scholarly journals Estimating the Effect of Climatic Variables and Crop Area on Maize Yield in Ghana

2012 ◽  
Vol 3 (9) ◽  
pp. 313-321
Author(s):  
Henry De-Graft Acquah
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Crop Yield ◽  
Crop Production ◽  
Growing Season ◽  
Maize Yield ◽  
Climatic Variables ◽  
Non Linear ◽  
Crop Area

Climate change tends to have negative effects on crop yield through its influence on crop production. Understanding the relationship between climatic variables, crop area and crop yield will facilitate development of appropriate policies to cope with climate change. This study therefore examines the effects of climatic variables and crop area on maize yield in Ghana based on regression model using historical data (1970-2010). Linear and Non-linear regression model specifications of the production function were employed in the study. The study found that growing season temperature trend is significantly increasing by 0.03oC yearly whereas growing season rainfall trend is insignificantly increasing by 0.25mm on yearly basis. It was also observed that rainfall is becoming increasingly unpredictable with poor distributions throughout the season. Results from the linear and non-linear regression models suggest that rainfall increase and crop area expansion have a positive and significant influence on mean maize yield. However, temperature increase will adversely affect mean maize yield. In conclusion, the study found that there exists not only a linear but also a non-linear relationship between climatic variables and maize yield.

scholarly journals Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 °C & 2.0 °C

2018 ◽  
Author(s):  
Yi Chen ◽  
Zhao Zhang ◽  
Fulu Tao
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Extreme Events ◽  
Crop Production ◽  
Maize Yield ◽  
Crop Growth ◽  
Growth Duration ◽  
Growth Environment ◽  
Warming Scenario ◽  
Yield Decrease

Abstract. A new temperature goal of holding the increase in global average temperature well below 2℃ above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ℃ above pre-industrial levels has been established in Paris Agreement, which calls for understanding of climate risk under 1.5 ℃ & 2.0 ℃ warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e., maize, wheat, rice) in China during 2106–2115 at warming scenarios of 1.5 ℃ & 2.0 ℃ using the method of ensemble simulation with well-validated MCWLA family crop models, their 10 sets of optimal crop model parameters, and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and probability of crop yield decrease. Results showed that the decrease of crop growth duration and the increase of extreme events impacts in the future would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the cultivation areas. By contrast, with the moderate increases in temperature, solar radiation, precipitation, and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resource could be ameliorated which enhance canopy photosynthesis, and consequently biomass accumulations and yields. The moderate climate change would slightly deteriorate maize growth environment but result in much more appropriate growth environment for wheat and rice. As a result, the wheat and rice yields could increase by 3.9 % and 4.1 %, respectively, and maize yield could increase by 0.2 %, at a warming scenario of 1.5 ℃. At the warming scenario of 2.0 ℃, wheat and rice yield would increase by 8.6 % and 9.4 %, respectively, but maize yield could decrease by 1.7 %. In general, the warming scenarios would bring more opportunities than risks for the crop development and food security in China. Moreover, although variability of crop yield would increase with the change of climate scenario from 1.5 ℃ warming to 2.0 ℃ warming, the probability of crop yield decrease would decrease. Our findings highlight that the 2.0 ℃ warming scenario would be more suitable for crop production in China, but the expected increase in extreme events impacts should be paid more attention to.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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.

ACE: A non-linear regression model

1988 ◽  
Vol 3 (4) ◽  
pp. 301-313 ◽  
Author(s):  
Ildiko E. Frank ◽  
Silvia Lanteri
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Non Linear

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

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