CHAPTER ONE
2. RESPONSE OF SELECTED SOYBEAN GENOTYPES TO DIFFERENT SILICON CONCENTRATIONS
2.2 MATERIALS AND METHOD
2.2.1 Study site, plant materials and treatments
Two independent experiments were carried out during 2010. The experiments were conducted in temperature controlled glasshouse (experiment one) and irrigated tunnel (experiment two) at the University of KwaZulu-Natal (UKZN), Pietermaritzburg. The study used 10 selected soybean genotypes. Details of the test genotypes are indicated in Table 2.1
49
Table 2.1. List, pedigree and seed source of ten soybean genotypes used in this study.
Name Pedigree Source*
BARC-4 Clark 63(8)/Hardee. USDA
L82-1449-II - USDA
L76-1988 Williams(6) x (Harosoy(5) x D54-2437) USDA
BARC-2 Clark 63(8) x (Hill x Clark) USDA
Clark Lincoln(2) x Richland
USDA
Williams Wayne x L57-0034 (Clark x Adams)
USDA
BARC-14 nodulated D76-8070(4) X Clark rj1
USDA
BARC-17 nodulated Ripley(4) X Clark rj1
USDA
Magoye - Landrace
LS 6161 R - Link seeds, South Africa
* USDA=United States Department of Agriculture
Genotypes were subjected to three different silicon concentrations (0, 200 and 250 ppm Si) prepared from potassium silicate (KSi) and water. The silicon that was used in the trials was a soluble silica liquid fortified with potassium (AgriSilTM). This silicon was obtained from PQ Silicas South Africa. The solution of KSi was made up of silicon at 9.8% of the solution. This percentage was used to calculate the concentrations for the treatments.
2.2.2 Preparation of silicon concentrations
The potassium silicate solution contained 9.8% silicon. Therefore in 100 ml of potassium silicate the concentration of silicon is 9.8 ml. The concentration of the three prepared silicon treatments were measured in parts per million (ppm). The silicon treatments were made by diluting the potassium silicate solution in water. The amount of potassium silicate used for each concentration was calculated using the formula:
C1V1=C2V2. The initial concentration of silicon (C1) in the potassium silicate solution was 9.8 x104 ppm and the initial volume was (V1). The concentration of silicon that is required is (C2) 100 ppm and the volume that is required is (V2) 10000 ml. This means
that 10.2 ml of KSi when added to 10L of liquid (water) would give a solution that contained silicon in the concentration of 100ppm.
Therefore 20.4 ml of KSi was used to make up the 200ppm treatment and 25.5 ml of KSi was used to make up the 250ppm treatment. These treatments were prepared weekly using only plastic measuring equipment.
2.2.3 Experimental design and planting
The experiments were set out in the randomized complete block design consisting two factors i.e. ten genotypes and three silicon levels replicated three times. The 30 treatment combinations were randomly assigned in 90 plastic pots of 30cm diameter with a volume of 4 liters. Pots were filled with composted pine bark (National Plant Food cc, South Africa) potting medium which consisted fine composted pine bark with a high water holding capacity. Four plants were established per pot (Figures 2.1 and 2.2).
Figure 2.1. Two weeks old soybean plants established in the glasshouse at the University of KwaZulu-Natal in Pietermaritzburg.
51
Figure 2.2 Two weeks old soybean plants established in the irrigated tunnel at the University of KwaZulu-Natal in Pietermaritzburg, taken two weeks after planting.
2.2.4 Application of silicon and fertilizers
After planting, the three concentrations of silicon was applied twice a week and water applied as required until maturity. Prior to the study the soil was sampled and analyzed by Fertilizer Advisory Service, KwaZulu-Natal Department of Agriculture and Environmental Affairs, Soil Fertility and Analytical Services, Pietermaritzburg (Table 2.2). The details of the sampled soils are presented in Table 2.2 were used as a guide for the application of fertilizer. All soybean pots received 200 ml of fertilizer containing Nitrogen, Phosphorus and Potassium (NPK), in the ratio 3:1:3 once a week subsequent of and 150 ml calcium nitrate as slow releasing fertilizer once a month until maturity subsequent to emergence.
Table 2.2 Nutrient and lime analysis of soil sampled for this study as determined by the Fertilizer Advisory service, KwaZulu-Natal Department of Agriculture and Environmental Affairs, Soil Fertility and Analytical Services, Pietermaritzburg
P (mg/L)
K (mg/L)
Ca (mg/L)
Mg (mg/L)
EA (cmol/L)
TC
(cmol/L) AS % pH (KCl)
Zn (mg/L)
Mn (mg/L)
Cu (mg/L)
MIR clay
%
MIR organic
C % MIR N %
290 1737 1722 490 0.25 17.31 1 5.65 29.5 45 1.9 <5 >6 >0.6
EA=exchange acidity; TC=total cations; AS=acid saturation; MIR=mid-infrared 2.2.5 Data collection and analysis
In both experiments agronomic data were collected. The days to 50% flowering was taken when two of the four plants in each pot for each genotype at each level of silicon produced flowered. Days to 50 % maturity taken when two of the four plants in each pot for each genotype reached maturity i.e. pods were ready for harvesting. The plant height was taken at maturity and was measured in millimeters (mm) taken from soil surface to plant apex for all plants. At maturity the plants were harvested, the number of pods per plant counted for all plants; number of seeds per pod was counted for all plants; seed yield was taken in (g/pot) for all pots; the weight of 100 seeds was obtained by weighing a random sample of 100 seed, the roots (cut at rhizosphere) and aerial parts were harvested and dried for 72 hours at 70oC in a LABOTEC TERM-O-MAT (Labotec Oven, Model number 385, South Africa) oven before weighing to determine the dry root mass (g) and dry shoot mass (g) respectively together making up the dry biomass. The harvest index was then calculated by dividing the seed yield by the sum of the dry biomass and seed yield. The data collected for each experiment was analyzed separately then a combined analysis of variance carried out using GenStat (Genstat, 2009). Correlation analysis was conducted using the Pearson model on SPSS (2001) to test the association of traits. Principal component analysis was conducted using SPSS (2001) for each experiment. Principal component analysis was used to identify the number of influential components and predictor variables represented in each of the component. The method helps to find a linear combination of variables as a component that accounts for more variation than in the original variables. It then finds another subsequent component uncorrelated with the previous one that accounts for as
53
much of the remaining variation. Consequently, a few uncorrelated components will account for most of the variation that can be used to replace the original variables into manageable subsets of characters. Thus, the PCA analysis is very useful when several correlated traits are present in the study that might adversely affect the response.