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The effect of planting date on dry matter allocation coefficients on different soybean cultivars

Number of pages: 48 File Format: word File Code: 32365
Year: 2014 University Degree: Master's degree Category: Agricultural Engineering
Tags/Keywords: Beta function - Distribution coefficient - dry matter - Linear regression - Modeling of agricultural plants - Oily beans - planting date - Soybean cultivars
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  • Summary of The effect of planting date on dry matter allocation coefficients on different soybean cultivars

    Dissertation for Master Degree (M.A)

    Abstract

    In order to investigate the effect of planting date on dry matter allocation coefficients in soybean cultivars (DPX, Williams, Parshin), an experiment was conducted with 3 planting dates (June 30, July 9, and July 19) in the form of a randomized complete block design in four replications. In order to determine the dry matter allocation coefficients of plant organs in 5 stages of the phenological stages of the plant, two stages before flowering, one stage at the beginning of flowering, one stage at the beginning of seed filling and one stage at the beginning of seed filling, the dry weight was measured by organ (leaf, stem and reproductive organs) and by plotting the changes in the dry matter allocation of each organ against the remaining dry matter produced, the phenological turning points of the changes in the specific allocation coefficients and by drawing regression lines with different slopes, The allocation of dry matter to each organ was determined. The results showed that the stages of greening to flowering and flowering to ripening were two distinct stages in the process of dry matter changes, which were affected by planting date. Also, in this research, the beta function was used to predict the distribution of plant dry matter. The results showed that the change of planting date can affect the dry matter allocation coefficients of the two aforementioned stages, and on the other hand, the vertical distribution of the leaf surface in soybean cultivars was almost similar and related to the first and second layers. The coefficients obtained in this study are very valuable for quantifying the yield of soybean cultivars using simulation models.

    Key words: dry matter distribution coefficient, soybean cultivars, linear regression, beta function

    Introduction

    Soy or oil bean with the scientific name Glycin Max, is a bushy and leafy annual plant from the leguminous family, which is mostly cultivated in the world for its seeds (Ahmed Afkari, 2008).

    The length of the growth period and the speed of vegetative growth depend a lot on the variety, the length of the day and the date of planting, but many cultivars complete their life stages within 80 to 120 days. They deliver (Khajapour, 1377). Soybean has a deep main root that can penetrate up to 60 cm deep in the soil. Nitrogen fixation nodules containing rhizobium bacteria can be observed on the roots of the plant (Khajapour, Mohammad, 2017).

    What is modeling?

    Modeling is a set of activities that are performed to prepare a model of a system or build a model (Sultani, 2018).

    Why modeling?

    Our goal in building models for agricultural systems is to prepare a type of law (mathematical formulation) by which performance can be calculated and all questions can be answered at the same time (Sultani, 2018). Of course, there are other reasons for building models, which are summarized as follows:
    1-Reducing long-term field experiments in a specific area. 2- Interpretation of climatic data in terms of potential production and limitations. 3- Evaluation of plant and soil management. 4- Assessing the risk of management operations in performance. 5- Better understanding of their biological and physical and interactive systems

    Modeling of agricultural plants has been accepted as a useful method in the research and management of these crops (Inman-Bamber[1] et al., 2001). Determining the potential of crops (Cherow [2]-Naya-Muth et al., 2000; Sinclair [3] et al., 1999; b1999 and Inman-Bamber et al., 1998), crop management and planning (Robertson et al., 1999; Sinclair et al., a 1999; Brennan et al., 1999 and Sinclair et al., 1998) and Forecasting agricultural products (McGlinchey, 1999) are examples of the use of simulation models.

    The power of a model depends on the accuracy of that model's prediction of plant performance (Inman-Bamber, 2002). Determining the process of allocation of materials between leaves, stems and reproductive organs is a key thing to quantify biomass accumulation. The distribution of dry matter in crops determines the performance of the plant. The ability to predict seed yield potential is possible by calculating the rate of dry matter accumulation and its distribution in different organs (Robertson and Karberi, 2001). Different algorithms have been used to describe dry matter in crops.. Shank[4] (1945) suggested that the partitioning of photosynthetic materials should be calculated from the C/N ratio first.

    Broward (1962) proposed to design a model based on the N/C ratio that predicts the partitioning of materials, and Thornley (1972) expressed Brewer's model in the form of a series of mathematical equations.

    A model of plant growth is a mathematical description of our understanding of plant behavior, and due to the use of mathematical functions, this behavior must be completely clear and certain at each stage, and there is no room for possibility or possibility anymore. The need for an equation forces us to consider assumptions and the model is built to test these assumptions. If the model's predictions of the existing reality are not accurate, we must accept that our knowledge of the studied system is not complete (Banayan, 2000). From these models, they are used to conduct various studies, such as choosing the right plant and variety for planting and determining the best agricultural management (Eagly and Bruning, 1992), estimating the production potential of areas (Mink and Hamer [5], 1995), determining the policy for breeding cultivars (Hebkot [6], 1997), determining research priorities, technology transfer (Nix [7], 1984), zoning, ecological (Bowman and Lansigan[8], 1994; Agarwal, 1993) and predicting the effects of climate change (Melkonian et al.[9], 1997; Sinclair and Rawlins[10], 1993) have been used. Radiation intake and radiation use efficiency (RUE) are estimated (Monteith, 1977). The next step in plant simulation models is to assign the produced dry matter to different organs. This allocation is done using distribution coefficients [11] (PC). The use of dry matter distribution coefficients has long been used in crop plant simulation models. The same method has been used in most crop plant models related to Wageningen University (Gwadrian and Van Laar[12], 1994).

    Some researchers designed models of the distribution of photosynthetic materials based on the source and reservoir relationship.

    The distribution of dry matter as a result of the flow of assimilates from the source organs to the reservoir organs. is (Marselis, 1996).

    The next step in plant modeling is determining the part of dry matter allocated to different parts of the plant, which is called the partition coefficient. There are two important methods for quantifying dry matter distribution:

    In the first method, plant seed yield (Y) depends on what proportion of the stored dry matter the plant allocates to the seeds. Harvest index is a concept that shows the efficiency of dry matter distribution to seeds. In a significant number of crops, it has been shown that the harvest index increases linearly with a constant slope (HI/dt) during the effective grain filling period, from the time of linear increase of the grain filling period (-a/b) to the end of grain filling (m). This method has been widely used to describe dry matter (for example, Monteith, 1977; Sinclair, 1986; Soltani et al., 1999; Bindi[13] et al., 1999; Robertson[14] et al., 2001; Soltani and Galshi, 2002).

    In the second method, the distribution of dry matter between Different plant organs are described using dry matter distribution coefficients. In this method, plant performance depends on the coefficient of distribution of dry matter to seeds and the amount of re-transfer of dry matter from other organs. The transfer itself is usually a function of the fraction of dry matter that can be transferred to the grain and the total dry matter at the beginning of grain filling. This method has also been used in many agricultural simulation models, including Wilkerson et al. (1981) and Wong and Engel (2002). of soybean cultivars (DPX, Persing and Williams) an experiment with CRBD design and four replications was conducted. 3 sowing dates were included (20 Jun, 30 Jun and 10 Jul, 2011).

  • Contents & References of The effect of planting date on dry matter allocation coefficients on different soybean cultivars

    List:

    Abstract ..1

    Chapter One: Introduction

    Introduction ..3

    1-2- Why Modeling?.3

    1-3- The Effects of Planting Date. 6

    Chapter Two: Review of Resources

    Review of Resources ..10

    Chapter Three: Materials and Methods

    3-1- Geographical and climatic characteristics of the experiment place. 16

    3-2 Soil characteristics. Measured or recorded. 17

    3-7-1- Phenological stages. 17

    3-7-2- Plant dry weight. 18

    3-7-3- Leaf area index (LAI) measurement. 18

    3-7-4- Performance and performance components. 18

    3-8- Statistical analysis. 18

    Chapter Four: Results and Discussion

    4-1- Performance and performance components. 21

    4-2- Accumulation of dry matter. 23

    4-3- Dry matter distribution coefficients. 25

    4-4- Dry matter distribution coefficients in different organs. 25

    4-5 - Investigating how dry matter is allocated to different organs according to the vertical profile of the plant. 30. 4-5-1- Distribution of leaf dry matter distribution coefficients in the vertical profile of the plant. 30

    4-5-1-1- Phase I (germination to flowering). 30

    4-5-1-2- Phase II (flowering to ripening stage). 31

    Chapter Five: Conclusion

    Conclusion ..35

    Sixth chapter: suggestions

    Suggestions ..37

    Resources ..39

    Latin summary. 45

     

    Source:

     

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