Shifts of growing‐season precipitation peaks decrease soil respiration in a semiarid grassland

2017 ◽  
Vol 24 (3) ◽  
pp. 1001-1011 ◽  
Author(s):  
Jingyi Ru ◽  
Yaqiong Zhou ◽  
Dafeng Hui ◽  
Mengmei Zheng ◽  
Shiqiang Wan
Keyword(s):  
Soil Respiration ◽  
Growing Season ◽  
Semiarid Grassland ◽  
Growing Season Precipitation

Pattern analysis of growing season precipitation in Southern Canada

1987 ◽  
Vol 25 (2) ◽  
pp. 137-158 ◽  
Author(s):  
Michael B. Richman ◽  
Peter J. Lamb
Keyword(s):  
Pattern Analysis ◽  
Growing Season ◽  
Growing Season Precipitation

Ecological responses of Stipa steppe in Inner Mongolia to experimentally increased temperature and precipitation. 3. Soil respiration

2018 ◽  
Vol 40 (2) ◽  
pp. 153 ◽  
Author(s):  
Xuexia Wang ◽  
Yali Chen ◽  
Yulong Yan ◽  
Zhiqiang Wan ◽  
Ran Chao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Respiration Rate ◽  
Inner Mongolia ◽  
Growing Season ◽  
Climatic Warming ◽  
Soil Respiration Rate ◽  
Natural Rainfall ◽  
Temperature And Precipitation

The response of soil respiration to simulated climatic warming and increased precipitation was evaluated on the arid–semi-arid Stipa steppe of Inner Mongolia. Soil respiration rate had a single peak during the growing season, reaching a maximum in July under all treatments. Soil temperature, soil moisture and their interaction influenced the soil respiration rate. Relative to the control, warming alone reduced the soil respiration rate by 15.6 ± 7.0%, whereas increased precipitation alone increased the soil respiration rate by 52.6 ± 42.1%. The combination of warming and increased precipitation increased the soil respiration rate by 22.4 ± 11.2%. When temperature was increased, soil respiration rate was more sensitive to soil moisture than to soil temperature, although the reverse applied when precipitation was increased. Under the experimental precipitation (20% above natural rainfall) applied in the experiment, soil moisture was the primary factor limiting soil respiration, but soil temperature may become limiting under higher soil moisture levels.

scholarly journals Identifying maize yield and precipitation gaps in Uganda

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Terence Epule Epule ◽  
Driss Dhiba ◽  
Daniel Etongo ◽  
Changhui Peng ◽  
Laurent Lepage
Keyword(s):  
Cropping Systems ◽  
Growing Season ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
World Bank Group ◽  
Precipitation Data ◽  
Yield Data ◽  
Yield Gaps ◽  
Maize Yields ◽  
Growing Season Precipitation

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

Heavy rainfall in peak growing season had larger effects on soil nitrogen flux and pool than in the late season in a semiarid grassland

2022 ◽  
Vol 326 ◽  
pp. 107785
Author(s):  
Linfeng Li ◽  
Yanbin Hao ◽  
Zhenzhen Zheng ◽  
Weijin Wang ◽  
Joel A. Biederman ◽  
...  
Keyword(s):  
Soil Nitrogen ◽  
Heavy Rainfall ◽  
Growing Season ◽  
Nitrogen Flux ◽  
Semiarid Grassland ◽  
Late Season

Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation

Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 652-664 ◽  
Author(s):  
N. C. Wagner ◽  
B. D. Maxwell ◽  
M. L. Taper ◽  
L. J. Rew
Keyword(s):  
Field Data ◽  
Wheat Yield ◽  
Growing Season ◽  
Predictor Variables ◽  
Data Sets ◽  
Wild Oat ◽  
Best Fitting ◽  
Weed Infestation ◽  
Growing Season Precipitation ◽  
Better Than

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.

scholarly journals Assessing the impact of climate variability on catchment water balance and vegetation cover

2011 ◽  
Vol 8 (3) ◽  
pp. 6291-6329 ◽  
Author(s):  
X. Xu ◽  
D. Yang ◽  
M. Sivapalan
Keyword(s):  
Statistical Analysis ◽  
Water Balance ◽  
Climate Variability ◽  
Vegetation Cover ◽  
Growing Season ◽  
Woody Vegetation ◽  
Elasticity Analysis ◽  
Total Runoff ◽  
Subsurface Runoff ◽  
Growing Season Precipitation

Abstract. Understanding the interactions among climate, vegetation cover and the water cycle lies at the heart of the study of watershed ecohydrology. Recently, considerable attention is being paid to the effect of climate variability (e.g., precipitation and temperature) on catchment water balance and also associated vegetation cover. In this paper, we investigate the general pattern of long-term water balance and vegetation cover (as reflected in fPAR) among 193 study catchments in Australia through statistical analysis. We then employ the elasticity analysis approach for quantifying the effects of climate variability on hydrologic partitioning (including total runoff, surface and subsurface runoff) and on vegetation cover (including total, woody and non-woody vegetation cover). Based on the results of statistical analysis, we conclude that annual runoff (R), evapotranspiration (E) and runoff coefficient (R/P) all increase with vegetation cover for catchments in which woody vegetation is dominant and annual precipitation is relatively high. Annual evapotranspiration (E) is mainly controlled by water availability rather than energy availability for catchments in relatively dry climates in which non-woody vegetation is dominant. The ratio of subsurface runoff to total runoff (Rg/R) also increases with woody vegetation cover. Through the elasticity analysis of catchment runoff, it is shown that precipitation (P) in the current year is the most important factor affecting the change in annual total runoff (R), surface runoff (Rs) and subsurface runoff (Rg). The significance of other controlling factors is in the order of the annual precipitation in the previous year (P−1 and P−2), which represent the net effect of soil moisture, and the annual mean temperature (T) in the current year. Change of P by +1 % causes a +3.35 % change of R, a +3.47 % change of Rs and a +2.89 % change of Rg, on average. Likewise a change of temperature of +1° causes a −0.05 % change of R, a −0.07 % change of Rs and a −0.10 % change of Rg, on average. Results of elasticity analysis on the maximum monthly vegetation cover indicate that incoming shortwave radiation during the growing season (Rsd,grow) is the most important factor affecting the change in vegetation cover. Change of Rsd,grow by +1 % produces a −1.08 % change of total vegetation cover (Ft) on average. The significance of other causative factors is in the order of the precipitation during growing season, mean temperature during growing season and precipitation during non-growing season. The growing season precipitation is more significant than the non-growing season precipitation to non-woody vegetation cover, but the both have equivalent effects to woody vegetation cover.

scholarly journals Modelling of Soil Respiration in Hungary

2006 ◽  
Vol 55 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Ferenc Ács ◽  
H. Breuer
Keyword(s):  
Soil Respiration ◽  
Water Balance ◽  
Air Temperature ◽  
Sandy Loam ◽  
Growing Season ◽  
Biogeochemical Model ◽  
Clay Loam ◽  
Water Balance Components ◽  
Linear Relationships ◽  
Time Periods

The climatology of soil respiration in Hungary is presented. Soil respiration is estimated by a Thornthwaite-based biogeochemical model using soil hydrophysical data and climatological fields of precipitation and air temperature. Soil respiration fields are analyzed for different soil textures (sand, sandy loam, loam, clay loam and clay) and time periods (year, growing season and months).  Strong linear relationships were found between soil respiration and the actual evapotranspiration for annual and growing season time periods. In winter months soil respiration is well correlated with air temperature, while in summer months there is a quite variable relationship with water balance components. The strength of linear relationship between soil respiration and climatic variables is much better for coarser than for finer soil texture.

scholarly journals Forest Soil Respirations are More Sensitive to Nighttime Temperature Change in Eastern China

2018 ◽  
Vol 47 (1) ◽  
pp. 249-254
Author(s):  
Zhaoyong SHI ◽  
Ke LI ◽  
Yongming WANG ◽  
Bede S. MICKAN ◽  
Weikang YUAN ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Soil Temperature ◽  
Temperature Change ◽  
Global Climate ◽  
Eastern China ◽  
Growing Season ◽  
Mean Value ◽  
Apparent Temperature ◽  
Effect Of Temperature ◽  
The Difference

Soil respiration is one of the main fluxes in the global carbon cycle. The effect of temperature on soil respiration is well understood. The response of soil respiration to temperature warming is called apparent temperature sensitivity (Q10) of soil respiration, which is an important parameter in modeling soil CO2 effluxes under global climate warming. The difference of Q10 between daytime and nighttime was hardly reported although attentions are attracted by the differences of temperature change and its effects on vegetation productivity. In this study, we investigated the Q10 of soil respiration in daytime and nighttime by modeling empirical functions based on the in situ measurement of soil respiration and temperature in temperate and subtropical forests of eastern China. Our results showed that the Q10 of soil respiration is higher in nighttime with the mean value of 2.74 and 2.35 than daytime with the average of 2.49 and 2.18 in all measured months and growing season, respectively. Moreover, the explanatory rate of soil temperature to soil respiration in nighttime is also higher than in daytime in each site in both all measured and growing seasons. The Q10 and explanatory rate of soil temperature to soil respiration in nighttime is 1.08 and 1.15 times in daytime in growing season. These findings indicate that soil respiration has a bigger sensitivity to temperature in nighttime than daytime. The change of soil temperature explains more variation of soil respiration in nighttime than daytime.

The intra- and inter-annual responses of soil respiration to climate extremes in a semiarid grassland

Geoderma ◽  
2020 ◽  
Vol 378 ◽  
pp. 114629
Author(s):  
Linfeng Li ◽  
Ruyan Qian ◽  
Weijin Wang ◽  
Xiaoming Kang ◽  
Qinwei Ran ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Climate Extremes ◽  
Semiarid Grassland
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