**Internal Code :****Subject Code :**ERS 301**University :****Subject Name :**

The study was carried out in order to explore the responses of rock phosphate, super phosphate and poultry manures on height and chlorophyll content of corn. After estimating these parameters, results showed that chlorophyll content and the height of corn is highly affected by different fertilizers. We have used R software to interpret the results. We have calculated the p-value and correlation coefficient between parameters of corn data. Also, we have used graphical study to describe the level and impact of fertilizers on the height of corn and chlorophyll content.

**Keywords**

Corn height, Chlorophyll, Poultry manures, Rock phosphate, Super phosphate.

Corn is a crop which is able to be used for many purposes. It gives food for person, animals and birds. Corn can be seen as rich source of raw material. For the industry, it is extensively used for the preparation of many useful things such as cornstarch, corn syrup, corn dextrose, corn flakes etc.

The impact of fertilizers on growth of height and chlorophyll of corn is the main purpose of our research. Global fertilizer supply has increased drastically since 1960. Fertilizers have been highly recommended to improve the quality of corn production. Super phosphate is more costly than rock phosphate. And rock phosphate is a natural source so it has focussed our attention on rock phosphate fertilizer because of its lower cost than super phosphate. A field experiment was conducted .For evaluating the effects of poultry manures, rock phosphate and super phosphate fertilizers and involvement between treatments on corn production on a soil, we have conducted a field experiment.

In recent time, the execution of hybrid method of manufacturing has mostly applied in production of food grains which includes use of advanced fertilizers like rock phosphate. The annual rate of fertilizer growth between 1990 and 2020 has been especially high for Asia. In conventional Agriculture, farmers apply high doses of fertilizers to realize high crop yield. However, their indiscriminate use is causing problems in soil structure deterioration, ground water pollution, high nitrate in vegetables.

Several authors have discussed these types of study in the agricultural science. These study contain the fertilizer effect on soil and the impact on chlorophyll and various features on different crops. Comparative growth of four tropical pastures legumes and guinea grass with different phosphorus sources (Kerridge and Ratcliff, 1982). Applications of seasonal climate forecasting in agricultural and natural ecosystems (Hammer et al., 2000).Effect of rock phosphate and superphosphate fertilizer on the productivity of maize (Lukiwati, 2002). The corn production as affected by Tillage system and starter fertilisers (Vetsch and Randall, 2002). Effectiveness of Farmyard Manure, Poultry Manure and Nitrogen for Corn Productivity (Khaliq et al., 2004).

In inventory of Australian software tools for on farm water management (Crawn et al. 2005). The analysis of high yielding maize production which is affected by environmental conditions like temperature and various agroeconomic inputs like fertilisers, poultry manures (Birch et al. 2008). Reliability and Production of quick to medium maturity maize in areas of variable rainfall in north-east Australia (Birch et al. 2008). Analysis and modelling of water stress on Maize growth and yield in dryland conditions (Song et al., 2010). Corn residual nitrate and its implications for all nitrogen management in winter wheat (Forrestal, 2011). Effect of Phosphorus Fertilizers on Growth and Physiological Phosphorus Use Efficiency of Three Soy Bean Cultivars (Dalshad et al., 2013).

Various engineering and innovation techniques and also enlightened the environmental , socio-economic factors and plant science (Koech et al., 2015). Changes of dry matter, biomass and relative growth with different phonological stages of corn (Koca and Erekul, 2016). Nitrogen and phosphorus are critical determinants of plant growth and productivity. Influence of nitrogen and phosphorus on the growth and root morphology of Acer mono (Razaq et al. 2017). Furthermore, non-optimal supply of fertilizers at sowing should not be followed to reduce adverse impact on corn production. Use of rock phosphate and other forms of fertilizers and explained positive impact on water consumption (Koech and Langat, 2018). Synergistic and Antagonistic Effects of Poultry Manure and Phosphate Rock on Soil P Availability, Ryegrass Production, and P Uptake (Poblete-Grant et al., 2019). Comparison of nutrient management recommendations and soil health indicators in southern Idaho (Leytem et al., 2019).

We have taken the data set of 64 observations which has 4 treatments i.e. control, rock phosphate, super phosphate and poultry manures which has effected the height and chlorophyll of corn crop. Each treatment has 16 groups. These various fertilizers have been applied on small area of 50m x 50m. For interpreting results,we have used R software for statistical analysis.

We have calculated the p-value for analysing the data that parameters of corn data are significant and non-significant. We have also built the analysis of variance table .

For finding the p-value of different fertilizers, we have used logistic regression analysis.

The summary of the logistic regression model gives the variation between different fertilizers.

For graphical representation of the given data, we have drawn a Box plot for chlorophyll and height. Which has represented its mean, median, quartile deviations. After this we have drawn fit plot charts for fitted our variable points in the best fit line.

**Boxplot-** Boxplot is used to represent the distribution of data. They have showed that how far the extreme values are the most of the data. It has been built five values: the the maximum value, minimum values, the first quartile, the third quartile and the median. The largest and the smallest values label the end points of the axis. The middle end of the data fall inside the box. The first quartile one end of the axis and the third quartile marks the other end of the box.

**Figure 1:** Boxplot for Distribution of Chlorophyll

The boxplot of figure 1 have shown the measures of dispersion for chlorophyll content for each treatment i.e. Control, Poultry manures, Rock phosphate and the Super phosphate.

**Figure 2:** Boxplot for Distribution of Height

The boxplot of figure2 have shown the measures of dispersion for height of corn for each treatment i.e. Control, Poultry manures, Rock phosphate and the Super phosphate.

When, we talk about the linear relationship between two variables, a straight line become in our mind. However, linear relationship does not fit to explain all the data always. Many data must fit the curves in present time. We must placed the nonlinear regression instead of linear regression, it is the powerful alternative to linear regression because it provides the more flexible curve-fitting functionality than the linear regression. The idea is to find the nonlinear function that the best fits the specific curve in the given data. We have used R software to make this task easier.

The plot of Chlorophyll against height in Figure 3 and Figure 4 represents the relationship between these two variables. There are two fit plots for chlorophyll vs height. Figure 3 have shown a linear relationship between chlorophyll vs height and Figure 4 have shown a curvilinear relationship between chlorophyll vs height.

The slope in this linear regression analysis implies a strong dependence of chlorophyll content on the height of corn. This suggests that chlorophyll and the height of corn are useful covariates for the analysis. This analysis is based on 4 observations (Control, Rock phosphate, Super phosphate, Poultry manures ).

**Figure 3:** Plot for Chlorophyll vs Height

**Figure 4:** Plot for Chlorophyll vs Height

This analysis is based on 4 treatments (Control, Rock phosphate, Super phosphate, Poultry manures ). The estimates of the slopes within each treatment group along with the standard errors and the t and p values for chlorophyll are given in Table 1. The estimates of the slopes within each treatment group along with the standard errors and the t and p values for height of corn are given in Table 2.

The ANOVA table provides much information but the researcher has the major interest to be focused on the value in the ‘p-value’ column, because this column presents the exact significance level of the ANOVA. If the value found in this column is less than the critical value of alpha set by the researcher, then it concludes that the effect is said to be significant. Since this value of the p-value is usually set at .05, any value less than set p-value, it will result significant effects, while any value greater than this set p-value will result non significant effects. The result has shown in the Table 1, the calculated value is 4.995e^{-13}, so the effects would be statistically significant.

Similarly, the result has shown in the Table 2, the exact significance is < 2.2e^{-16 }Which is less than the tabulated value (0.05), then the effects would be statistically significant.

**Table 1:** Analysis of Variance for Chlorophyll

Source |
Degree of Freedom |
Sum of squares |
Mean sum of squares |
F-Value |
Prob>F |

Treatment(Fertilizers) |
3 |
1012.52 |
337.51 |
34.254 |
4.995e |

Error |
60 |
591.18 |
9.85 |
||

Corrected Total |
63 |
1603.7 |

R-square |
Adjusted R-square |
Root MSE |

0.6314 |
0.6129 |
3.138 |

The results obtained by doing the analysis using the R programming are given in the sequence.

The first output of analysis (given in Table 1) for chlorophyll which performs a simple linear regression analysis over all the 64 observations. The model is able to explain about 90 per cent of the total variability in the data (R^{2}= 0.6314; p=4.995e^{-13}).

**Table 2:** Analysis of Variance for Height

Source |
Degree of freedom |
Sum of squares |
Mean sum of squares |
F-Value |
Prob>F |

Treatment(Fertilizers) |
3 |
25263.9 |
8421.3 |
72.885 |
< 2.2e |

Error |
60 |
6932.6 |
115.5 |
||

Corrected Total |
63 |
32196.5 |

R-square |
Adjusted R-square |
Root MSE |

0.7847 |
0.7729 |
10.747 |

The second output of analysis (given in Table 2) for height of corn which performs a simple linear regression analysis over all the 64 observations. The model is also able to explain about 90 per cent of the total variability in the data (R^{2}= 0.7729; p< 2.2e^{-16}).

**Table 3:** Parameter estimates with standard errors for Chlorophyll

Parameters |
Estimated value |
Standard error |
t- value |
Prob > |t| |

Intercept |
32.3938 |
0.7847 |
41.280 |
< 2e-16 |

Poultry manure |
10.2063 |
1.1098 |
9.197 |
4.62e-13 |

Rock phosphate |
5.7000 |
1.1098 |
5.136 |
3.21e-06 |

Super phosphate |
9.1250 |
1.1098 |
8.222 |
2.05e-11 |

Table 3 provides a solution for testing the equality of slopes for each treatment.

This leads us to a conclusion that the treatment effects differ significantly (p =4.995e^{-13}). This is the adjusted treatment SS and allows us to test the parameters effects, adjusting for all other factors (in this case the chlorophyll) included in the model. The reason for adjustments has already been described above. It is, therefore, evident that the covariate has an impact on the inference and treatment effects, which were significantly different in the absence of covariate has become homogeneous in the presence of covariate.

**Table 4:** Parameter estimates with standard errors for Height

Parameters |
Estimated value |
Standard error |
t- value |
Prob > |t| |

Intercept |
57.938 |
2.687 |
21.560 |
< 2e-16 |

Poultry manure |
43.188 |
3.800 |
11.364 |
< 2e-16 |

Rock phosphate |
26.000 |
3.800 |
6.841 |
4.66e-09 |

Super phosphate |
52.125 |
3.800 |
13.716 |
< 2e-16 |

Table 4 provides a solution for testing the equality of slopes for each treatment.

This leads us to a conclusion that the treatment effects differ significantly (p < 2.2e^{-16}). This is the adjusted treatment SS and allows us to test the parameters effects, adjusting for all other factors (in this case the height of corn) included in the model.

The boxplots which have shown in figure 1 and figure 2 represents the distribution of chlorophyll and the height of corn through treatments: control, Rock phosphate, Super phosphate and poultry manures. These boxplots have given all measures of dispersion for chlorophyll and the height of corn.

We need to determine the differences between the means are statistically significant or not. For this, comparison between the calculated p-value and tabulated p-value has been done at the significance level for analysing the hypothesis. The null hypothesis implies that the population means are all equal. Usually, a significance value sets at 5% level. When we take a significance level of 0.05, it indicates the conclusion of 5% risk of a difference exists when there is no absolute difference.

If the calculated value is less than the tabulated value, we accept the null hypothesis. If the calculated value is greater than the tabulated value, we reject the null hypothesis.

In this article, we get that that calculated value is less than the tabulated value for chlorophyll content, which is not significant at 5% level of significance, and the null hypothesis H_{0} may be accepted which has shown in Table 1 and we also have seen that the calculated value is less than the tabulated value for height of corn, which is not significant at 5% level of significance, and again the null hypothesis H_{0} may be accepted which has shown in Table 2.

So, the effects of various treatments on chlorophyll and height of corn would be statistically significant.

For enhance our study, we have built two fit plots for chlorophyll vs height which gives the linear line (in figure 3) and curve for best fit. These two types of lines cover the most of the points of the variables: chlorophyll and height of corn.

The author wishes to acknowledge University of New England for the provision of the data set and statistical guidance.

**References**

Birch, C.J., McLean, G. and Sawers, A. (2008): Analysis of high yielding maize production- a study based on a commercial crop. Australian Journal of Experimental Agriculture, 48(3), 296-303.

Birch, C.J., Stephen, K., McLean, G., Doherty, A. Hammer, G.L. and Roberston, M. (2008): Reliability and Production of quick to medium maturity maize in areas of variable

rainfall in north-east Australia. Australian Journal of Experimental Agriculture, 48(3), 326-334.

Crawn, T., Townsend, J. and Wilshire, A. (2005): In inventory of Australian software tools for on farm water management, Cooperative Research Centre of Irrigation futures.

Dalshad, A. D, P. D., Pakhshan, M. M and Shireen, A. Amin (2013): Effect of Phosphorus Fertilizers on Growth and Physiological Phosphorus Use Efficiency of Three Soy Bean Cultivars. Journal of Agriculture and Veterinary Science, 3(6), 2319-2380.

Forrestal, P.J. (2011): Corn residual nitrate and its implications for all nitrogen management in winter wheat .Faculty of the Graduate School of the University of Maryland, College Park.

Hammer, G.L, Nicholls, N. And Mitchell, C. (2000): Applications of seasonal climate forecasting in agricultural and natural ecosystems: the Australian experience. Atmospheric and Oceanographic Sciences Library, Kluwer Academic Publishers; Dordrecht, The Netherlands, 21, 453–462.

Kerridge, P.C. and Ratcliff, D. (1982): Comparative growth of four tropical pastures legumes and guinea grass with different phosphorus sources. Tropical Grasslands, 16, 33-40.

Khaliq, T, Mahmood, T, Kamal, J and Masood, A. (2004): Effectiveness of Farmyard Manure, Poultry Manure and Nitrogen for Corn (Zeamays L.) Productivity. International journal of Agriculture and Biology, 1560–8530/2004/06–2–260–263 http://www.ijab.org.

Koca, Y. and Erekul, O. (2016): Changes of dry matter, biomass and relative growth with different phonological stages of corn. Agriculture and Agricultural Science Procedia, 10, 67-75.

Koech, R , Smith, R. And Gillies, M. (2015): Trends in the use of surface irrigation in Australian irrigated agriculture: An investigation into the role surface irrigation will play in future Australian agriculture. Water, 42(5), 84-92.

Koech, R. and Langat, P. (2018): Improving irrigation water use efficiency: A review of advances, challenges, opportunities in the Australian context, Wate,10, 3-17.

Leytem, A.B., Rogers, C.W., Tarkalson, D., Dungan, R.S., Haney, R.L. and Moore, A.D. (2019): Comparison of nutrient management recommendations and soil health indicators in southern Idaho. Agrosystems, DOI: 10.1002/agg2.20033.

Lukiwati, D. R. (2002): Effect of rock phosphate and superphosphate fertilizer on the productivity of maize var. Bisma. J.J. Adu-Gyamfi (Ed.). Food security in nutrient-stressed environments: exploiting plants' genetic capabilities, 95, 183-187.

Poblete-Grant, P. , Biron, P., Bariac, T. , Cartes, P. , Mora , M., and Rumpel, C. (2019): Availability, Ryegrass Production, and P Uptake. Agronomy, 9, 191; doi: 10.3390 /agronomy 9040191.

Razaq, M., Zhang, P., Shen,H., Salahuddin (2017): Influence of Nitrogen and Phosphorous on the Growth and Root Morphology of Acer Mono, PLOS,12(2): doi: 10.1371/journal.pone.0171321.

Song, Y., Birch, C., Qu, S., Doherty, A. and Hanan, J. (2010): Analysis and modelling of water stress on Maize growth and yield in dryland conditions, Plane roduction Science, 13(2), 199-208.

Vetch, J.A. and Randall, G. (2002): The corn production as affected by Tillage system and starter fertiliser, American Society of Agronomy, 94, 532-540.

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