Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan

2017 ◽  
Vol 56 (4) ◽  
pp. 897-913 ◽  
Author(s):  
Ting Meng ◽  
Richard Carew ◽  
Wojciech J. Florkowski ◽  
Anna M. Klepacka
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Climate Model ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
Weather Data ◽  
Growth Stages ◽  
Yield Distribution ◽  
The Impact

AbstractThe IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.

scholarly journals Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

2021 ◽  
Vol 13 (12) ◽  
pp. 2249
Author(s):  
Sadia Alam Shammi ◽  
Qingmin Meng
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Negative Impact ◽  
Positive Impact ◽  
Information Criterion ◽  
Growing Season ◽  
Maximum Temperature ◽  
Linear Regression Models ◽  
The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

scholarly journals Reinspecting the Climate-Crop Yields Relationship at a Finer Scale and the Climate Damage Evaluation: Evidence from China

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yongbin Zhu ◽  
Yajuan Shi ◽  
Changxin Liu ◽  
Bing Lyu ◽  
Zhenbo Wang
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Spring Wheat ◽  
Crop Yields ◽  
Growth Stages ◽  
Climate Variables ◽  
Negative Effects ◽  
Summer Maize ◽  
Climate Effects ◽  
Positive Effects

This paper reinvestigated the climate-crop yield relationship with the statistical model at crops’ growing stage scale. Compared to previous studies, our model introduced monthly climate variables in the production function of crops, which enables separating the yield changes induced by climate change and those caused by inputs variation and technique progress, as well as examining different climate effects during each growing stage of crops. By applying the fixed effect regression model with province-level panel data of crop yields, agricultural inputs, and the monthly climate variables of temperature and precipitation from 1985 to 2015, we found that the effects of temperature generally are negative and those of precipitation generally are positive, but they vary among different growth stages for each crop. Specifically, GDDs (i.e., growing degree days) have negative effects on spring maize’s yield except for the sowing and ripening stages; the effects of precipitation are negative in September for summer maize. Precipitation in December and the next April is significantly harmful to the yield of winter wheat; while, for the spring wheat, GDDs have positive effects during April and May, and precipitation has negative effects during the ripening period. In addition, we computed climate-induced losses based on the climate-crop yield relationship, which demonstrated a strong tendency for increasing yield losses for all crops, with large interannual fluctuations. Comparatively, the long-term climate effects on yields of spring maize, summer maize, and spring wheat are more noticeable than those of winter wheat.

scholarly journals Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool

2020 ◽  
Vol 12 (17) ◽  
pp. 2749
Author(s):  
Marta Aranguren ◽  
Ander Castellón ◽  
Ana Aizpurua
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
N Fertilization ◽  
Wheat Crop ◽  
Growth Stages ◽  
Climate Conditions ◽  
N Status

Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.

Combining agronomic practices and herbicides improves weed management in wheat–canola rotations within zero-tillage production systems

Weed Science ◽  
2005 ◽  
Vol 53 (4) ◽  
pp. 528-535 ◽  
Author(s):  
Robert E. Blackshaw ◽  
Hugh J. Beckie ◽  
Louis J. Molnar ◽  
Toby Entz ◽  
James R. Moyer
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Weed Management ◽  
Crop Yields ◽  
Wheat Yield ◽  
Integrated Weed Management ◽  
Weed Biomass ◽  
Weed Growth ◽  
Spring Canola ◽  
Crop Seed

Development of more comprehensive and cost-effective integrated weed management systems is required to facilitate greater integrated weed management adoption by farmers. A field experiment was conducted at two locations to determine the combined effects of seed date (April or May), seed rate (recommended or 150% of recommended), fertilizer timing (applied in fall or spring), and in-crop herbicide dose (50% or 100% of recommended) on weed growth and crop yield. This factorial set of treatments was applied in four consecutive years within a spring wheat–spring canola–spring wheat–spring canola rotation in a zero-till production system. Both wheat and canola phases of the rotation were grown each year. Weed biomass was often lower with May than with April seeding because more weeds were controlled with preplant glyphosate. However, despite fewer weeds being present with May seeding, wheat yield was only greater in 1 of 7 site-years, and canola yield was never greater with May compared with April seeding. Higher crop seed rates had a consistently positive effect on reducing weed growth and the weed seedbank. Crop yield was sometimes greater, and never lower, with higher seed rates. Fertilizer timing did not have a large effect on crop yield, but applying N in the spring compared with fall was less favorable for weeds as indicated by lower weed biomass and a 20% decrease in the weed seedbank. In-crop herbicides applied at 50% compared with 100% doses often resulted in similar weed biomass and crop yield, especially when higher crop seed rates were used. Indeed, the weed seedbank at the conclusion of the 4-yr experiment was not greater with the 50% compared with 100% herbicide dose at one of two locations. This study demonstrates the combined merits of early seeding (April), higher crop seed rates, and spring-applied fertilizer in conjunction with timely but limited herbicide use to manage weeds and maintain high crop yields in rotations containing wheat and canola.

scholarly journals The effect of soil and weather conditions on yields of winter wheat in multi-environmental trials

2019 ◽  
Vol 56 (2) ◽  
pp. 263-279 ◽  
Author(s):  
Marzena Iwańska ◽  
Michał Stępień
Keyword(s):  
Winter Wheat ◽  
Crop Yields ◽  
Wheat Yield ◽  
Weather Conditions ◽  
Grain Filling ◽  
Growth Stages ◽  
Yield Data ◽  
Winter Wheat Yield ◽  
Crop Growth Stage ◽  
The Impact

SummaryDrought reduces crop yields not only in areas of arid climate. The impact of droughts depends on the crop growth stage and soil properties. The frequency of droughts will increase due to climate change. It is important to determine the environmental variables that have the strongest effect on wheat yields in dry years. The effect of soil and weather on wheat yield was evaluated in 2018, which was considered a very dry year in Europe. The winter wheat yield data from 19 trial locations of the Research Center of Cultivar Testing (COBORU), Poland, were used. Soil data from the trial locations, mean air temperature (T) and precipitation (P) were considered as environmental factors, as well as the climatic water balance (CWB). The hydrothermal coefficient (HTC), which is based on P and T, was also used. The effect of these factors on winter wheat yield was related to the weather conditions at particular growth stages. The soil had a greater effect than the weather conditions. CWB, P, T and HTC showed a clear relationship with winter wheat yield. Soil data and HTC are the factors most recommended for models predicting crop yields. In the selection of drought-tolerant genotypes, the plants should be subjected to stress especially during the heading and grain filling growth stages.

Spring Wheat, Canola, and Sunflower Response to Persian Darnel (Lolium persicum) Interference

2004 ◽  
Vol 18 (3) ◽  
pp. 509-520 ◽  
Author(s):  
Johnathon D. Holman ◽  
Alvin J. Bussan ◽  
Bruce D. Maxwell ◽  
Perry R. Miller ◽  
James A. Mickelson
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Weed Management ◽  
Management Practices ◽  
Yield Loss ◽  
Wheat Yield ◽  
Integrated Weed Management ◽  
Crop Density ◽  
The Impact ◽  
Number Of Branches

Integrated weed management practices, such as crop rotation and increased seeding rates, potentially improve weed management. Yet, few studies compare competitive interactions of weeds with different crops. This research quantified the impact of Persian darnel on spring wheat, canola, and sunflower yield across different seeding rates. Increasing crop density increased yield when Persian darnel affected crop yield early in physiological development. Crop yield loss was estimated to reach 83, 70, and 57% for spring wheat, canola, and sunflower, respectively, at high Persian darnel densities. Persian darnel reduced spring wheat yield by limiting the number of tillers per plant and seed per tiller; reduced canola yield by limiting the number of branches per plant, pods per branch, and seed per pod; and reduced sunflower yield by limiting the number of seed per plant. Persian darnel affected crop growth early in physiological development, indicating that interspecific interference occurred early in the growing season. Cultural and resource management aimed at reducing Persian darnel impact on resource availability and crop yield components will reduce Persian darnel impact on crop yield.

scholarly journals THE IMPACT OF SOIL TILLAGE AND FORECROP ON AGRICULTURAL PHYSICAL PARAMETERS AND CROP YIELD OF SPRING WHEAT IN THE NORTHERN PART OF KAZAKHSTAN

2019 ◽  
pp. 26-33
Author(s):  
V. V. Zabolotskih ◽  
Ia. P. Nazdrachev ◽  
S. A. Zhurik
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Wheat Yield ◽  
Soil Layer ◽  
Black Soil ◽  
Crop Rotations ◽  
Soil Tillage ◽  
Physical Parameters ◽  
No Till ◽  
The Impact

The research was carried out on the carbonate black soil of Akmola region in two five-field crop rotations in 2010-2016. The scheme of the experiment included the following ways of soil tillage: deep flat-cutting DF-3-5 (at a depth of 25-27 cm), surface flat-cutting KPSSH-9 (at 10-12 cm), para ploughing SHR-4.5 (at 25-27 cm) and No-till . The researchers observed the highest moisture reserves in the soil layer of 0-100 cm for 7 years in the variants with deep flat-cutting tillage and para ploughing: 115.6 and 112.6 on steam, 108.5 and 105.4 mm on pea. The lowest amount of moisture (80.4 mm) was observed in No-till treatment. The density of the arable soil layer of the southern carbonate black soil before spring wheat sowing corresponded to the appropriate values. When experiencing mechanical treatment, the density of arable layer varied from 1.23 to 1.26 g/cm3; in No-till variant it was 1.31 g/cm3. The concentration of valuables in the variants with deep and surface mechanical tillage varied within 70.9-75.6%, when No-till method was applied, the parameter reduced to 64.5 - 61.5%. The yields of spring wheat sown on steam varied within 1.70 - 1.82 t/ha and did not depend on the soil tillage., Regardless the soil treatment, cultivation of wheat after peas reduced grain harvest on 0.12 - 0.27 t/ha in comparison with steam variant. The authors didn’t observe reliable differences in the crop yield between the first and second crops of grain-steamed crop rotation when applying mechanical soil tillage. In the No-till system there was a reliable decrease in wheat yield on 0.24 t/ha.

Spring wheat productivity forecasting in the forest steppe zone of Volga by least squares method

10.12737/2167 ◽  
2014 ◽  
Vol 8 (4) ◽  
pp. 92-98
Author(s):  
Аввакумов ◽  
Oleg Avvakumov
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Spring Wheat ◽  
Crop Yields ◽  
Moving Average ◽  
Wheat Yield ◽  
Steppe Zone ◽  
Soil Parameters ◽  
Forest Steppe ◽  
Phosphorus And Potassium

The efficient use of arable land is based on the prediction of crop yields. In extensive farming system the productivity forecasting was carried out by scores of soil fertility, where the level of soil fertility was associated with soil type, and crop yields depended on the leading basic and sustained properties - humus content, cation-exchange capacity, particle size distribution. The system of intensive farming is based on the use of mineral and organic fertilizers. The leading factor in the formation of crop yield is the soil security by mobile soil nutrients on the background of optimal soil parameters. Nowadays, crop yield forecasting is made with the use of the available to plants macro nutrition content. The article presents the results of spring wheat yield prognosis in the Laishevo municipal district with the use of MatLab (matrix operations). The matrix was made according to the data over the last 43 years, it’s moving averages with steps of 11 and 22 years, the content of mobile phosphorus and potassium, determined by the method of Kirsanov. The predictive ability is confirmed by correlation analysis, for the actual number of crop coefficients of correlation with phosphorus and potassium are 0.52 and 0.61, respectively, for the moving average yields are equal to 0.94 and 0.95. A comparison of the actual spring wheat yield (УФ) with the calculated data (the model 1 and the model 2) shows the average deviation of 30%. Similar calculations for the derived series of the moving average of crop yield for the step length of 11 years gives a marked decrease in the deviation of 5-6 %. This convergence of data with the calculated У11 (the model 1 and the model 2, in the left part of the table) indicates for the elimination of weather factor for У11, which influences the overall level of productivity of spring wheat in the forest-steppe zone. The conclusion of the article is the inclusion of agro-climatic conditions (precipitation and temperature) for further calculation of crop yields forecasting.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

scholarly journals The Pitfalls of Relating Weeds, Herbicide Use, and Crop Yield: Don't Fall Into the Trap! A Critical Review

2020 ◽  
Vol 2 ◽  
Author(s):  
Nathalie Colbach ◽  
Sandrine Petit ◽  
Bruno Chauvel ◽  
Violaine Deytieux ◽  
Martin Lechenet ◽  
...  
Keyword(s):  
Crop Yield ◽  
Weed Management ◽  
Crop Production ◽  
Yield Loss ◽  
Crop Yields ◽  
Cropping Systems ◽  
Crop Productivity ◽  
Arable Farming ◽  
Herbicide Use ◽  
The Impact

The growing recognition of the environmental and health issues associated to pesticide use requires to investigate how to manage weeds with less or no herbicides in arable farming while maintaining crop productivity. The questions of weed harmfulness, herbicide efficacy, the effects of herbicide use on crop yields, and the effect of reducing herbicides on crop production have been addressed over the years but results and interpretations often appear contradictory. In this paper, we critically analyze studies that have focused on the herbicide use, weeds and crop yield nexus. We identified many inconsistencies in the published results and demonstrate that these often stem from differences in the methodologies used and in the choice of the conceptual model that links the three items. Our main findings are: (1) although our review confirms that herbicide reduction increases weed infestation if not compensated by other cultural techniques, there are many shortcomings in the different methods used to assess the impact of weeds on crop production; (2) Reducing herbicide use rarely results in increased crop yield loss due to weeds if farmers compensate low herbicide use by other efficient cultural practices; (3) There is a need for comprehensive studies describing the effect of cropping systems on crop production that explicitly include weeds and disentangle the impact of herbicides from the effect of other practices on weeds and on crop production. We propose a framework that presents all the links and feed-backs that must be considered when analyzing the herbicide-weed-crop yield nexus. We then provide a number of methodological recommendations for future studies. We conclude that, since weeds are causing yield loss, reduced herbicide use and maintained crop productivity necessarily requires a redesign of cropping systems. These new systems should include both agronomic and biodiversity-based levers acting in concert to deliver sustainable weed management.

Sign in / Sign up

Export Citation Format

Share Document