K. K. Mohapatra, R. K. Patra, A. Nayak, R. Tripathi, K. N. Mishra, L. M. Garnayak, B. S. Rath, Anjani Kumar
{"title":"使用最小数据集进行土壤肥力监测和绘图:稻田案例研究","authors":"K. K. Mohapatra, R. K. Patra, A. Nayak, R. Tripathi, K. N. Mishra, L. M. Garnayak, B. S. Rath, Anjani Kumar","doi":"10.35709/ory.2022.59.4.7","DOIUrl":null,"url":null,"abstract":"Odisha is an agrarian state where 73% population dependent on farming and most of them are small and marginal farmer. Farmers of this state also vulnerable to different natural extreme events like flood, drought and cyclone. For sustainable crop production it is essential to develop a village level the soil fertility index (SFI) map at a higher resolution. A total of 192 gridded soil samples with a resolution of 0.5 km × 0.5 km were collected in Tangi Choudwar. The Soil organic carbon (SOC), available Nitrogen (N), Phosphorous (P), potassium (K), and micronutrient (Fe, Cu, Mn, Zn and B) maps were developed using spatial variability analysis. Then a complete soil fertility index map was developed using the Arc GIS software. In spatial variability analysis SOC and micronutrients (Fe, Cu, Mn, Zn and B) content were higher in NE site of Tangi Choudwar block. The SFI value was range between 0.21 to 0.62 with higher SFI in reported at NE site. Person’s correlation of rice grain yield showed a positively correlation with SFI (0.44, P<0.05). These results showed that soil fertility index can be used in precision agriculture during crop planning and restoration of soil fertility of a region.","PeriodicalId":19618,"journal":{"name":"ORYZA- An International Journal on Rice","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Soil fertility monitoring and mapping with a minimal set of data: A case study for rice paddies\",\"authors\":\"K. K. Mohapatra, R. K. Patra, A. Nayak, R. Tripathi, K. N. Mishra, L. M. Garnayak, B. S. Rath, Anjani Kumar\",\"doi\":\"10.35709/ory.2022.59.4.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Odisha is an agrarian state where 73% population dependent on farming and most of them are small and marginal farmer. Farmers of this state also vulnerable to different natural extreme events like flood, drought and cyclone. For sustainable crop production it is essential to develop a village level the soil fertility index (SFI) map at a higher resolution. A total of 192 gridded soil samples with a resolution of 0.5 km × 0.5 km were collected in Tangi Choudwar. The Soil organic carbon (SOC), available Nitrogen (N), Phosphorous (P), potassium (K), and micronutrient (Fe, Cu, Mn, Zn and B) maps were developed using spatial variability analysis. Then a complete soil fertility index map was developed using the Arc GIS software. In spatial variability analysis SOC and micronutrients (Fe, Cu, Mn, Zn and B) content were higher in NE site of Tangi Choudwar block. The SFI value was range between 0.21 to 0.62 with higher SFI in reported at NE site. Person’s correlation of rice grain yield showed a positively correlation with SFI (0.44, P<0.05). These results showed that soil fertility index can be used in precision agriculture during crop planning and restoration of soil fertility of a region.\",\"PeriodicalId\":19618,\"journal\":{\"name\":\"ORYZA- An International Journal on Rice\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ORYZA- An International Journal on Rice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35709/ory.2022.59.4.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ORYZA- An International Journal on Rice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35709/ory.2022.59.4.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
奥里萨邦是一个农业邦,73%的人口依赖农业,其中大多数是小农和边缘农民。这个州的农民也容易受到洪水、干旱和飓风等不同自然极端事件的影响。为了实现作物的可持续生产,有必要开发高分辨率的村级土壤肥力指数(SFI)图。在Tangi Choudwar共采集了192个网格化土壤样品,分辨率为0.5 km × 0.5 km。利用空间变异分析,建立了土壤有机碳(SOC)、速效氮(N)、速效磷(P)、速效钾(K)和微量元素(Fe、Cu、Mn、Zn和B)图谱。然后利用Arc GIS软件编制了完整的土壤肥力指数图。土壤有机碳和微量元素(Fe、Cu、Mn、Zn和B)含量均以东北地块高。SFI值在0.21 ~ 0.62之间,NE位点的SFI值较高。稻米产量的Person相关与SFI呈显著正相关(0.44,P<0.05)。结果表明,土壤肥力指标可用于精准农业作物规划和区域土壤肥力恢复。
Soil fertility monitoring and mapping with a minimal set of data: A case study for rice paddies
Odisha is an agrarian state where 73% population dependent on farming and most of them are small and marginal farmer. Farmers of this state also vulnerable to different natural extreme events like flood, drought and cyclone. For sustainable crop production it is essential to develop a village level the soil fertility index (SFI) map at a higher resolution. A total of 192 gridded soil samples with a resolution of 0.5 km × 0.5 km were collected in Tangi Choudwar. The Soil organic carbon (SOC), available Nitrogen (N), Phosphorous (P), potassium (K), and micronutrient (Fe, Cu, Mn, Zn and B) maps were developed using spatial variability analysis. Then a complete soil fertility index map was developed using the Arc GIS software. In spatial variability analysis SOC and micronutrients (Fe, Cu, Mn, Zn and B) content were higher in NE site of Tangi Choudwar block. The SFI value was range between 0.21 to 0.62 with higher SFI in reported at NE site. Person’s correlation of rice grain yield showed a positively correlation with SFI (0.44, P<0.05). These results showed that soil fertility index can be used in precision agriculture during crop planning and restoration of soil fertility of a region.