Can meteorological data and normalized difference vegetation index be used to quantify soil pH in grasslands?

Dai, Erfu; Zhang, Guangyu; Fu, Gang; Zha, Xinjie

doi:10.3389/fevo.2023.1206581

Cited by 4 publications

(28 citation statements)

References 39 publications

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“…Earlier research introduced the data acquisition (e.g., NDVI max : growing season maximum normalized difference vegetation index; AT: annual temperature; AP: annual precipitation; ARad: annual radiation) in detail [ 8 , 15 , 56 ]. Kjeldahl, Soxhlet extraction, complete combustion, Van Soest and anthrone-based methods were used to analyze crude protein (CP), ether extract (EE), crude ash (Ash), neutral detergent fiber (NDF)/acid detergent fiber (ADF), and water-soluble carbohydrate (WSC) [ 8 ].…”

Section: Methodsmentioning

confidence: 99%

Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

2023

Plants

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Uncertainties about the impacts of anthropogenic activities and climate change on forage nutrition storage of grasslands can limit the adaptive management of grasslands across the whole Tibetan Plateau. The main objective was to investigate the impacts of anthropogenic activities and climate change on the forage nutrition storage of grasslands on the Tibetan Plateau. Based on random forest models, we quantified the responses of forage nutrition storage to anthropogenic activities and climate change across the whole Tibetan grasslands from 2000 to 2020. Warming and increased precipitation did not always increase forage nutrition storage, and cooling and decreased precipitation did not always reduce forage nutrition storage. Compared to temperature and precipitation changes, radiation change had stronger contributions to potential and actual forage nutrition storage. Humankind's activities altered the impacts of climate change on forage nutrition storage. The impacts of anthropogenic activities on forage nutrition storage increased linearly with increasing mean annual temperature and decreasing elevation but showed quadratic relationships with longitude, mean annual precipitation and radiation. The change in the impacts of humankind’s activities on forage nutrition storage was more closely related to radiation change than temperature and precipitation changes. The findings observed by this study caution that the impacts of radiation change on forage nutrition forage should be taken seriously under global change. Both climate change and humankind activities cannot always increase forage nutrition storage but may cause the degradation of forage nutrition storage.

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Section: Methodsmentioning

confidence: 99%

Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

2023

Plants

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“…In contrast, monthly air temperature, precipitation, radiation and normalized difference vegetation index data were used to estimate soil NH 4 + -N, NO 3 − -N and AP under free-grazing conditions. According to previous studies [24,25], the sample function of R 4.2.2 software was used to separate the original data (soil NH 4 + -N, NO 3 − -N or AP, and their corresponding independent variables) into two groups. One group (n = 30) was used for model accuracy test, and the rest was used for model construction.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…However, the extent to which the climate trivariate (temperature, precipitation and radiation) and NDVI can col-lectively explain variations in soil available nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau, compared to the climate trivariate alone, remains unknown due to a lack of relevant studies. Third, while previous research confirms that random forest algorithm is more accurate than other algorithms in quantifying pasture nutrient quality and nutrient pool, plant diversity, soil moisture, soil pH, soil carbon, nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau [22][23][24][25][26], it is uncertain whether the random forest algorithm also exhibits superior performance compared to other models in quantifying variations in soil-available nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau.…”

Section: Introductionmentioning

confidence: 95%

“…The nine algorithms employed in this study were the RFA, generalized boosted regression algorithm (GBRA), support vector machine algorithm (SVA), multiple linear regression algorithm (MLRA), recursive regression tree algorithm (RRTA), artificial neural network algorithm (ANNA), generalized linear regression algorithm (GLRA), conditional inference tree algorithm (CITA) and eXtreme gradient boosting algorithm (eXGBA). According to some previous studies [22][23][24][25][26], we hypothesized that the performances of RFA were stronger than the other eight algorithms in estimating soil NH 4 + -N, NO 3 − -N and AP.…”

Section: Introductionmentioning

confidence: 99%

“…These studies have explored the feedback effects on grassland ecosystem productivity and forage nutrition quality [19][20][21]. Radiation, as a significant climate variable, has been demonstrated to markedly influence the nutritional quality of herbage, herbage stock, plant diversity, soil moisture, soil pH, soil carbon, nitrogen and phosphorus in the Qinghai-Xizang grassland ecosystems [22][23][24][25][26]. The combined influence of temperature, precipitation and radiation appears to explain the productivity and diversity of alpine grassland more than the individual factors of temperature and precipitation [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Modelling Soil Ammonium Nitrogen, Nitrate Nitrogen and Available Phosphorus Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

Sun,

Qi,

et al. 2024

Sustainability

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There is still a lack of high-precision and large-scale soil ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N) and available phosphorus (AP) in alpine grasslands at least on the Qinghai–Xizang Plateau, which may limit our understanding of the sustainability of alpine grassland ecosystems (e.g., changes in soil NH4+-N, NO3−-N and AP can affect the sustainability of grassland productivity, which in turn may alter the sustainability of livestock development), given that nitrogen and phosphorus are important limiting factors in alpine regions. The construction of big data mining models is the key to solving the problem mentioned above. Therefore, observed soil NH4+-N, NO3−-N and AP at 0–10 cm and 10–20 cm, climate data (air temperature, precipitation and radiation) and/or normalized vegetation index (NDVI) data were used to model NH4+-N, NO3−-N and AP in alpine grasslands of Xizang under fencing and grazing conditions. Nine algorithms, including random forest algorithm (RFA), generalized boosted regression algorithm (GBRA), multiple linear regression algorithm (MLRA), support vector machine algorithm (SVMA), recursive regression tree algorithm (RRTA), artificial neural network algorithm (ANNA), generalized linear regression algorithm (GLMA), conditional inference tree algorithm (CITA), and eXtreme gradient boosting algorithm (eXGBA), were used. The RFA had the best performance among the nine algorithms. Climate data based on the RFA can explain 78–92% variation of NH4+-N, NO3−-N and AP under fencing conditions. Climate data and NDVI together can explain 83–93% variation of NH4+-N, NO3−-N and AP under grazing conditions based on the RFA. The absolute values of relative bias, linear slopes, R2 and RMSE values between simulated soil NH4+-N, NO3−-N and AP based on RFA were ≤8.65%, ≥0.90, ≥0.91 and ≤3.37 mg kg−1, respectively. Therefore, random forest algorithm can be used to model soil available nitrogen and phosphorus based on observed climate data and/or normalized difference vegetation index in Xizang’s grasslands. The random forest models constructed in this study can be used to obtain a long-term (e.g., 2000–2020) raster dataset of soil available nitrogen and phosphorus in alpine grasslands on the whole Qinghai–Tibet Plateau. The raster dataset can explain changes in grassland productivity from the perspective of nitrogen and phosphorus constraints across the Tibetan grasslands, which can provide an important basis for the sustainable development of grassland ecosystem itself and animal husbandry on the Tibetan Plateau.

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Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Wang,

Qi,

et al. 2024

Front. Earth Sci.

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Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

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Can meteorological data and normalized difference vegetation index be used to quantify soil pH in grasslands?

Cited by 4 publications

References 39 publications

Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

Modelling Soil Ammonium Nitrogen, Nitrate Nitrogen and Available Phosphorus Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

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