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What are good soil test numbers?

A reading of 7 is neutral; crops typically grow best when pH is between 6 (slightly acidic) and 7.5 (slightly alkaline). Results of soil pH are reported on a logarithmic scale; a soil with a pH of 6 is 10 times more acidic than a soil with a pH of 7, and a pH of 5 is 100 times more acidic than a pH of 7.

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Soil testing is used to assess what soil nutrients are present or may be needed for optimum plant growth and yield potential. Due to variability in soil, lab analysis, and reporting, guidelines specific to your region may exist. A local agronomist or extension specialist can provide information specific to your area. The results from a soil test list the concentration of each measured nutrient, an interpretation value (low, optimum, and high), and recommendations for amendments or nutrient application.

Soil Lab Testing and Results

Soil sampling must be conducted properly to obtain quality soil test results. Each sample should be representative of the entire field or specified sampling unit. Comparing soil tests over time is the best method of evaluating nutrient management decisions. Samples must be taken at the proper depth during the same time frame each year that sampling is conducted. Sample depths can vary by test type, but it is usually 6-12 inches. Soil test results list the type of test conducted, the result, and may include interpretation or recommendations. When reviewing lab results, it is important to know what extraction method was used and how the results were reported. Labs may report results in parts per million (ppm) or lb/acre. To convert ppm to lb/acre, multiply ppm by 2 (lb/acre = ppm x 2). To convert lb/acre to ppm, divide lb/acre by 2 (ppm = lb/acre ÷ 2).

Soil Parameters in a Soil Lab Test

Soil test results include the amount of macronutrients nitrogen (N), phosphorus (P), and potassium (K); secondary macronutrients sulfur (S), calcium (Ca), and magnesium (Mg); and micronutrients copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), boron (B), chloride (Cl), nickel (Ni), and molybdenum (Mo) in the soil. Other soil characteristics that may be included in soil test results are organic matter (OM) content, soil pH, soluble salts (salinity), and cation exchange capacity (CEC). Nitrogen - An adequate supply of N is associated with high photosynthetic activity, vigorous growth, and dark-green plant vegetation. There are two forms of plant available N: nitrate (NO 3 -) and ammonium (NH 4 +). Nitrate is measured most often in soil tests. Soil test results report nitrate-N in lb/acre. It is important to remember that nitrate levels on a soil test reflect what is immediately available and not what will be available in the future from mineralization of organic matter or lost from leaching or denitrification. The Late Spring Nitrate Test, also known as the Pre-sidedress Nitrate Test (PSNT), may be used in-season when corn plants are 6-12 inches tall to determine how much N should be sidedressed. Since nitrate soil test thresholds are regional, consult the nitrate soil test thresholds specific to your geography. Phosphorus – Phosphorus is needed by the plant to store energy created from photosynthesis and carbohydrate metabolism to be used for plant growth and reproductive processes. Phosphorus is not as naturally abundant in the soil as other macronutrients and is relatively immobile. The amount of plant available P in the soil solution is related to soil pH. Different P extraction methods are used depending on the soil pH: Bray P (acidic soils), Mehlich-3 P (acidic soils), and Olsen P (neutral to alkaline soils). Test results may vary based on the extraction method or test used for measuring P. Table 1 provides recommendations for corn production based on soil test results from each of the three methods.

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What happens if K is too large in KNN?

The value of k in the KNN algorithm is related to the error rate of the model. A small value of k could lead to overfitting as well as a big value of k can lead to underfitting. Overfitting imply that the model is well on the training data but has poor performance when new data is coming.

codecademy.com - K-Nearest Neighbors - Supervised Learning - Codecademy

The Euclidean Distance between two points can be computed, knowing the coordinates of those points.

On a 2-D plane, the distance between two points p and q is the square-root of the sum of the squares of the difference between their x and y components. Remember the Pythagorean Theorem: a^2 + b^2 = c^2 ?

We can write a function to compute this distance. Let’s assume that points are represented by tuples of the form (x_coord, y_coord) . Also remember that computing the square-root of some value n can be done in a couple of ways: math.sqrt(n) , using the math library, or n ** 0.5 (n raised to the power of 1/2).

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