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Maji P, Mistri B. Comparative assessment of soil quality dynamics using SQI modelling approach: a study in rice bowl of West Bengal, India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:567. [PMID: 38775991 DOI: 10.1007/s10661-024-12697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 04/30/2024] [Indexed: 06/21/2024]
Abstract
The study attempted to evaluate the agricultural soil quality using the Soil Quality Index (SQI) model in two Community Development Blocks, Ausgram-II and Memari-II of Purba Bardhaman District. Total 104 soil samples were collected (0-20 cm depth) from each Block to analyse 13 parameters (bulk density, soil porosity, soil aggregate stability, water holding capacity, infiltration rate, available nitrogen, available phosphorous, available potassium, soil pH, soil organic carbon, electrical conductivity, soil respiration and microbial biomass carbon) in this study. The Integrated Quality Index (IQI) was applied using the weighted additive approach and non-linear scoring technique to retain the Minimum Data Set (MDS). Principal Component Analysis (PCA) identified that SAS, BD, available K, pH, available N, and available P were the key contributing parameters to SQI in Ausgram-II. In contrast, WHC, SR, available N, pH, and SAS contributed the most to SQI in Memari-II. Results revealed that Ausgram-II (0.97) is notably higher SQI than Memari-II (0.69). In Ausgram-II, 99.72% of agricultural lands showed very high SQI (Grade I), whereas, in Memari-II, 49.95% of lands exhibited a moderate SQI (Grade III) and 49.90% showed a high SQI (Grade II). Sustainable Yield Index (SYI), Sensitivity Index (SI) and Efficiency Ratio (ER) were used to validate the SQIs. A positive correlation was observed between SQI and paddy ( R2 = 0.82 & 0.72) and potato yield (R2 = 0.71 & 0.78) in Ausgram-II and Memari-II Block, respectively. This study could evaluate the agricultural soil quality and provide insights for decision-making in fertiliser management practices to promote agricultural sustainability.
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Affiliation(s)
- Piyush Maji
- Department of Geography, The University of Burdwan, Burdwan, West Bengal, India, 713104.
| | - Biswaranjan Mistri
- Department of Geography, The University of Burdwan, Burdwan, West Bengal, India, 713104
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Shi H, Lu X, Sun T, Liu X, Huang X, Tang Z, Li Z, Xiang Y, Zhang F, Zhen J. Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters. PLANTS (BASEL, SWITZERLAND) 2024; 13:1314. [PMID: 38794385 PMCID: PMC11124996 DOI: 10.3390/plants13101314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.
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Affiliation(s)
- Hongzhao Shi
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Xingxing Lu
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Department of Mechanical Engineering, College of Mechanical and Electrical Engineering, Yangling Vocational & Technical College, Xianyang 712100, China
| | - Tao Sun
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Xiaochi Liu
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Xiangyang Huang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Zijun Tang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Zhijun Li
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Youzhen Xiang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Fucang Zhang
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
| | - Jingbo Zhen
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China; (H.S.); (X.L.); (T.S.); (X.L.); (X.H.); (Z.T.); (F.Z.); (J.Z.)
- Institute of Water–Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China
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Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation. ELECTRONICS 2021. [DOI: 10.3390/electronics10182183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.
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