scholarly journals Computer simulation modeling of Leaf Area Index (LAI) in maize

2014 ◽  
pp. 5-8
Author(s):  
Károly Bakó ◽  
László Huzsvai
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Leaf Development ◽  
Simulation Modeling ◽  
Data Series ◽  
Weather Station ◽  
Area Index ◽  
Average Temperature ◽  
Set Up ◽  
The University

This study presents a PHP-based model capable of calculating maize leaf area index. The model calculates LAI from emergence to 75% silking. The basis of calculation is represented by the daily average temperature values. The usability of the model was tested using three years' temperature and LAI data series from the values obtained by the weather station set up at the Látókép Experiment Site of the University of Debrecen, Centre for Agricultural Sciences between 1994 and 1996. During the running of the model, it was observed that temperature affects the intensity of leaf development to a various extent.

scholarly journals Evaluating the effect of plant population densities and nitrogen application on the leaf area index of maize in a reclaimed wetland in Kenya

2016 ◽  
Vol 8 (1) ◽  
pp. 139-148 ◽  
Author(s):  
Catherine Waithira Njuguna ◽  
Hellen Wangechi Kamiri ◽  
John Robert Okalebo ◽  
Wilson Ngetich ◽  
Syphilline Kebeney
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Density ◽  
Block Design ◽  
Nitrogen Application ◽  
Maize Production ◽  
Area Index ◽  
High Plant Density ◽  
Plant Densities ◽  
Set Up

Abstract Maize is the main staple food in Kenya with over 90% of Kenyans relying on it. While the annual national consumption is increasing, the production of this crop has been on the decline in the last two decades. Maize production in Kenya fell by 33.4% in 2013 with Nyeri among the counties said to be grappling with the production of this crop. Land pressure is one of the major causes of decreased availability of food as well as soil depletion and encroachment upon fragile ecosystems such as wetlands. Nitrogen is a key nutrient in the production of maize, and its deficiency is a major factor limiting its production. This study investigated the effect of N application at 120 kg N/ha and maize density on the Leaf Area Index in reclaimed wetland soils in an experimental set-up comprising a randomized complete block design with three replications. The research was carried out in Nyeri County, Kenya. Leaf Area Index (LAI) was determined using the given SunScan formula. Measurements were done continuously until crop physiological maturity. Results indicated that the leaf area index increased with nitrogen application and reduced with spacing for most treatments. There were no significant differences between the two methods (Copy Method and SunScan). Leaf Area Index (LAI) was high in treatments containing nitrogen and high plant density. It was concluded that high plant density gives high LAI. 50 cm * 12.5 cm (-N) and 50 cm * 12.5 cm (+N) are the recommended plant densities for the site.

scholarly journals Effect of the plant density on different maize (Zea mays L.) hybrids yields and leaf area index (LAI) values

2015 ◽  
pp. 51-56
Author(s):  
Eszter Murányi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Density ◽  
Maximum Yield ◽  
Row Spacing ◽  
Higher Plant ◽  
Area Index ◽  
Maize Hybrids ◽  
Small Plot ◽  
The University

We have investigated the plant number reactions of three maize hybrids of various genotypes in a small-plot field experiment. The plant numbers were 50, 70 and 90 thousand ha-1, while the row distances were 45 and 76 cm. The experiment was set on the Látókép Experimental Farm of Centre for Agricultural Sciences of the University of Debrecen in four replications on calcareous chernozem soil. The assimilation area and the leaf area index have important role in development of the crop yield. The studied three different genotype maize hybrids reached its maximum leaf area index at flowering. The maximum leaf area index increased linearly with increasing plant density. The season-hybrids reached less yield and leaf area index. According to our experimental results, we have concluded that with the decrease of the row spacing, the yield increased in the average of the hybrids. The studied hybrids reached the maximum yield at 70 and 90 plants ha-1 plant density. We determined the optimal plant number that is the most favourable for the certain hybrid under the given conditions.The higher plant density was favourable at 45 cm row spacing than 76 cm. The hybrids reached the maximum grain yield at 45 cm row spacing between 76 712–84 938 plants ha-1, while the optimum plant density at 76 cm row spacing changed between 61 875–65 876 plants ha-1. The leaf area index values between the applied plant density for the flowering period (July 1, 24), we defined a significant differences. In the archived yields were significant differences at the 45 cm row spacing between 50 and 70, 90 thousand ha-1 plant density, while the number for the 76 cm row spacing used did not cause a significant differences in the yield. There were significant differences between the examined hybrids of yields.

scholarly journals Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index

2018 ◽  
Vol 8 (8) ◽  
pp. 1300 ◽  
Author(s):  
Chunrong Qiu ◽  
Guiping Liao ◽  
Hongyuan Tang ◽  
Fan Liu ◽  
Xiaoyi Liao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Information ◽  
Complex Environment ◽  
Area Index ◽  
New Type ◽  
Characteristic Values ◽  
Set Up

AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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