INTRODUCTION. Internal Organization of the Plant Body. Summary of Types of Cells and Tissues. General References. DEVELOPMENT OF THE SEED PLANT. The Embryo. From embryo to the Adult Plant. Apical Meristems and Their Derivatives. Differentiation, Specialization, and Morphogenesis. References. THE CELL. Cytoplasm. Nucleus. Plastids. Mitochondria. Microbodies. Vacuoles. Paramural Bodies. Ribosomes. Dictyosomes. Endoplasmic Reticulum. Lipid Globules. Microtubules. Ergastic Substances. References. CELL WALL. Macromolecular Components and Their Organization in the Wall. Cell Wall Layers. Intercellular Spaces. Pits, Primary Pit--Fields, and Plasmodesmata. Origin of Cell Wall During Cell Division. Growth of Cell Wall. References. PARENCHYMA AND COLLENCHYMA. Parenchyma. Collenchyma. References. SCLERENCHYMA. Sclereids. Fibers. Development of Sclereids and Fibers. References. EPIDERMIS. Composition. Developmental Aspects. Cell Wall. Stomata. Trichomes. References. XYLEM: GENERAL STRUCTURE AND CELL TYPES. Gross Structure of Secondary Xylem. Cell Types in the Secondary Xylem. Primary Xylem. Differentiation of Tracheary Elements. References. XYLEM: VARIATION IN WOOD STRUCTURE. Conifer Wood. Dicotyledon Wood. Some Factors in Development of Secondary Xylem. Identification of Wood. References. VASCULAR CAMBIUM. Organization of Cambium. Developmental Changes in the Initial Layer. Patterns and Causal Relations in Cambial Activity. References. PHLOEM. Cell Types. Primary Phloem. Secondary Phloem. References. PERIDERM. Structure of Periderm and Related Tissues. Development of Periderm. Outer Aspect of Bark in Relation to Structure. Lenticels. References. SECRETORY STRUCTURES. External Secretory Structures. Internal Secretory Structures. References. THE ROOT: PRIMARY STATE OF GROWTH. Types of Roots. Primary Structure. Development. References. THE ROOT: SECONDARY STATE OF GROWTH AND ADVENTITIOUS ROOTS. Common Types of Secondary Growth. Variations in Secondary Growths. Physiologic Aspects of Secondary Growth in Roots. Adventitious Roots. References. THE STEM: PRIMARY STATE OF GROWTH. External Morphology. Primary Structure. Development. References. THE STEM: SECONDARY GROWTH AND STRUCTURAL TYPES. Secondary Growth. Types of Stems. References. THE LEAF: BASIC STRUCTURE AND DEVELOPMENT. Morphology. Histology of Angiosperm Leaf. Development. Abscission. References. THE LEAF: VARIATIONS IN STRUCTURE. Leaf Structure and Environment. Dicotyledon Leaves. Monocotyledon Leaves. Gymnosperm Leaves. References. THE FLOWER: STRUCTURE AND DEVELOPMENT. Concept. Structure. Development. References. THE FLOWER: REPRODUCTIVE CYCLE. Microsporogenesis. Pollen. Male Gametophyte. Megasporogenesis. Female Gametophyte. Fertilization. References. THE FRUIT. Concept and Classification. The Fruit Wall. Fruit Types. Fruit Growths. Fruit Abscission. References. THE SEED. Concept and Morphology. Seed Development. Seed Coat. Nutrient Storage Tissues. References. EMBRYO AND SEEDLING. Mature Embryo. Development of Embryo. Classification of Embryos. Seedling. References. Glossary. Index.

Anatomy of Seed Plants

This article introduces the FAO crop model AquaCrop. It simulates attainable yields of major herbaceous crops as a function of water consumption under rainfed, supplemental, deficit, and full irrigation conditions. The growth engine of AquaCrop is water-driven, in that transpiration is calculated first and translated into biomass using a conservative, crop-specific parameter: the biomass water productivity, normalized for atmospheric evaporative demand and air CO 2 concentration. The normalization is to make AquaCrop applicable to diverse locations and seasons. Simulations are performed on thermal time, but can be on calendar time, in daily time-steps. The model uses canopy ground cover instead of leaf area index (LAI) as the basis to calculate transpiration and to separate out soil evaporation from transpiration. Crop yield is calculated as the product of biomass and harvest index (HI). At the start of yield formation period, HI increases linearly with time after a lag phase, until near physiological maturity. Other than for the yield, there is no biomass partitioning into the various organs. Crop responses to water deficits are simulated with four modifiers that are functions of fractional available soil water modulated by evaporative demand, based on the differential sensitivity to water stress of four key plant processes: canopy expansion, stomatal control of transpiration, canopy senescence, and HI. The HI can be modified negatively or positively, depending on stress level, timing, and canopy duration. AquaCrop uses a relatively small number of parameters (explicit and mostly intuitive) and attempts to balance simplicity, accuracy, and robustness. The model is aimed mainly at practitioner-type end-users such as those working for extension services, consulting engineers, governmental agencies, nongovernmental organizations, and various kinds of farmers associations. It is also designed to fit the need of economists and policy specialists who use simple models for planning and scenario analysis.

AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles

Drought tolerance improvement in crop plants: An integrated view from breeding to genomics

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1086&context=usdaarsfacpub

Growing degree-days: one equation, two interpretations

Nitrogen is the most limiting nutrient for crop production in many of the world's agricultural areas and its efficient use is important for the economic sustainability of cropping systems Furthermore, the dynamic nature of N and its propensity for loss from soil‐plant systems creates a unique and challenging environment for its efficient management Crop response to applied N and use efficiency are important criteria for evaluating crop N requirements for maximum economic yield Recovery of N in crop plants is usually less than 50% worldwide Low recovery of N in annual crop is associated with its loss by volatilization, leaching, surface runoff, denitrification, and plant canopy Low recovery of N is not only responsible for higher cost of crop production, but also for environmental pollution Hence, improving N use efficiency (NUE) is desirable to improve crop yields, reducing cost of production, and maintaining environmental quality To improve N efficiency in agriculture, integrated N management strategies that take into consideration improved fertilizer along with soil and crop management practices are necessary Including livestock production with cropping offers one of the best opportunities to improve NUE Synchrony of N supply with crop demand is essential in order to ensure adequate quantity of uptake and utilization and optimum yield This paper discusses N dynamics in soil‐plant systems, and outlines management options for enhancing N use by annual crops

Enhancing nitrogen use efficiency in crop plants

Water is generally the limiting factor in U.S. Great Plains wheat (Triticum aestivum L.) production. With increasing demands for limited water, improving the efficacy of irrigation is critical. One technique is to irrigate during responsive stages of crop development, but few studies have examined this approach. This 2-yr study on a Nunn clay loam soil (fine, montmorillonitic, mesic Aridic Argiustoll) was designed to examine the effects of irrigation, based on stage of crop development, on winter wheat yield, yield components (on a plant basis), and specific culm responses. In the first year, the treatments were control (dryland), and irrigation at late jointing. In the second year, the treatments were dryland, irrigation at late jointing, irrigation at anthesis, and irrigation at both late jointing and anthesis. Irrigation at late jointing or anthesis significantly increased grain yield and the most important yield component (spikes per plant), as well as spikelets per plant, number of kernels per plant, and kernel weight per plant. The increased spikes per plant in the irrigation treatments, particularly with late-jointing irrigation, was due to reduced tiller abortion. Increased yield was primarily due to the contribution of more secondary tillers (T10, T11, T20, T21, T30, and T31) that produced spikes. The contribution of main stems to the total yield decreased from 92% to at most 86% with irrigation, although the dry weight of main-stem spikes increased with irrigation. The contribution to total yield of the main yield-producing tillers, T1 and T2, decreased from 20 to 15% and 19 to 15%, respectively, with irrigation. As with main-stem spikes, irrigation also increased T1 and T2 spike dry weight. Therefore, the production of secondary spikes due to irrigation treatments was not at the expense of main stem or primary tiller spikes. If only one irrigation can be applied, irrigation at late jointing is recommended for central Great Plains conditions, due to its greater effect on tiller survival. This implies that developmental and physiological processes at late jointing are critical in determining final grain yield, and water stress should be avoided at this growth stage

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Irrigation and Culm Contribution to Yield and Yield Components of Winter Wheat

Stand establishment and subsequent autumn development and growth are important determinants of winter wheat (Triticum aestivum L.) yield. Soil management practices change soil properties and conditions, which alter seedling emergence, crop development and growth. Pre-plant soil management practices were studied for 6 years in a wheat–fallow rotation in eastern Colorado, USA, to isolate the impacts of pre-plant tillage (PT) and residue level on winter wheat seedling emergence and autumn development and growth. A split plot design was used with PT, using a moldboard plow that incorporated surface residue, and with no-tillage (NT). The tillage systems represented the main plots and three residue levels within each tillage treatment as subplots: no residue (0R), normal residue (1R) and twice-normal residue (2R). Residue amount had little effect on emergence or autumn growth and development. PT resulted in soil water loss from the plow zone. NT plots had more favorable soil water levels in the seeding zone which resulted in faster, more uniform and greater seedling emergence in 4 out of the 6 years. This is especially critical for stand establishment in years with low rainfall after planting. Soil or air temperature did not account for differences among treatments. Earlier and greater seedling emergence in NT treatments resulted in greater autumn development and growth. Shoot biomass, tiller density and leaf numbers were greater in NT, and again residue amount had little effect. At spring green-up, NT treatments had greater soil water in the profile. Grain yield was always equal or greater in NT than in PT, and positively correlated with earlier/greater seedling emergence and autumn growth. NT will enhance soil protection and likely increase snow catch, reduce evaporation and benefit yield in semiarid eastern Colorado.

Soil management alters seedling emergence and subsequent autumn growth and yield in dryland winter wheat–fallow systems in the Central Great Plains on a clay loam soil

Predicting the rate of leaf appearance, or phyllochron, aids in understanding and modeling grass development and growth. Nine equations predicting the phyllochron of wheat (Triticum aestivum L.) were evaluated using field data from a variety of locations, cultivars, and management practices. Each equation is referred to by the last name of the first author; if there is more than one equation by the first author, additional descriptors were included. The BAKER and KIRBY equations predict the phyllochron based on changes in daylength following seedling emergence; CAO-TEMP and CAO-DAY use a curvilinear relationship with temperature and daylength; VOLK mathematically refines CAO-TD MIGLIETTA-DAY adds photoperiod effects ot MIGLIETTA. No equation adequately predicted the phyllochron. The r 2 values between predicted and measured phyllochron for winter wheat and spring cultivars, respectively, were BAKER (0.001, 0.486, KIRBY (0.002, 0.487), CAO-DAY (0.000, 0.174), MIGLIETTA-DAY (0.013, 0.008), MIGLIETTA (0.002, 0.405), CAO-TEMP (0.100, 0.190), CAO-FIELD (0.078, 0.036), CAO-TD KIRBY tended to overestimate the phyllochron; and the remaining equations were biased towards underestimating the phyllochron. Equations developed from field data had the greater range of predicted phyllochrons. Based on multiple criteria, the BAKER equation best predicted the phyllochron for the experimental data set. Other factors must be added to the equations to improve predictions. Much opportunity exists to improve our ability to predict the phyllochron

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Accuracy of Equations Predicting the Phyllochron of Wheat

In predicting wheat (Triticum aestivum L.) phenology, logic suggests that basing thermal unit accumulation on near-surface soil temperature should give a better representation of shoot apex thermal unit accumulation than air temperature until internode elongation raises the apex above the soil surface. A study was undertaken to determine if predictions of winter wheat phenology are improved when based on measured near-surface soil temperature rather than air temperature. Air temperature 1.5 m above the soil surface and soil temperature at crown depth (the position of the shoot apex before stem elongation) were collected for 23 site-years across the U.S. Central Great Plains representing a range of cultivars, soils, management practices, and climates. Seven site-years from different sites were randomly selected to calculate the mean thermal units from both seeding and 1 January to specific growth stages based on both air and soil temperature. These means were used to predict occurrence of growth stages for the remaining 16 site-years. In no instance did soil temperature significantly improve prediction of winter wheat phenology. From these results, we conclude that the additional effort and expense of using soil temperature in predicting winter wheat phenology are not justified.

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Gregory S. McMaster

Papers

Irrigation and Culm Contribution to Yield and Yield Components of Winter Wheat

Soil management alters seedling emergence and subsequent autumn growth and yield in dryland winter wheat–fallow systems in the Central Great Plains on a clay loam soil

Accuracy of Equations Predicting the Phyllochron of Wheat

Is Soil Temperature Better than Air Temperature for Predicting Winter Wheat Phenology

Computer use in agriculture: an analysis of Great Plains producers.