The Nutrient Expert decision support system improves nutrient use efficiency and environmental performance of radish in North China

Excessive fertilization has led to nutrient use inefficiency and serious environmental consequences for radish cultivation in North China.The Nutrient Expert(NE)system is a science-based,site-specific fertilization decision support system,but the updated NE system for radish has rarely been evaluated.This study aims to validate the feasibility of NE for radish fertilization management from agronomic,economic,and environmental perspectives.A total of 46 field experiments were conducted over four seasons from April 2018 to November 2019 across the major radish growing regions in North China.The results indicated that NE significantly reduced N,P_(2)O_(5),and K_(2)O application rates by 98,110,and 47 kg ha^(-1) relative to those in the farmers’practice(FP),respectively,and reduced N and P_(2)O_(5) inputs by 48 and 44 kg ha^(-1),respectively,while maintaining the same K_(2)O rate as soil testing(ST).Relative to FP and ST,NE significantly increased radish yield by 2.7 and 2.6 t ha^(-1)(4.2 and 4.0%)and net returns by 837 and 432 USD ha^(-1),respectively.On average,NE significantly improved the agronomic efficiency(AE)of N,P,and K(relative to FP and ST)by 42.4 and 31.0,67.4 kg kg^(-1) and 50.9,and 20.3 and 12.3 kg kg^(-1);enhanced the recovery efficiency(RE)of N,P,and K by 11.4 and 7.0,14.1 and 7.5,and 11.3 and 6.3 percentage points;and increased the partial factor productivity(PFP)of N,P,and K by 162.9 and 96.8,488.0 and 327.3,and 86.9 and 22.4 kg kg^(-1),respectively.Furthermore,NE substantially reduced N and P_(2)O_(5) surpluses by 105.1 and 115.1 kg ha^(-1),respectively,and decreased apparent N loss by 110.8 kg ha^(-1) compared to FP.These results indicated that the NE system is an effective and feasible approach for improving NUE and promoting cleaner radish production in North China.

Soil Nutrient Detection and Recommendation Using IoT and Fuzzy Logic

Precision agriculture is a modern farming practice that involves the usage of Internet of Things(IoT)to provide an intelligent farm management system.One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth.The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers.This can be done by identifying the deficient nutrients with the help of an IoT system.As traditional approach is time consuming,an IoT-enabled system is developed using the colorimetry principle which analyzes the amount of nutrients present in the soil and a fuzzy expert system is designed to recommend the quantity of fertilizers to be added in the soil.A set of 27 IF-THEN rules are framed using the Mamdani inference system by relating the input and output membership functions based on the linguistic variable for fertilizer recommendation.The experiments are conducted using MATLAB for different ranges of Nitrogen(N),Phosphorous(P)and Potassium(K).The NPK data retrieved by the system is sent to the ThingSpeak cloud and displayed on a mobile application that assists the farmers to know the nutrient information of their field.This system delivers the required nutrient information to farmers which results in efficient green farming.

Performance evaluation of IoT-based service system for monitoring nutritional deficiencies in plants

This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things(IoT)for monitoring nutritional deficiencies in plants and providing fertilizer recommendations.There are two distinct differences between this work and previous ones;namely,this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability.We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT.We have also successfully tested the performance of the service system platform using the JMeter software.The dependability value measured from the five tested variables(reliability,availability,integrity,maintainability,and safety)showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions.From a future perspective,this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields,reduce fertilizer costs,and prevent the use of excessive fertilizers,which can cause environmental pollution.

Alkaline hydrolyzable nitrogen and properties that dictate its distribution in paddy soil profiles

Nitrogen(N) is a key nutrient for rice production, and its bioavailability in paddy soils is strongly coupled to soil organic matter(SOM) cycling. A better understanding of potentially available N forms in soil, such as alkaline hydrolyzable N(AH-N), and their depth distribution will support the development of best management practices to improve the N use efficiency of rice while minimizing adverse environmental effects. Fifteen rice(Oryza sativa L.) fields from Southern Brazil were selected, and stratified soil samples were taken to a depth of 60 cm before crop establishment. Selected soil physical and chemical properties were analyzed to evaluate their relationships with AH-N contents in the soil profile. The AH-N contents below 20 cm varied extensively(increased,reduced, or constant) compared with that above 20 cm. Although clay and clay + silt contents were highly correlated to AH-N for some soils, the major property dictating AH-N distribution by depth was total N(TN), as the correlation between TN and AH-N was mainly by direct effect. The proportion of TN recovered as AH-N across sites and depths presented high amplitude, and thus AH-N was not a constant N pool across depths, indicating that AH-N can be affected by soil management practices even when TN showed no major changes. The distinct distribution of AH-N across soil sampling sites and depths indicates that depths greater than 20 cm should be considered when calibrating the AH-N index for N fertilizer recommendations for flooded rice in Southern Brazil.