Potash fertilizer supplies the essential element, potassium, for the stimulation of plant growth. It plays a critical role in the growth process and is essential to boost yields of many major crops. Potash fertilizer are available in different types with different physical and chemical characteristics. Potash fertilizer includes red granular, red standard, white granular, white soluble, and pink standard. These products range in size from a fine powder (size ~ 0.2 mm), standard (size ~ 0.8 mm), granular (size ~ 2.5 mm), and coarse with size of almost 4 mm (Garret 1996).
Potash fertilizer is hygroscopic. Fertilizer adsorbs water when it is exposed to an atmosphere with high relative humidity. If the moisture content of the potash fertilizer exceeds 0.2%, caking and dust formation can occur after subsequent drying (Zhou 2000). Caking and dust formation are problematic because they impede the flow of potash during distribution and agricultural application (Peng et al. 1999).
Knowledge of moisture content in stored potash is crucial because it can be used as a product quality indicator and management tool for potash during its storage, shipment, and handling. Typically, moisture content of potash fertilizer is determined by drying and weighing techniques. Reflectance measurements may represent a suitable alternative. No studies using NIR spectroscopy for determining moisture content of inorganic fertilizers have been undertaken, previously.
Use of NIRS techniques to sense moisture content has been the subject of many other investigations. NIR spectroscopy has been used to measure moisture content of food products, agricultural products, and manure. Ren and Chen (1997) used NIRS to determine the moisture content of ginseng roots. Calibration equations were developed using wavelengths in the 1100 –2500 nm region and first order derivatives and scatter correction were used. High correlation and low SEP were attained during validation.
Near infrared reflectance spectroscopy has also been used to successfully measure moisture content of food products. Pioneering work in this field was conducted by Ben-Gera and Norris (1968a) who used NIRS to measure the near-infrared absorbance properties of meat emulsions. The difference in optical density between 1800 and 1725 nm gave a high correlation to moisture content. Ben-Gera and Norris (1968b) also used NIRS to determine moisture content of ground soybeans. In this research a calibration between moisture content and intensity of the 1940 nm water absorption band was constructed. Adamopoulos and Goula (2004) used NIRS to measure the moisture content of taramoslata, a traditional Greek food. Calibration models based upon six wavelengths were developed using multiple linear regression. Lee et al. (1997) used NIRS to measure moisture content of Cheddar cheese curds. A high degree of correlation was obtained during validation. Wold and Isaksson (1997) used NIRS to determine the moisture content of whole Atlantic salmon, wherein results
showed that NIRS was suitable for non-destructive determination of moisture content.
Finally, NIRS has been used by several researchers to measure the moisture content in manure from several species. Reeves (2001) used NIRS to determine the moisture content of poultry manure. In this research, partial least squares regression (PLS) was used to develop calibration models and spectral data were treated using either multiplicative scatter correction or mean and variance scaling. Malley et al. (2002) used NIRS to determine the moisture content of hog manure in pits and lagoons, with good results. Models were developed using multiple linear regression and first and second order derivatives were used for data smoothing.
The goal of this research was to investigate the correlation between moisture content and spectral reflectance for red standard potash with moisture content between 0 and 1%. This could lead to the development of an ability to remotely sense moisture content in potash piles. Specific objectives were:
1. To investigate reflectance properties of red standard potash in the visible and near infrared portions of the
electromagnetic spectrum, and
2. To select optimum wavelengths and develop models for moisture content prediction based on these wavelengths. <based on>
(Source: Faraji, H., Crowe, T., Besant, R., Sokhansanj, S. and Wood, H., Department of Agricultural and Bioresource Engineering, Department of Mechanical Engineering, and Department of Electrical Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. (http://www.engr.usask.ca/societies/csae/protectedpapers/c0301.pdf)Read more