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

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.

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.

Embryo dune development drivers: beach morphology, growing season precipitation, and storms

2017 ◽  
Vol 42 (11) ◽  
pp. 1733-1744 ◽  
Author(s):  
Marinka E.B. van Puijenbroek ◽  
Juul Limpens ◽  
Alma V. de Groot ◽  
Michel J.P.M. Riksen ◽  
Maurits Gleichman ◽  
...  
Keyword(s):  
Growing Season ◽  
Beach Morphology ◽  
Growing Season Precipitation

scholarly journals Distinct Climate Effects on Dahurian Larch Growth at an Asian Temperate-Boreal Forest Ecotone and Nearby Boreal Sites

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Enzai Du ◽  
Yang Tang
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Temperate Forest ◽  
Growing Season ◽  
Maximum Temperature ◽  
Dahurian Larch ◽  
Growth Responses ◽  
Significant Difference ◽  
Growing Season Precipitation ◽  
Forest Ecotone

Climate change is exerting profound impacts on the structure and function of global boreal forest. Compared with their northern counterparts, trees growing at the southern boreal forest and the temperate-boreal forest ecotone likely show distinct responses to climate change. Based on annual basal areal increment (BAI) of Dahurian larch (Larix gmelinii Rupr.) plantations with similar ages, tree densities and soil nutrient conditions, we investigated the tree growth responses to inter-annual climate variations at an Asian temperate-boreal forest ecotone and nearby boreal sites in northeast China. Annual BAI changed nonlinearly with cambial age in the form of a lognormal curve. The maximum annual BAI showed no significant difference between the two bioregions, while annual BAI peaked at an elder age at the boreal-temperate forest ecotone. After eliminating the age associated trend, conditional regression analyses indicate that residual BAI at the boreal sites increased significantly with higher growing-season mean nighttime minimum temperature and non-growing-season precipitation, but decreased significantly with higher growing-season mean daytime maximum temperature during the past three decades (1985–2015). In contrast, residual BAI at the boreal-temperate forest ecotone only showed a positive and weak response to inter-annual variations of growing-season precipitation. These findings suggest distinct effects of inter-annual climate variation on the growth of boreal trees at the temperate-boreal forest ecotone in comparison to the southern boreal regions, and highlight future efforts to elucidate the key factors that regulate the growth ofthe southernmost boreal trees.

REGRESSIONS FOR ESTIMATING STRAW YIELDS AND N AND P CONTENTS OF SPRING WHEAT AND N MINERALIZATION IN A BROWN LOAM SOIL

1988 ◽  
Vol 68 (2) ◽  
pp. 337-344 ◽  
Author(s):  
C. A. CAMPBELL ◽  
R. P. ZENTNER ◽  
F. SELLES
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Spring Wheat ◽  
N Mineralization ◽  
Soil Moisture Content ◽  
N Uptake ◽  
Growing Season ◽  
Net N Mineralization ◽  
Loam Soil ◽  
Growing Season Precipitation

Data from an 18-yr crop rotation study carried out on a Brown loam soil at Swift Current, Saskatchewan, were used to estimate equations that relate spring wheat straw yields, and N and P content of grain and straw to moisture use (MU). Moisture use was defined as soil moisture content in 0- to 120-cm depth at seeding, less soil moisture content at harvest, plus growing season precipitation. Grain yields were also related to straw yields and to N content of the straw. Potential net N mineralization (Nmin) in summerfallow (periods during the growing season with negative Nmin omitted) was related (r = 0.74**) to precipitation received during the spring to fall period. An attempt to relate apparent net Nmin (determined by N balance) in cropped systems to growing season precipitation or to MU was not successful. Highly significant linear regressions were obtained for straw yields, grain N and P contents vs. MU, and for grain yield vs. straw yield (r = 0.66** – 0.83**), but the other relationships were less reliable (r = 0.41** – 0.55**) though still significant. We discussed how these relationships might be used to estimate fertilizer N requirements, for examining N immobilization-mineralization, and for estimating residue sufficiency for erosion control on summerfallowed land. Key words: Straw:grain ratio, N uptake, P uptake, crop residues, N mineralization

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