instance leaf orientation can switch from a random distribution in nearly isolated plants (i.e. 3 plants m-²) to a ditch distribution where the leaves are placed perpendicular to rows, when the plants are grown at commercial crop densities. Orientation of leaves in a maize canopy is altered by intraspecific interference, thereby more effectively intercepting incident solar irradiance (Stewart & Dwyer 1993, Maddonni et al. 2001 a). Both field measurements and computer simulations indicate that maize canopies with leaves perpendicular to the rows may present increased light interception (about 10 % higher) and grain yield (about 10 % higher) than similar canopies with randomly orientated leaves. Across-row leaf orientation at high plant population should provide more rapid canopy closure, enhance crop competition with weeds and reduce dependence on herbicides while enhancing grain yield (Toler et al. 1999, Maddonni et al. 2001 b). This shade avoidance syndrome (Smith 2000) involves a series of changes in plant architecture in response to the low red to far-red ratio of vegetation canopies, which improve the exposure of the foliage to photosynthetic light. Phytochrome–mediated changes include enhanced axis growth reduced branching, organ reorientation and accelerated flowering. Upright leaf angle has been proposed to increase canopy photosynthesis in situations where LAI already tends to be high, such as with high planting densities and narrow row spacings (Loomis & Williams 1969, Duncan 1971). Evidence indicating how leaf inclination angle influences canopy photosynthesis was reported with rice by Tanaka (1972). He demonstrated by mechanically manipulating the leaf arrangement, that a horizontal-leafed canopy showed a plateau type response of photosynthetic rate to radiation, with low photosynthesis, while an erect-leafed rice canopy showed a higher photosynthetic rate. The rice yield of the horizontal-leafed rice canopy was about 70 % that of the vertical-leafed rice canopy. The relative importance of these responses depends on the species. In maize plant stature and tillering responded to low red to far-red ratio but the largest effects were those associated with a redirection of the leaves toward gaps with high red to far-red ratio.

Robertson 1994 in field studies indicated that vertical distribution of maize leaf area could be predicted in crop growth models from leaf appearance, final leaf number and additional information of leaf sizes and leaf angles. Across all genotypes, a consistent relationship was found between plant height increase and leaf appearance, with height increasing at a slow rate until the appearance of leaf 7, afterwards height increased at 5 times the initial rate until the appearance of the flag leaf. Madakadze et al. (1998) working with switchgrass populations showed that vertical distribution of LAI among populations differed throughout the growing season and that early in the seasons, the increases in light interception closely followed increases in LAI.

2.7. Leaf senescence

A normal process in the life cycle of plants is senescence. It is a terminal phase in the development of every organ, including leaves, stems, flowers and fruits. Senescence generally occurs without simultaneous growth, following organ maturity. It is influenced by environmental or endogenous (e.g. hormonal) perturbations by initiating or accelerating the different steps of the process. During this process in leaf a large part of leaf nitrogen, carbon and minerals is recycled to other organs of the plant (Nooden 1988 a). In summer crops, such as sunflower, maize and sorghum, senescence starts before all the leaf area is fully developed (i.e. before flowering) and progresses at an increased rate during the grain-filling period. Consequently green leaf area duration has always been shown to depend on the availability of assimilates to sustain grain growth during the post-flowering period. There are two important factors regulating leaf senescence at the whole-plant level: source-sink-relationships and nitrogen (N) status in the plant (Christensen et al. 1981, Tollennaar & Daynard 1982, Crafts-Brandner et al. 1984, Feller & Fischer 1994). Changes in the source-sink-ratio during grain filling is frequently accompanied by a dramatic change in stover weight as the supply of assimilate by the sources and the demand of assimilate by the sinks is buffered by assimilates temporarily stored in the stover. Dry matter of stover has been found to either increase when assimilate supply exceeds demand for grain growth or decrease when the demand is greater than the supply from current photosynthesis (Tollennaar & Daynard 1982, Barnett & Pearce 1983). Nitrogen (N) status also affects leaf senescence. Grain N is supplied from vegetative tissue as well as from concurrent N uptake (Pan et al. 1986). Nitrogen uptake is dependent upon availability of soluble carbohydrates to the roots (Tolley-Henry et al. 1988) and consequently the critical period for N supply is during reproductive growth when partitioning of carbohydrates is shifted from support of root activity to support of ear growth. Reduction of N uptake will enhance N mobilisation from leaves and stems. N mobilisation from leaves brings a decline in photosynthetic activity and eventually leaf senescence (Wada et al. 1993). Acceleration of leaf senescence is also thought to be adaptive in plants subjected to water shortage because it reduces the water demand cumulated over the whole plant cycle, thereby avoiding water deficit during seed filling. It also allows recycling of scarce resources to the reproductive sinks. However, early leaf senescence in crop species correlates with lower yield because cumulative photosynthesis is reduced (Wolfe et al. 1988 a, b).

▼ 12 

Selection based on delayed leaf senescence (stay-green plants) under drought conditions allowed obtaining sorghum hybrids with improved yields under water deficit (Borrell et al. 2000 a, b). Rajcan & Tollenaar (1999 a, b) attributed greater dry matter accumulation in some maize varieties tested to greater leaf longevity and that the number of green leaves, an indicator of leaf longevity, was greatest when supply and demand of assimilates during grain filling were approximately equal. The report also suggests that new hybrid had increased leaf longevity relative to an old hybrid, because of a larger source-sink-ratio during grain filling. According to Valentinuz & Tollenaar (2004), grain yield improvement of maize (Zea mays L.) hybrids has been associated with delayed leaf senescence. A top-bottom profile of leaf senescence was observed during the second half of the grain filling period with leaves in the central section of the canopy being the last leaves to senesce and this phenomenon was more marked in the newer hybrids. Nevertheless, stay-green plants do not necessarily produce higher yields, especially when chlorophyll catabolism and nutrient remobilization are disabled (Thomas & Horwarth 2000). Senescence is sped up by water or nitrogen deficits (Wolfe et al. 1988 a, b) and delayed when reproductive sinks are removed (Nooden 1988 b, Wolfe et al. 1988 a). Prediction and manipulation of leaf senescence is therefore crucial to optimise crop management and plant response to water deficit.

2.8. Stay-green

Stay-green or delayed foliar senescence in maize is a secondary trait that divides maize varieties into two major maturity types. Delaying leaf senescence is an effective strategy for increasing cereal production, particularly under water-limited conditions (Mahalakshmi & Bidinger 2002). A number of annual cereals exhibit genetic variation for the degree or rate of leaf senescence during grain filling (Thomas & Smart 1993). Specifically, stay-green has been associated with reduced lodging, lower susceptibility to charcoal rot ( Mughogho & Pande 1984) and improved grain filling and grain yield under stress (Rosenow & Clark 1981). Because of the benefits, selection for enhanced stay-green has been an important component of breeding for improved drought tolerance and improved grain yield in breeding programms in the USA (Rosenow et al. 1983) and Australia (Henzell et al. 1992) for many years. Although the ability of leaves to delay senescence has a genetic base in sorghum (van Oosterom et al. 1996), the expression of the character is strongly influenced by environmental factors. Sufficient expression of the trait for selection is thus dependent upon the occurance of a prolonged period of drought stress during the grain filling period, of sufficient severity to accelerate normal leaf senescence, but not of sufficient magnitude to cause premature death of the plants. In maize during the process of maturity there are varieties whose residual organs (leaves/stem) mature faster than the corn. While in another type, residual parts of the plant (leaves and stem) stay green longer than the cob rapidly matures (stay-green type). For silage purposes, the former types are suitable, while the latter are suitable for corn. Stay-green varieties have the advantage of maintaining healthy leaves and stem often times resistant to Helminthosporium and stemfusarium (Eder & Widenbauer 2003). The longer the leaves/stem stay green, than the corn matures, the more dry matter content begins to deteriorate. Varieties with stay-green trait tend to maintain more photosynthetically active leaves than varieties not possessing this trait, especially at postanthesis drought (Rosenow et al. 1983). Expression of stay-green has been reported in Sorghum bicolor (L.), Zea mays L. (Rajcan & Tollennaar 1999 a, b) as well as in other cereals like rice and oats.

Green leaf area at physiological maturity has proved to be an excellent indicator of stay-green and has successfully been used to select drought-resistant sorghums in the USA (Rosenow et al. 1983) and in Australia (Henzell et al. 1992). Key components, which determine green leaf area at maturity include: maximum green leaf area (total plant leaf area), duration of leaf senescence and rate of leaf senescence. Maximum green leaf area is the basis from which green leaf area at maturity is determined. It is from this point that the leaf area begins to decline according to the onset and rate of senescence up to maturity

▼ 13 

Green leaf area at maturity can then be mathematically described as follows:

GLAM = MGLA- (Durationsen * Ratesen)[Eq. 3]

GLAM is green leaf area at maturity (cm² plant-1), MGLA is the total plant leaf area (cm² plant-1), Durationsen is the duration of leaf senescence (°C d) and Ratesen is the rate of leaf senescence (cm² plant-1 °C d).

▼ 14 

Therefore, once the maximum (total) plant leaf area is set, retention of green leaf area during grain filling will be determined by the time at which leaves begin to die (onset of senescence) and the rate at which death occurs (rate of senescence) [Borrell et al. 2000 a].

Two factors that affect the components of green leaf area at maturity are water and genotype (variety). Timing and severity of drought are critical in determining both leaf area development and subsequent senescence. To improve yield under drought knowledge of the extent of genotypic variation in the components of green leaf area at maturity is required, especially higher total plant leaf area, delayed onset of leaf senescence and reduced rate of leaf senescence are all pathways to increased green leaf area at maturity. Environmental conditions resulting in high leaf area production at anthesis followed by severe postanthesis water deficit are most conducive to the expression of stay-green (Borrell et al. 2000 b). Genotypic differences in delayed onset and reduced rate of leaf senescence were explained by differences in specific leaf nitrogen and nitrogen uptake during grain filling. Leaf nitrogen concentration at anthesis was positively correlated with onset and negatively correlated with the rate of leaf senescence under terminal water deficit (Borrell & Hammer 2000).

2.9. Leaf (area) duration

Duration of leaf senescence is defined as the number of degree-days from the onset of senescence to physiological maturity. The importance of longer growth duration was amplified in the quest to increase the productivity of rice (Oryza sativa L.) up to 15 t ha-1 in irrigated ecosystems in Asia (Kropff et al. 1994). High nitrogen use efficiency trait of OBA SUPER 2 (Kling et al. 1996, Oikeh et al. 1996) possibly resulting from longer green leaf area duration and it’s direct impact on extending the period of dry matter accumulation after anthesis, translated to grain production.

2.10. Light interception

▼ 15 

The accumulation of biomass by crops results from the amount of incident photosynthetically active radiation (PAR) intercepted by the canopy and from the efficiency with which the intercepted PAR is converted into dry matter. The terms intercepted radiation and absorbed radiation are often used interchangeably in literature, but distinction has been made between the two terms by Asrar et al. (1989) and Russell et al. (1989). Intercepted radiation does not explicitly consider radiation absorption. Although photons must be intercepted before they can be absorbed, some are scattered (reflected or transmitted). However, Gallo & Daughtry (1986) observed that the differences between IPAR and APAR were less than 3.5 % from planting until just before physiological maturity of corn. Thus IPAR is a reasonable approximation of APAR as long as full green canopies are present. The difference between APAR and IPAR for incomplete canopies or canopies which include senesced plant material may be large.

Crop photosynthesis and hence bio mass production are directly associated with light interception by the canopy (Muchow et al. 1990). Light interception has been related to the leaf area index (LAI) of the crop by exponential functions (Jones & Kiniry 1986) of the general form:

fIPAR = a (1-e-kLAI)[Eq. 4]

▼ 16 

Where fIPAR is the fraction of photosynthetically active radiation intercepted by the canopy k is the attenuation coefficient and is a plateau value. Evidence exists of differences in light interception along the cycle in maize (Loomis et al. 1968) and in sorghum (Rosenthal et al. 1985). These differences may be partly explained by: i) senesced leaves, which continue to intercept light but are not included in the measurement of LAI (Gallo et al. 1993) and ii) light interception by the panicles (Duncan et al. 1967, Tetio-Kagho & Gardner 1988 a, b). In maize similar exponential functions for the relationship between fIPAR and green LAI (GLAI) have been found (Jones & Kiniry 1986, Muchow et al. 1990), but with differing values of the estimated attenuation coefficient (k) and of the maximum value of fIPAR (the plateau value a). Differences in both coefficients are probably due to effects of cultivar differences in plant height (Edmeades & Lafitte 1993), leaf angle (Loomis et al. 1968, Pepper et al. 1977), leaf number and LAI (Dwyer et al. 1992) on radiation interception with time. Sowing date (Andrade et al. 1993, Cirilo & Andrade 1994), plant population (Loomis et al. 1968) and water regime (Matthews et al. 1988, Muchow 1989) may also modify canopy structure resulting in a particular pattern of fIPAR evolution. As light travels downwards through a canopy it suffers a reduction in its photosynthetic photon flux density and a significant alteration in its spectral composition. Because absorption by green tissues is more intense in the blue (400-500 nm) and red (600-700 nm) wavebands and reflection is more intense in the far-red waveband (700-800 nm). The red to far-red ratio reaching the plant base is greatly reduced at high leaf area indexes. Thus, the vertical profile of light quantity and quality within a canopy are known to regulate leaf senescence rate. Maddonni & Otegui 1996 have observed genotypic differences in the area of individual leaves.

During the presilking period, dry matter distribution among leaves, stems and roots is simulated as a function of temperature and stage of development (Tollenaar 1989 a, b). If the daily assimilate demand by the grain exceeds the assimilate supply by net crop photosynthesis, remobilization of carbohydrates occurs from stems and leaves. The reduction in leaf weight can result in a reduced potential leaf photosynthetic rate and eventually leaf senescence. The self-destruction of a maize canopy due to low source-sink-ratio has been documented by Tollenaar & Daynard 1982). During the post silking period grain growth has priority for assimilate over vegetative tissue. Grain growth is the product of kernel number and rate of dry matter accumulation per kernel. Kernel growth is dependent only on temperature, if the assimilate is not limiting after the onset of the linear period of grain filling (Tollenaar & Bruulsema 1988). Differences in yield potential among species appear in part to be associated with differences in effective filling period. Genetic variability for filling period exists among genotypes of maize (Daynard et al. 1971, Daynard & Kannenberg 1976).

2.11. Radiation use efficiency

Variability within a crop species in the amount of dry mass produced per unit intercepted solar radiation or radiation use efficiency (RUE) is important for the quantification of plant productivity. RUE is easily measured in field experiments and is used to quantify plant growth. It is used to integrate leaf area, solar radiation interception and productivity per unit leaf area into crop productivity. Differences in dry matter accumulation among crop cultivars can be attributed to differences in either the absorption of incident photosynthetically active radiation (PAR) and/or the conversion of absorbed PAR into dry matter (Tollenaar & Aguilera 1992). Linearity has been found between CO2 assimilation of canopies integrated over one day (daily assimilation) and daily absorbed or intercepted PAR, implying constant photosynthetic RUE on a daily basis (Sinclair & Muchow 1999). Increased dry matter accumulation of new maize hybrids after silking can be attributed, in a large part, to increased radiation use efficiency. Drought stress reduces the efficiency with which absorbed PAR is used by the crop to produce new dry matter (the radiation use efficiency RUE) [Earl & Davis 2003]. This can be detected as a decrease in the amount of crop dry matter accumulated per unit of PAR absorbed over a given period of time (Stone et al. 2001) or as a reduction in the instantaneous whole-canopy net CO2 exchange rate per unit absorbed PAR (Jones et al. 1986). Slow development of maize (Zea mays L.) canopies may limit light interception and potential productivity (Westgate et al. 1997). Early canopy closure through narrower row spacings and greater plant population densities than normally used for hybrids adapted to particular location may increase RUE and grain yield. Muchow & Davis (1988) related RUE to specific leaf N (SLN) (0.5-1.6 g N m-² of leaf) for sorghum and maize. There are numerous reports of lower RUE after silking (Muchow & Sinclair 1994, Major et al. 1991).

2.12. Temperature sum (GDD, HU)

▼ 17 

Temperature among environmental factors is considered the primary determinant of plant development rate. A system to quantify the rate as a function of temperature was introduced more than two centuaries ago (Wang 1960) and is in use today for various crops, although not necessarily in the same form as originally proposed. The relation of accumulated thermal units to crop development has been variously tested (Cross & Zuber 1972, Bunting 1976), compared between crops (Neild 1982). In general the effect of temperature on plant functioning is brought about by the action on enzymatic activities. A large number of enzymes play a role in plant development and presumably enzymes providing photosynthesis are very important. There is a great deal of difference between C-3 and C-4 species as far as enzymes involved in photosynthesis are concerned. The pyruvate-phosphate dikinase, which provides the phosphoenolpyruvate (PEP) and hence CO2 acceptor in C-4 species, is sensitive to low temperature (Edwards & Ku 1987). Whereas the Rubisco found in the C-3 species is very efficient even at low temperatures. This difference is clearly expressed in the leaf development temperature response. The ‘degree-day’ unit stems mainly from the relationship between development rate and temperature. The same grains are harvested in very different climates it would be interesting to compare the sums of heat degrees over the months during which wheat does most of its growing and reaches complete maturity in hot countries like Spain and Africa or in temperate countries like France and in the colder countries of the North.

Growing degree-days (GDD) or Heat Units (HU) is frequently used to describe the timing of biological processes (McMaster & Wilhelm 1997). The basic equation used is eq. 6. Two methods of interpreting this equation for calculating GDD are: method 1 if the daily mean temperature is less than the base, it is set equal to the base temperature or method 2 if Tmax or Tmin < Tb, they are reset equal to Tb. Differences between the methods occur if Tmin is less than Tb and then method 1 accumulates fewer GDD than method 2) (McMaster & Wilhelm 1997). When incorporating an upper threshold as commonly done with corn, there was a greater difference between the two methods.

In practice, the concept of growing degree-days (GDD) assumes that plant growth is related directly to the average daily temperature. The degree-days for each day are added together or accumulated throughout the growing season. If mean daily temperature is equal or less than the base temperature, the degree-days value is zero (Edey 1977).

▼ 18 

In Europe through the widespread temperature sum of AGPM (L’Association Générale des Producteurs de Maïs) developed in France between 1978 and 1983 similar in principle to GDD (Growing-Degree-Days in the US) and CHU (Corn Heat Unit in Canada), growth and maturity stages in maize crop can be estimated (Eder & Krützfeldt 2000, Herrmann 2000, Pickert et al. 2001, Rath et al. 2002). Low temperature and drought may limit potential leaf expansion, which in effect affects photosynthesis and hence crop yield. Temperature can influence crop yields through effects on radiation interception, radiation use, yield component elaboration and/or carbohydrate partitioning. Lafitte & Edmeades (1997) indicated that adaptation groups differed greatly in grain and total biomass production across enviroments, large differences were observed in harvest index, supporting the hypothesis that temperature has important effects on dry matter partitioning to grain, all yield components were affected. Muchow 1990 showed that the rate of grain-growth increased and the duration of grain-filling was shorter as temperature increased and that whilst the rate of both milk-line and black-layer development increased with temperature the development of milk-line was less variable and proved to be the better indicator of the end of effective grain-filling. Maize cultivars with broad thermal adaptation may be useful in areas where the crop experiences large fluctuations in temperatures or when a cultivar is targeted for several areas with contrasting temperature regimes. However, it may not be possible to select a cultivar with high and stable grain yield across temperatures ranging from 13 °C to 28 °C, because cool and warm temperature adaptation may be mutually exclusive traits. Broad adaptation is possible across a more moderate range of temperatures, however and can be improved by selection (Lafitte et al. 1997). The duration of development period in maize (silage) is influenced above all by temperature conditions (Schuppenies 1989). The sum of an effective temperature is a measuring number, with which the period is defined and the course of maturity stated. The estimation of the course of maturity through temperature sum requires determination of dates of development stages.

2.13. BBCH Decimal Codes for the growth stages of maize

Virtually all growth processes in plants such as leaf photosynthesis and dry matter distribution are influenced by stages of development and duration of the same, which affects crop dry matter accumulation and grain yield. In crop production crops with identical phenological growth stages are grouped under a general form of decimal scale known as BBCH-Code (Weber & Bleiholder 1990). For instance the BBCH-Codes for the growth stages of maize, rape, field beans, sunflower and peas allows the use of identical code numbers for similar phenological growth stages of the different plant species, although it cannot describe special features of each crop or weed in detail. Owing to its universal usability BBCH-code has greatly contributed in the standardization and rationalization in Agricultural research work (Bleiholder et al. 1990). The extended BBCH scale is a system for coding of phenologically similar growth stages of all mono- and dicotyledonous plant species based on the well known cereal code of Zadoks et al. 1974 and Hack et al. 1992. The BBCH key is a decimal system with 10 principal growth stages and up to 10 secondary ones starting with seed germination and sprouting of perennials progressing through leaf production and extension growth to flowering and senescence. Therefore, it can also be a suitable tool to define the growth stages of different weed species (Hess et al. 1997).

Phenology, photosynthesis and partitioning are the three most important components of the maize-crop-growth simulator (MAIS). The phenological phase duration (planting to silking, silking to maturity) increases or decreases proportionally to the change in duration of the entire life cycle expressed in thermal leaf units (Boote & Tollenaar 1994). The course of growth and development of maize plant during the vegetation period is the foundation for yield formation (Geisler 1983). Accurate simulation of phenology is important because dry matter accumulation and grain yield are directly related to the duration of the life cycle and virtually all growth processes (leaf photosynthesis, dry matter distribution) are a function of stage of development. Tollenaar et al. (1979) simulated mais phenology using the relationship between temperature and rate of leaf appearance, genetic and environmental influences on duration of the life cycle expressed as effects on total leaf number (Tollenaar & Hunter 1983). Although no new leaves emerge after silking the relationship between rate of leaf appearance and temperature (thermal leaf units) is assumed to quantify the rate of development during the entire life cycle of maize.

2.14. Forage quality and NIRS

▼ 19 

The quality control of agricultural products is an important field of interest in agricultural research and advisory work (Volkers et al. 2003). Evaluation of hybrid stability for yield and forage quality is therefore an important criterion in forage production. Plant cells can be divided into cell solubles and cell wall material. Cell solubles are contained within the boundaries of the cell wall and are easily digested. Cell solubles include crude protein (nucleic acids, amino acids, proteins and other nitrogen-containing compounds), sugars, starch and lipids (fats). In comparison the cell wall contains slowly digestible material called fibre, which includes hemi-cellulose, cellulose and the mostly indigestible substance lignin. These fibre fractions are included in the neutral detergent fibre (NDF) and acid detergent fibre (ADF) fractions often used in forage analysis reports. Decline in cell solubles are due to increased fibre (cellulose, hemi cellulose and lignin) movement of nutrients from leaves to roots and leaching of cell solubles by rain and snow during dormancy. Near infrared reflectance spectroscopy (NIRS) provides a method for the simultaneous measurement of multiple quality traits like in vitro digestible organic matter (IVDOM), crude protein (XP), crude starch, insoluble organic substances (IOS), crude fibre (XF), acid detergent fibre (ADF), sugar content and dry matter content. Dry matter content of cob is an essential characteristic used in estimation and assessment of nutrient content and energy concentration of silage maize (Knabe et al. 1987). A rise in dry matter content in cob is accompanied by decrease in crude fibre content. A rise in starch content is accompanied by increase in energy concentration in the whole plant. A major advantage of near-infrared reflectance spectroscopy (NIRS) is its ability to analyse samples without chemical treatments, hence costs sample material (Barber et al. 1990) and chemical wastes can be reduced. Furthermore, NIRS has less variance in analyses of the same sample than laboratory analyses (Marum & Aastveit 1990). The use of NIRS to predict the quality of forage maize at a mature stage is commonly accepted (Mainka 1990, Paul et al. 1992). Although the feeding value of maize silage is considered as rather constant (Cox et al. 1994), its digestibility and energy content may vary due to growth conditions and genotype (Deinum & Struik 1988). Particularly under less favourable climatic conditions in the northern parts of Europe, where a low temperature sum during the growing season, (or on the reverse, in areas of high temperature and heat) regularly restricts the growth of maize, its quality can vary considerably. Thus, an accurate prediction of the quality is essential to meet the animals’ requirements and to avoid nutrient losses to the environment (Volkers et al. 2003). Sample handling, processing and analysis methods are important in NIRS analysis. A high level of accuracy and precision in the laboratory will not improve upon poor sampling technique, nor will it give more accurate analysis for estimating composition (NIRS 2 Version 3.0 1992).

Forage value of silage maize depends very strongly on the degree of maturity of the maize plant at the time of silage. Various experiments have documented the best time to harvest corn for silage to optimize yield and quality (Bal et al. 1997, Weaver et al. 1978). Wiersma et al. (1993) reported that corn silage quality is inversely related to the stage of maturity at harvest. With the development of a prognosis model, it is now possible to estimate the optimum time for harvest, that is, the point of increased (maximum) forage value with reduced losses and fixing of appropriate dates of labour requirement. However, it is uncertain to estimate the maturity time due to changing weather conditions (Rath et al. 2002). The most important parameters (characteristics) of forage ration besides mineral substances are energy content, crude protein, starch and sugar contents, are forage structures which include crude fibre, ADF and NDF. Crude ashes are another determinant of the energy content of silage. This is because it does not contribute in supply of energy, but rather acts as a thinning (diluting) factor. It also contains dirts or pollutants in form of sand. Therefore, correct determination of crude ashes is a basic forage analysis requirement (Tillmann 2002). Forage value of maize is basically defined through the concurrent developmental processes of cob and residual plant during the generative development (Degenhardt 1996). In this phase transfer of nutrients from stem, leaves and cob leaf to the cob occurs (Hepting 1988). This results in a continuous increase in the portion of energy-rich cob in whole plant-dry mass. According to Hepting (1988) this process depends strongly on genotype and environmental conditions.


© Die inhaltliche Zusammenstellung und Aufmachung dieser Publikation sowie die elektronische Verarbeitung sind urheberrechtlich geschützt. Jede Verwertung, die nicht ausdrücklich vom Urheberrechtsgesetz zugelassen ist, bedarf der vorherigen Zustimmung. Das gilt insbesondere für die Vervielfältigung, die Bearbeitung und Einspeicherung und Verarbeitung in elektronische Systeme.
XDiML DTD Version 4.0Zertifizierter Dokumentenserver
der Humboldt-Universität zu Berlin
HTML generated:
09.03.2006