ITEST™

LEAF

PRECISION LEAF ANALYSIS

Agri Technovation integrates individual leaf samples and maps with the interpretation of leaf tissue analyses, including ratios between nutrients and corrective recommendations.

Optimal foliar nutrition is quite a complex subject. With leaf analyses, important information about the mineral nutrient status of the crop and the physiological activity of specific plant organs, often not visible in the field, can be obtained. These analyses can quantify mineral nutrient deficiencies to address the ‘hidden hunger’ in the plant, and also indicate the physiological reaction of the plant to current and future management practices and climate regimes. The focus of leaf analyses is to determine a plant’s reaction to available nutrition levels and/or the result of applied nutrients and management practices. We use two tried and tested methods to identify nutrient deficiencies in crops.

ITEST LEAF

ITEST™LEAF analysis

Our ITEST™LEAF service offers producers leaf mineral nutrient analyses with maps, along with an interpretation of the results by a crop specialist (1st method). In addition to the standard tissue mineral analysis, mineral nutrient relationships are also expressed according to a DRIS (Diagnosis and Recommendation Integrated System) analysis (2nd method). The advantage of this analysis is that the time of sampling does not impact the results (ratios).

By using this two-method approach, nutrient levels in the specific plant tissues and the relationship between the nutrients (DRIS) are integrated to provide a comprehensive recommendation regarding plant nutrition.

Furthermore, both the standard minimum/maximum norms and the relationships between the elements are used to compile a crop-specific risk profile during the season. ITEST™LEAF analyses offer producers various benefits, such as:

• Accurate information

• A detailed guideline for effective crop production

• The identification of nutritional profiles of various cultivars and genetics

• The ability to act proactively instead of reactively

• More effective use of inputs, usually resulting in cost savings

• Specific GPS-point data collection which is representative of that field

• Establishment of crop phenological stage norms for cultivars through data mining