Nitrogen is an essential element for plant growth and development; however, due to environmental pollution, high nitrate concentrations accumulate in the edible parts of these leafy vegetables, particularly if excessive nitrogen fertilizer has been applied. Consuming these crops can harm human health; thus, developing a suitable strategy for the agricultural application of nitrogen fertilizer is important. Organic, inorganic, and liquid fertilizers were utilized in this study to investigate their effect on nitrate concentrations and lettuce growth. The results of this pot experiment show that the total nitrogen concentration in soil and the nitrate concentration in lettuce increased as the amount of nitrogen fertilizer increased. If the recommended amount of inorganic fertilizer (200 kg·N·ha−1) is used as a standard of comparison, lettuce augmented with organic fertilizers (200 kg·N·ha−1) have significantly longer and wider leaves, higher shoot, and lower concentrations of nitrate.

Effects of Nitrogen Fertilizers on the Growth and Nitrate Content of Lettuce (Lactuca sativa L.)

Carbon and nitrogen mineralization kinetics in soil of rice–rice system under long term application of chemical fertilizers and farmyard manure

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.

Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Dessureault-Rompre, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N 0) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R 2=0.17–0.18) of SNS...

https://bioone.org/journals/canadian-journal-of-soil-science/volume-95/issue-1/cjss-2014-057/Predicting-soil-nitrogen-supply-from-soil-properties/10.1139/cjss-2014-057.pdf

Predicting soil nitrogen supply from soil properties

Potato production is critical to economy of Prince Edward Island (PEI), but has been linked to increasing groundwater nitrate contamination and anoxic events in estuaries. Given that PEI is entirely groundwater-dependent for potable water, this has resulted in considerable pressure to find solutions to protect water quality. However, our understanding of the controls on nitrate loading to groundwater, and the consequent potential to mitigate nitrate loading through beneficial management practices (BMPs), is limited. In PEI, flow and nitrate transport in the vadose zone are controlled by the matrix porosity of the till and sandstone, while flow and transport in the saturated zone are controlled by a conductive fracture network with limited storage capacity. Diffusion of nitrate stored in the matrix may introduce a significant time lag between reduced nitrate loading from implementation of BMPs and measureable improvement in groundwater quality. BMPs with potential to mitigate nitrate loading to groundwater through improved nitrogen fertilizer practices in producing potato crops have been identified, but implementation of these practices alone is not sufficient to control nitrate loading due to the economic context of potato production. Additional BMPs for improved potato cropping systems have also been identified; however, reducing the intensity of agricultural production, including the proportion of land in potato production, at the watershed level may be required to meet the drinking water guideline for nitrate in PEI. Given the important role of market forces in determining profitability of crop production, it becomes challenging to modify crop production practices to meet an environmental goal without some additional external incentive or public pressure.

Controls on Nitrate Loading and Implications for BMPs Under Intensive Potato Production Systems in Prince Edward Island, Canada

Prediction of Soil Nitrogen Supply in Potato Fields using Soil Temperature and Water Content Information

This study evaluated different strategies for use of a simple first-order kinetic model (N min = N 0 [1-e ―kt ] where No is potentially mineralizable N and k is the mineralization rate constant) to predict growing season soil N supply (SNS) in potato (Solanum tuberosum L.) fields under cool humid climatic conditions. Direct application of the kinetic model for the 0- to 15-cm depth significantly underestimated a field-based measure of plant available soil N supply (PASNS). Modeling strategies that considered the soil mineral N (SMN) present at the start of the growing season, or included a pool of labile mineralizable N (Pool I) not normally considered in determination of N 0 , performed better, but still underestimated high values of PASNS. Strategies which included a greater soil depth (0-30 cm), or which assumed that the mineralizable N pool was replenished during the growing season, overestimated PASNS. A strategy which used a higher value of k for Pool I gave the most promising results. Results of this study highlight the importance of considering both SMN and labile mineralizable N pools in predicting SNS, and suggest that it is possible to estimate growing season SNS in humid regions using simple kinetic models.

Prediction of Soil Nitrogen Supply in Potato Fields in a Cool Humid Climate

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Prediction of Soil Nitrogen Supply in Potato Fields using Soil Temperature and Water Content Information

Prediction of Soil Nitrogen Supply in Potato Fields in a Cool Humid Climate