Apparent electrical conductivity in correspondence to soil chemical properties and plant nutrients in soil

Spatial variability and relationship between soil apparent electrical conductivity (EC a), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. EC a technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil EC a within a Malaysian rice cultivation area using VerisEC sensor. The EC a values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil EC a was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly

Evaluation of leaf total nitrogen content for nitrogen management in a Malaysian paddy field by using soil plant analysis development chlorophyll meter

Problem statement: Laboratory plant testing is usually time-consuming and high-costing. Hence, plant nutrient variability must be measured rapidly and the information made known to the farmers before the new season starts. Site-specific crop management, well-established in some developed countries, is now being considered in other places such as Malaysia. Approach: The application of site-specific management principles and techniques to diverse crops and small-scale farming systems in Malaysia will present new challenges. Describing within-field variability in typical Malaysian production settings is a fundamental first step toward determining the size of management zones and the inter-relationships between limiting factors, for establishment of site-specific management strategies. Results: Measurements of rice SPAD readings and nitrogen content were obtained in a Malaysian rice paddy field. SPAD reading data was manually collected on 80DAT and measured using a Minolta SPAD 502. Leaf samples were collected at 60 points at the same time to compare results from sampling with SPAD reading values. Samples nitrogen content was analyzed in a laboratory. Analysis of variance, variogram and kriging were conducted to determine the variability of the measured parameters and also their relationship. SPAD reading and nitrogen content maps were created on the interpretation of the data was investigated. Conclusion/Recommendations: Finally the research indicated that SPAD readings are closely related to leaf N content which means the potential for technology of precision farming to understand and control variation in Malaysian production fields and also SPAD chlorophyll meter ability to monitor the N status of rice and recommend the amount of N fertilization. Additional research is needed to confirm the results with data from other fields and crops

A review of optical methods for assessing nitrogen contents during rice growth

Concerns over the use of nitrogen have been increasing due to the high cost of fertilizers and environmental pollution caused by excess nitrogen applications in paddy fields. Several methods are available to assess the amount of nitrogen in crops. However, they are either expensive, time consuming, inaccurate, and/or require specialists to operate the tools. Researchers have recently suggested remote sensing of chlorophyll content in crop canopies as a low-cost alternative to determine plant nitrogen status. This article describes the most recent technologies and the suitability of different remote sensing platforms for determining the status of chlorophyll content and nitrogen in crops. Finally, the role of vegetation indices in nutrient assessment is explained. Among different remote sensing platforms, a low altitude remote sensing system using digital cameras, which record data in visible bands can be used to determine the status of nitrogen and chlorophyll content. However, the vegetation indices need to be correctly chosen for best results

Multi-Spectral Images Tetracam agriculture Digital Camera to Estimate Nitrogen and Grain Yield of Rice at difference Growth Stages

Several methods are available to monitor the nitrogen content of rice during its various growth stages. However, monitoring still requires a quick, simple, accurate and inexpensive technique that needs to be developed. In this study, Tetracam Agriculture Digital Camera was used to acquire high spatial and temporal resolution images to determine the status of N and predict the grain yield of rice (Oryza sativa L.). Twelve pots of rice were subjected to four different N treatments (0, 125, 175 and 250 kg ha-1). Three replicates were arranged in a randomized complete block design to determine the status of N and predict rice yield. The images were captured at different growth stages (i.e., tillering, panicle initiation, booting and heading stage) of rice in each pot. N and grain yield were significantly correlated with NDVI (R2 = 0.78) and GNDVI (R2 = 0.88), especially at the panicle initiation and booting stages, respectively. The study demonstrated the suitability of using the Tetracam images as a sensor for estimating chlorophyll content and N. Moreover, the findings showed that the images revealed their potential use in forecasting grain yield at different growth stages of rice

Temporal variability of SPAD chlorophyll meter readings and its relationship to total nitrogen in leaves within a Malaysian paddy field

Recently, site-specific crop management, well-established in some developed countries, is now being considered in other places such as Malaysia. So, describing within-field variability in a typical Malaysian paddy field was conducted to show the temporal variability of SPAD readings and leaves total N. The main objective of this study was to seek appropriate tool to expedite the adoption of PF for double cropping rice cultivation. For this reason, SPAD readings data was collected at 2 different rice growth stages (55DAT and 80DAT) using a Minolta SPAD 502. Leaf samples were collected at 20 random points in each plot to compare the results from SPAD readings values. Nitrogen content was extracted from samples in a laboratory. Finally, SPAD readings and total nitrogen maps were created on the interpretation of the data. Semivariograms, visual observation and statistical analysis indicated higher sampling error and stronger spatial dependence at 80DAT and also same trends of SPAD readings and leaves total N in most areas of the field. The increasing of SPAD readings values with growth stage was observed in this study. SPAD readings at 55DAT had a better relationship to leaves total N than 80DAT. The study concluded that SPAD chlorophyll meter is able to provide a rapid and reasonably accurate estimate of leaf N content and the potential for applying principles and technology of precision farming to understand and control spatial and temporal variation in Malaysian paddy fields

Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale

Nitrogen is an important variable for paddy farming management. The objectives of this study were to develop and test a new method to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all visible bands derived from images captured using a conventional digital camera. The images from the 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index. Digital photographs of the upper most collared leaf of rice (Oriza sativa L.), grown over a range of soils with different nitrogen treatments, were processed into 11 indices and IPCA through six growth stages. Also a conventional digital camera mounted to an unmanned aerial vehicle was used to acquire images over the rice canopy for the purpose of verification. The result indicated that the conventional digital camera at the both leaf (r = −0.81) and the canopy (r = 0.78) scale could be used as a sensor to determine the status of chlorophyll content in rice plants through different growth stages. This indicates that conventional low-cost digital cameras can be used for determining chlorophyll content and consequently for monitoring nitrogen content of the growing rice plant, thus offering a potentially inexpensive, fast, accurate and suitable tool for rice growers. Additionally, results confirmed that a low cost LARS system would be well suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in rice farming in a fast, inexpensive and non-destructive way

Water stress and natural zeolite impacts on phisiomorphological characteristics of moldavian balm (Dracocephalum moldavica l.).

There is no doubt that certain plants offer valuable medicinal properties. But there are many challenges that local communities face in the harvesting and cultivation of these plants. Moldavian balm (Dracocephalum moldavica L.) is an important medicinal plant and there are three pharmaceutical products that originate from in Iran. Water source limitation is one of the important problems in Iran. The scientists suggest that active substance production is the results of environmental stress on plants. Natural zeolits have some properties such as water absorption and emission and nitrate leaching inhibition, which is useful for soil amendment. A pot experiment was conducted in greenhouse with 12 factorial treatments and three replicates. Four levels of Zeolit and three levels of water stress applied during the plant growth. Growth and development factors such as wet and dry weight, leaf area, chlorophyll content, number of leaves, root length and essential oils content were measured. The results showed that zeolit application had not a significant effect on dry weight. Moreover, there were not a significant effect of water stress on leaf area, number of leaves and root length. There was not an interaction between zeolit and water stress on wet and dry weight and also root length but this interaction was significant on leaf area, chlorophyll content, number of leaves and essential oils content. It concluded that medium level of zeolit with the lowest level of water stress recommended for herb and essential oil production of moldavian balm

Development of on-the-go soil organic matter sensor

Soil organic matter (OM) greatly influences soil quality and productivity. Conventional soil OM analysis is expensive, time consuming and laborious. To practice precision farming, one important step is to describe the variability of a farm and the conventional analysis is always delayed. Quick ground sensor or on-the-go sensor can help to achieve this need. With the development of a new technology, the soil OM information can be gathered in a real time basis by using Soil Organic Matter Sense (SOMSENSE) with the integration of software developed using MATLAB. A model of soil OM estimation based on Red, Green, and Blue (RGB) scales showed significant findings when plotting on 1:1 line. This technique will help farmers or farm managers to determine their field variability quickly to practice precision farming based on OM variability map

Remote quantification of soil organic carbon: role of topography in the intra-field distribution

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information