The effects of carbon， nitrogen， and potassium addition on the decomposition characteristics of litter in desert grasslands

doi:10.11686/cyxb2024126

Abstract

Abstract:

Decomposition of litter is an important component process of energy flow and material cycling in grassland ecosystems. However it is not clear how litter decomposition in desert grasslands responds to the addition of carbon （C）， nitrogen （N）， and potassium （K）. Clarification of this relationship is needed to improve our understanding of the nutrient cycling process in desert grasslands. In this study， the mesh bag decomposition method was used in a randomized block design， to study the effects of exogenous C， N， and K addition on the litter decomposition characteristics of the dominant species Stipa breviflora and Leymus secalinus in Ningxia desert grassland. Litter decomposition was tested in soils fertilized with one of three rates of C （vermicompost， 30% C）， N （urea， 46% N） or K （KCl， 60% K） as follows： C₁（0 g·m^-2·yr^-1）， C₂ （52.54 g·m^-2·yr^-1） and C₃（705.09 g·m^-2·yr^-1） set at the experimental site； along with nutrient addition treatments. N₁ （0 g·m^-2·yr^-1）， N₂ （5 g·m^-2·yr^-1） and N₃ （20 g·m^-2·yr^-1）， three C， N， and K concentration gradients， K₁ （0 g·m^-2·yr^-1）， K₂ （9.0 g·m^-2·yr^-1） and K₃ （18.0 g·m^-2·yr^-1）. It was found that significant differences in the effects of C， N， and K addition on litter decomposition and different decomposition rates of litter from S. breviflora and L. secalinus （P<0.05）. Half-lives for L. secalinus decay ranged from 4.93 to 17.54 months and for S. breviflora from 10.76 to 20.51 months. The decomposition rates of litter from S. breviflora and L. secalinus began to slow down at 120 and 240 days， respectively. Leaves of the two grass species displayed different responses to the addition of C， N， and K nutrients. Under the K₃ and K₃+N₃+C₂ treatments， the decomposition rate of L. secalinus litter was faster， while under the CK and K₂+N₂ treatments， the decomposition rate of S. breviflora litter was faster. According to the exponential decay model， the predicted decomposition rate for L. secalinus litter is greater than for S. breviflora litter. The litter of both L. secalinus and S. breviflora decomposed quickly in autumn. In summary， there were differences in litter decomposition rates of the two grass species in the same habitat， and high additions of K and N and low additions of C promoted litter decomposition rates more strongly than other nutrient addition combinations.

Key words: desert grassland, litter, decompose features, nutrient addition

Xin-yi LUO, Kai-yang QIU, Tao JIN, Ping-an BAO, Ye-yun HUANG, Yi HE, Ying-zhong XIE. The effects of carbon， nitrogen， and potassium addition on the decomposition characteristics of litter in desert grasslands[J]. Acta Prataculturae Sinica, 2025, 34(2): 41-53.

Figures/Tables 8

References 59

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处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）	处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）
K₁+N₁+C₁	y=0.94710e^-0.00216t	0.876	9.85	45.39	K₂+N₂+C₃	y=0.91837e^-0.00191t	0.821	10.61	50.80
K₁+N₁+C₂	y=0.92219e^-0.00135t	0.802	15.12	71.97	K₂+N₃+C₁	y=0.94643e^-0.00197t	0.845	10.80	49.76
K₁+N₁+C₃	y=0.93659e^-0.00210t	0.882	9.96	46.51	K₂+N₃+C₂	y=0.92422e^-0.00197t	0.855	10.40	49.36
K₁+N₂+C₁	y=0.98703e^-0.00207t	0.910	10.95	48.03	K₂+N₃+C₃	y=0.96869e^-0.00181t	0.856	12.18	54.58
K₁+N₂+C₂	y=0.92993e^-0.00191t	0.792	10.83	51.01	K₃+N₁+C₁	y=0.94194e^-0.00219t	0.874	9.64	44.69
K₁+N₂+C₃	y=0.90056e^-0.00177t	0.881	11.08	54.44	K₃+N₁+C₂	y=0.91022e^-0.00206t	0.818	9.69	46.95
K₁+N₃+C₁	y=0.91858e^-0.00116t	0.701	17.54	83.93	K₃+N₁+C₃	y=0.92522e^-0.00416t	0.847	4.93	23.38
K₁+N₃+C₂	y=0.92294e^-0.00179t	0.882	11.41	54.29	K₃+N₂+C₁	y=0.97824e^-0.00200t	0.896	11.19	49.56
K₁+N₃+C₃	y=0.93670e^-0.00166t	0.780	12.61	58.84	K₃+N₂+C₂	y=0.93927e^-0.00206t	0.846	10.20	47.46
K₂+N₁+C₁	y=0.94759e^-0.00199t	0.916	10.71	49.28	K₃+N₂+C₃	y=0.96153e^-0.00217t	0.833	10.04	45.41
K₂+N₁+C₂	y=0.92587e^-0.00216t	0.808	9.51	45.04	K₃+N₃+C₁	y=0.99318e^-0.00191t	0.699	11.98	52.16
K₂+N₁+C₃	y=0.87007e^-0.00163t	0.718	11.33	58.42	K₃+N₃+C₂	y=1.00549e^-0.00220t	0.919	10.59	45.47
K₂+N₂+C₁	y=0.96601e^-0.00202t	0.891	10.87	48.86	K₃+N₃+C₃	y=0.91724e^-0.00166t	0.781	12.18	58.42
K₂+N₂+C₂	y=0.93898e^-0.00202t	0.850	10.40	48.40

处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）	处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）
K₁+N₁+C₁	y=0.94710e^-0.00216t	0.876	9.85	45.39	K₂+N₂+C₃	y=0.91837e^-0.00191t	0.821	10.61	50.80
K₁+N₁+C₂	y=0.92219e^-0.00135t	0.802	15.12	71.97	K₂+N₃+C₁	y=0.94643e^-0.00197t	0.845	10.80	49.76
K₁+N₁+C₃	y=0.93659e^-0.00210t	0.882	9.96	46.51	K₂+N₃+C₂	y=0.92422e^-0.00197t	0.855	10.40	49.36
K₁+N₂+C₁	y=0.98703e^-0.00207t	0.910	10.95	48.03	K₂+N₃+C₃	y=0.96869e^-0.00181t	0.856	12.18	54.58
K₁+N₂+C₂	y=0.92993e^-0.00191t	0.792	10.83	51.01	K₃+N₁+C₁	y=0.94194e^-0.00219t	0.874	9.64	44.69
K₁+N₂+C₃	y=0.90056e^-0.00177t	0.881	11.08	54.44	K₃+N₁+C₂	y=0.91022e^-0.00206t	0.818	9.69	46.95
K₁+N₃+C₁	y=0.91858e^-0.00116t	0.701	17.54	83.93	K₃+N₁+C₃	y=0.92522e^-0.00416t	0.847	4.93	23.38
K₁+N₃+C₂	y=0.92294e^-0.00179t	0.882	11.41	54.29	K₃+N₂+C₁	y=0.97824e^-0.00200t	0.896	11.19	49.56
K₁+N₃+C₃	y=0.93670e^-0.00166t	0.780	12.61	58.84	K₃+N₂+C₂	y=0.93927e^-0.00206t	0.846	10.20	47.46
K₂+N₁+C₁	y=0.94759e^-0.00199t	0.916	10.71	49.28	K₃+N₂+C₃	y=0.96153e^-0.00217t	0.833	10.04	45.41
K₂+N₁+C₂	y=0.92587e^-0.00216t	0.808	9.51	45.04	K₃+N₃+C₁	y=0.99318e^-0.00191t	0.699	11.98	52.16
K₂+N₁+C₃	y=0.87007e^-0.00163t	0.718	11.33	58.42	K₃+N₃+C₂	y=1.00549e^-0.00220t	0.919	10.59	45.47
K₂+N₂+C₁	y=0.96601e^-0.00202t	0.891	10.87	48.86	K₃+N₃+C₃	y=0.91724e^-0.00166t	0.781	12.18	58.42
K₂+N₂+C₂	y=0.93898e^-0.00202t	0.850	10.40	48.40

处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）	处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）
K₁+N₁+C₁	y=0.94715e^-0.00198t	0.876	10.76	49.52	K₂+N₂+C₃	y=0.95998e^-0.00149t	0.670	14.59	66.11
K₁+N₁+C₂	y=0.94188e^-0.00135t	0.711	15.64	72.49	K₂+N₃+C₁	y=0.92832e^-0.00103t	0.835	20.03	94.54
K₁+N₁+C₃	y=0.95050e^-0.00136t	0.850	15.78	72.18	K₂+N₃+C₂	y=0.93147e^-0.00132t	0.695	15.71	73.86
K₁+N₂+C₁	y=0.91925e^-0.00126t	0.802	16.11	77.02	K₂+N₃+C₃	y=0.92818e^-0.00140t	0.738	14.73	69.55
K₁+N₂+C₂	y=0.91790e^-0.00120t	0.740	16.87	80.84	K₃+N₁+C₁	y=0.93901e^-0.00120t	0.808	17.51	81.47
K₁+N₂+C₃	y=0.91020e^-0.00119t	0.623	16.78	81.28	K₃+N₁+C₂	y=0.91293e^-0.00148t	0.666	13.56	65.42
K₁+N₃+C₁	y=0.89790e^-0.00110t	0.724	17.74	87.52	K₃+N₁+C₃	y=0.91028e^-0.00131t	0.672	15.25	73.84
K₁+N₃+C₂	y=0.94508e^-0.00139t	0.870	15.27	70.49	K₃+N₂+C₁	y=0.92698e^-0.00110t	0.795	18.71	88.48
K₁+N₃+C₃	y=0.90856e^-0.00114t	0.595	17.46	84.79	K₃+N₂+C₂	y=0.92766e^-0.00113t	0.826	18.23	86.15
K₂+N₁+C₁	y=0.92441e^-0.00110t	0.711	18.62	88.40	K₃+N₂+C₃	y=0.93520e^-0.00121t	0.773	17.25	80.68
K₂+N₁+C₂	y=0.91706e^-0.00128t	0.725	15.80	75.76	K₃+N₃+C₁	y=0.96269e^-0.00121t	0.613	18.05	81.48
K₂+N₁+C₃	y=0.92413e^-0.00132t	0.822	15.51	73.66	K₃+N₃+C₂	y=0.92374e^-0.00119t	0.759	17.19	81.69
K₂+N₂+C₁	y=0.89925e^-0.00095t	0.741	20.51	101.06	K₃+N₃+C₃	y=0.93029e^-0.00107t	0.773	19.34	91.07
K₂+N₂+C₂	y=0.93599e^-0.00128t	0.735	16.33	76.29

处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）	处理 Treatment	分解模型 Decomposition model	R²	T_0.5（Month）	T_0.95（Month）
K₁+N₁+C₁	y=0.94715e^-0.00198t	0.876	10.76	49.52	K₂+N₂+C₃	y=0.95998e^-0.00149t	0.670	14.59	66.11
K₁+N₁+C₂	y=0.94188e^-0.00135t	0.711	15.64	72.49	K₂+N₃+C₁	y=0.92832e^-0.00103t	0.835	20.03	94.54
K₁+N₁+C₃	y=0.95050e^-0.00136t	0.850	15.78	72.18	K₂+N₃+C₂	y=0.93147e^-0.00132t	0.695	15.71	73.86
K₁+N₂+C₁	y=0.91925e^-0.00126t	0.802	16.11	77.02	K₂+N₃+C₃	y=0.92818e^-0.00140t	0.738	14.73	69.55
K₁+N₂+C₂	y=0.91790e^-0.00120t	0.740	16.87	80.84	K₃+N₁+C₁	y=0.93901e^-0.00120t	0.808	17.51	81.47
K₁+N₂+C₃	y=0.91020e^-0.00119t	0.623	16.78	81.28	K₃+N₁+C₂	y=0.91293e^-0.00148t	0.666	13.56	65.42
K₁+N₃+C₁	y=0.89790e^-0.00110t	0.724	17.74	87.52	K₃+N₁+C₃	y=0.91028e^-0.00131t	0.672	15.25	73.84
K₁+N₃+C₂	y=0.94508e^-0.00139t	0.870	15.27	70.49	K₃+N₂+C₁	y=0.92698e^-0.00110t	0.795	18.71	88.48
K₁+N₃+C₃	y=0.90856e^-0.00114t	0.595	17.46	84.79	K₃+N₂+C₂	y=0.92766e^-0.00113t	0.826	18.23	86.15
K₂+N₁+C₁	y=0.92441e^-0.00110t	0.711	18.62	88.40	K₃+N₂+C₃	y=0.93520e^-0.00121t	0.773	17.25	80.68
K₂+N₁+C₂	y=0.91706e^-0.00128t	0.725	15.80	75.76	K₃+N₃+C₁	y=0.96269e^-0.00121t	0.613	18.05	81.48
K₂+N₁+C₃	y=0.92413e^-0.00132t	0.822	15.51	73.66	K₃+N₃+C₂	y=0.92374e^-0.00119t	0.759	17.19	81.69
K₂+N₂+C₁	y=0.89925e^-0.00095t	0.741	20.51	101.06	K₃+N₃+C₃	y=0.93029e^-0.00107t	0.773	19.34	91.07
K₂+N₂+C₂	y=0.93599e^-0.00128t	0.735	16.33	76.29

影响因子Influence factor	F	P
钾添加K addition （K）	0.300	0.741
氮添加N addition （N）	12.630	<0.001**
碳添加C addition （C）	7.348	<0.001**
时间Time （T）	1853.805	<0.001**
物种Species （S）	378.788	<0.001**
钾添加×氮添加K×N	1.356	0.249
钾添加×碳添加K×C	0.474	0.755
钾添加×时间K×T	0.435	0.856
钾添加×物种K×S	1.565	0.210
钾添加×氮添加×碳添加K×N×C	0.141	0.997
钾添加×氮添加×时间K×N×T	0.780	0.671
钾添加×氮添加×物种K×N×S	0.726	0.575
钾添加×氮添加×碳添加×时间K×N×C×T	0.824	0.757
钾添加×氮添加×碳添加×物种K×N×C×S	1.278	0.228
钾添加×氮添加×碳添加×时间×物种 K×N×C×T×S	1.029	0.423
氮添加×碳添加N×C	0.912	0.457
氮添加×时间N×T	3.569	0.002**
氮添加×物种N×S	2.249	0.107
氮添加×碳添加×时间N×C×T	0.623	0.823
氮添加×碳添加×物种N×C×S	1.173	0.322
氮添加×碳添加×时间×物种N×C×T×S	0.855	0.634
碳添加×时间C×T	3.080	0.006**
碳添加×物种C×S	0.707	0.494
碳添加×时间×物种C×T×S	0.931	0.472
时间×物种T×S	43.677	<0.001**