Regression analysis for yield loss of oil palm due to Ganoderma disease

2015 ◽  
Author(s):  
K. Assis ◽  
K. P. Chong ◽  
A. S. Idris ◽  
H. W. Hoong ◽  
C. M. Ho
Keyword(s):  
Regression Analysis ◽  
Oil Palm ◽  
Yield Loss

Salinity and climatic effects on the yields of citrus

1985 ◽  
Vol 25 (3) ◽  
pp. 711 ◽  
Author(s):  
PJ Cole ◽  
PI McCloud
Keyword(s):  
Regression Analysis ◽  
High Temperatures ◽  
Fruit Set ◽  
Yield Loss ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Multiple Linear Regression Analysis ◽  
Significant Variable ◽  
Irrigation Season ◽  
Climatic Effects

A multiple linear regression analysis of salinity and climate against yield of Valencia and Washington Navel Oranges was performed for the period 1945-79 on data from irrigated orchards in Sunraysia, Berri, Waikerie and Mypolonga. Principal component analysis was utilized to reduce the number of climatic variables introduced into the multiple regression analysis. High temperatures and high evaporation during flowering and fruit set (November and December) were associated with reduced yields in Sunraysia, Berri and Waikerie, possibly by increasing flower and fruit drop, and by reducing fruit set. At Mypolonga, the coolest location studied, high temperatures were associated with increased yields. Salinity was negatively associated with yield at Mypolonga, Waikerie and Berri, the locations of highest salinity. No effects were observed at Sunraysia. It was not possible to deduce a critical level of salinity in irrigation water that is associated with yield loss or the magnitude of yield loss from the statistical analyses. However, we did observe that salinity during the irrigation season prior to harvest was associated with decreased yields, while only at Mypolonga was salinity in the year of harvest a significant variable in the analyses.

scholarly journals GROWTH PROFILE ANALYSIS OF OIL PALM BY USING SPOT 6 THE CASE OF NORTH SUMATRA

2017 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Ita Carolita ◽  
J. Sitorus ◽  
Johannes Manalu ◽  
Dhimas Wiratmoko
Keyword(s):  
Regression Analysis ◽  
High Resolution ◽  
Satellite Data ◽  
Oil Palm ◽  
Elaeis Guineensis ◽  
Profile Analysis ◽  
Secondary Data ◽  
Profile Data ◽  
Growth Profile ◽  
North Sumatra

Oil Palm (Elaeis guineensis Jack.) is one of the world’s most important tropical tree crops. Its expansion has been reported to cause widespread environment impacts. SPOT 6 data is one of high resolution satellite data that can give information more detail about vegetation and the age of oil palm plantation. The objective of this study was to analyze the growth profile of oil palm and to estimate the productivity age of oil palm. The study area is PTP N 3 in Tebing Tinggi North Sumatera Indonesia.  The method that used is NDVI analysis and regression analysis for getting the model of oil palm growth profile. Data from the field were collected as the secondary data to build that model. The data that collected were age of oil palm and diameters of canopy for every age.   Results indicate that oil palm growth can be explained by variation of NDVI with formula y = -0.0004x2 + 0.0107x + 0.3912, where x is oil palm age and  Y is NDVI of SPOT, with R² = 0.657. This equation can be used to predict the age of oil palm for range 4 to 11 years with R2 around 0.89.

Impact of Nealley’s Sprangletop on Rough Rice Yield

2018 ◽  
Vol 32 (5) ◽  
pp. 532-536
Author(s):  
Eric P. Webster ◽  
Eric A. Bergeron ◽  
David C. Blouin ◽  
Benjamin M. McKnight ◽  
Matthew J. Osterholt
Keyword(s):  
Regression Analysis ◽  
Yield Loss ◽  
Rice Yield ◽  
Field Studies ◽  
Herbicide Application ◽  
Leaf Stage ◽  
Rough Rice ◽  
Rice Yields ◽  
The One ◽  
The Impact

AbstractTwo field studies were conducted in Louisiana to determine the impact of Nealley’s sprangletop on rough rice yield under multiple environments in 2014, 2015, and 2016. The first study evaluated optimal timings of Nealley’s sprangletop removal for optimizing rough rice yields. The second study evaluated the impact of Nealley’s sprangletop densities on rough rice yield. Nealley’s sprangletop was removed with applications of fenoxaprop at 122 g ai ha–1at 7, 14, 21, 28, 35, and 42 d after emergence (DAE). Nealley’s sprangletop removal at 7 and 14 DAE resulted in higher rough rice yields of 7,880 and 6,960 kg ha–1, respectively, when compared with the rice from the season-long Nealley’s sprangletop competition with a 6,040 kg ha-1yield. Delaying herbicide application from 7 DAE to 42 DAE resulted in a yield loss of 1,740 kg ha–1. Over the 35-d delay in application, rough rice yield loss from Nealley’s sprangletop interference was equivalent to 50 kg ha–1d–1. Nealley’s sprangletop densities were established at 1, 3, 7, 13, and 26 plants m–2by transplanting Nealley’s sprangletop when rice reached the one- to two-leaf stage. At Nealley’s sprangletop densities of 1 to 26 plants m–2, rough rice yields were reduced 10 to 270 kg ha–1, compared with the rice from weed-free plots. Based on regression analysis, Nealley’s sprangletop densities of 1, 35, 70, and 450 plants m–2reduced rough rice yield 0.14%, 5%, 10%, and 50%, respectively.

ANALISIS PENGARUH BIAYA PEMUPUKAN TANAMAN DAN BIAYA PANEN TERHADAP PENDAPATAN PETANI

2021 ◽  
Vol 3 (1) ◽  
pp. 53-63
Author(s):  
Saprida ◽  
Wilson Saruksuk
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Data Processing ◽  
Multiple Linear Regression ◽  
Oil Palm ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Analysis Method ◽  
The Status

Tujuan penelitian ini adalah Untuk mengetahui pengaruh biaya pemupukan tanaman terhadap pendapatan petani, dan Untuk mengetahui pengaruh biaya panen terhadap pendapatan petani. Pengambilan sampel dilakukan kepada petani kelapa sawit dengan sampel sebanyak 100 responden. Metode analisis yang dilakukan adalah dengan metode analisis regresi linear berganda, pengelolahan data dibantu oleh sofware (SPSS) Versi 25. Penelitian ini dilakukan pada bulan Agustus-September 2020.Hasil penelitian ini disimpulkan bahwa 1). Usia yang paling banyak memiliki kebun kepala sawit yaitu usia 41-50 tahun, 2). Jenis kelamin yang paling dominan yaitu laki-laki sebesar 73%, 3). Petani juga mulai bertani >6 tahun sebesar 62%, 4). Dan luas lahan yang di miliki petani kelapa sawit rakyat yaitu sebesar 4 - 6 ha sebanyak 63%, 5). Status lahan yang dikelola petani kelapa sawit adalah lahan sewa sebayak 51%.   The purpose of this study was to determine the effect of crop fertilization costs on farmers 'income, and to determine the effect of harvest costs on farmers' income. Sampling was carried out on oil palm farmers with a sample of 100 respondents. The method of analysis used is multiple linear regression analysis method, data processing is assisted by software (SPSS) Version 25. This research was conducted in August-September 2020. The results of this study concluded that 1). The age that has the most oil palm plantations is the age of 41-50 years, 2). The most dominant gender is male at 73%, 3). Farmers also started farming> 6 years by 62%, 4). And the area of ​​land owned by smallholder oil palm farmers is 4 - 6 ha as much as 63%, 5). The status of land managed by oil palm farmers is 51% leased land.

scholarly journals Pengaruh Tipe Petani dan Ekspektasi Harga dalam Sertifikasi Kelapa Sawit

2021 ◽  
Vol 5 (3) ◽  
pp. 679-690
Author(s):  
Indah Utami ◽  
◽  
Alin Halimatussadiah
Keyword(s):  
Regression Analysis ◽  
Oil Palm ◽  
Management Practice ◽  
Best Management Practice ◽  
Pesticide Use ◽  
Price Expectations ◽  
Ols Regression ◽  
Best Management ◽  
Oil Palm Plantation

Certification is a compliance in implementing Best Management Practice (BMP) in oil palm plantation businesses. Participation in certification is influenced by several factors, including socio-economic, demographic, environmental, political and other factors. This study to see the effect of farmer types (independent and scheme farmers) and higher price expectations in implementing oil palm certification. Other determinants also studied included income, education, age, pesticide use, farming experience, and location. Through OLS regression analysis, it is known that the farmer types, higher price expectations, income, pesticide use, farming experience, and location are factors that influence the area of certified oil palm plantations.

Influence of the Relative Time of Emergence of Wild Oat (Avena fatua) on Yield Loss of Barley (Hordeum vulgare) and Wheat (Triticum aestivum)

Weed Science ◽  
1985 ◽  
Vol 33 (4) ◽  
pp. 498-503 ◽  
Author(s):  
John T. O'Donovan ◽  
E. Ann De St. Remy ◽  
P. Ashely O'Sullivan ◽  
Don A. Dew ◽  
Arvind K. Sharma
Keyword(s):  
Triticum Aestivum ◽  
Regression Analysis ◽  
Hordeum Vulgare ◽  
Significant Relationship ◽  
Field Experiments ◽  
Yield Loss ◽  
Avena Fatua ◽  
Regression Equations ◽  
Wild Oat ◽  
Relative Time

Multiple regression analysis of data from field experiments conducted in Alberta at two locations between 1972 and 1983 indicated that there was a significant relationship between yield loss of barley (Hordeum vulgareL.) and wheat (Triticum aestivumL.) and relative time of emergence of wild oat (Avena fatuaL. ♯ AVEFA). At a given wild oat density, percent yield loss increased the earlier wild oat emerged relative to the crops and gradually diminished the later it emerged. However, the magnitude of the yield loss for both species varied with the year. Regression equations based on data pooled over years and locations were developed to provide an estimate of yield loss of barley and wheat due to relative time of wild oat emergence and wild oat density. The information should be considered when barley and wheat losses due to wild oat are being assessed.

Effect of density and time of removal of volunteer canola (Brassica rapa L.) on yield loss of wheat (Triticum aestivum L.)

2008 ◽  
Vol 88 (4) ◽  
pp. 839-842 ◽  
Author(s):  
John T O'Donovan ◽  
K. Neil Harker ◽  
Donald A Dew
Keyword(s):  
Triticum Aestivum ◽  
Regression Analysis ◽  
Key Words ◽  
Brassica Rapa ◽  
Nonlinear Regression ◽  
Field Experiments ◽  
Yield Loss ◽  
Wheat Yield ◽  
Triticum Aestivum L ◽  
Nonlinear Regression Analysis

Field experiments were conducted at Lacombe, Alberta, in 1976, 1978, and 1979. Nonlinear regression analysis of the data indicated that initial slopes (% wheat yield loss at low canola densities) varied from 0.29% in 1979 to 2.44% in 1978. However, volunteer canola at densities of 47 (1976), 345 (1978), and 251 (1979) plants m-2 had little effect on wheat yield if the canola was removed at approximately 25 d or earlier after wheat emergence. Key words: Brassia rapa, volunteer canola density, time of weed removal

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