Climate Factor: Humidity
Climate Factor: Humidity, Temperature, Rainfall
(i) Effect of Climate on Crops
Climate is one of the most important factors that determine the suitable type of crop according to the particular area. The crop production mainly depends on climate. The number of crops variation is determined by climate. Solar energy, temperature, and amount of rainfall are the Climatic factors that influence mainly growth, development and crops production.
Influence of Temperature
• Temperature is also an important factor for the growth and development of crops.
• For maximum dry matter collection, the optimum temperature is the main requirement.
Basic temperature points
Temperature limits for
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Advantages of Weeds
• Weeds Check the speed of Wind and Erosion of Water and Soil.
• Few category of Weeds is used as forage for Animal.
• Some of them can be used as Leafy Vegetables: E.g Tandulaja (Amaranthus Polygamus), Math (Amaranthus viridis)
• Some Weeds contain Medicinal Value:
• Some Weeds are important from the Economic point of view.
• It helps in Reclamation of Soils which are alkaline in nature.
• Certain Weeds Serves as embellishing and protect plants.
Example of Seed Dataset:
We have taken a very famous example of soyabean dataset to show the implementation of PCA and how it helps to identify key attributes to handle this. We took dataset in CSV format. This dataset consists of 35 attributes and 47 records containing information like date, plant-stand, temperature, hail and so on, Representing 35 attributes is not an easy task and even if we are able to represent then it becomes difficult to analyze which attribute is more important for our study. So we are using machine learning algorithm to get the most important attributes. To apply this machine learning technique first we will have to divide our dataset into training and testing part. In training set, we have 36 records with 2 attributes for X-axis and one attribute for Y-axis.In the test set, we have 10 records for both X & Y axis with one attribute. It gives us explained variance matrix which describes how much our approach is going to predict correctly. The visual representation using python for this dataset
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