Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data

2003 ◽  
Vol 24 (10) ◽  
pp. 2037-2054 ◽  
Author(s):  
M. H. Kalubarme ◽  
M. B. Potdar ◽  
K. R. Manjunath ◽  
R. K. Mahey ◽  
S. S. Siddhu
2000 ◽  
Vol 26 (7) ◽  
pp. 1177-1185 ◽  
Author(s):  
R.A. Seiler ◽  
F. Kogan ◽  
Guo Wei

1995 ◽  
Vol 21 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Paul C. Doraiswamy ◽  
Paul W. Cook

Soil Research ◽  
2018 ◽  
Vol 56 (1) ◽  
pp. 19 ◽  
Author(s):  
Rebecca Whetton ◽  
Yifan Zhao ◽  
Abdul M. Mouazen

Quantification of the agronomic influences of soil properties, collected at high sampling resolution, on crop yield is essential for site specific soil management. The objective of this study was to implement a novel Volterra Non-linear Regressive with eXogenous inputs (VNRX) model accounting for the linear and non-linear variability (VNRX-LN) to quantify causal factors affecting wheat yield in a 22-ha field with a waterlogging problem in Bedfordshire, UK. The VNRX-LN model was applied using high-resolution data of eight key soil properties (total nitrogen (TN), organic carbon, pH, available phosphorous, magnesium (Mg), calcium, moisture content and cation exchange capacity (CEC)). The data were collected with an on-line (tractor mounted) visible and near infrared spectroscopy sensor and used as multiple-input to the VNRX-LN model, whereas crop yield represented the single-output in the system. Results showed that the largest contributors to wheat yield were CEC, Mg and TN, with error reduction ratio contribution values of 14.6%, 4.69% and 1% respectively. The overall contribution of the soil properties considered in this study equalled 23.21%. This was attributed to a large area of the studied field having been waterlogged, which masked the actual effect of soil properties on crop yield. It is recommended that VNRX-LN is validated on a larger number of fields, where other crop yield affecting parameters e.g., crop disease, pests, drainage, topography and microclimate conditions should be taken into account.


1997 ◽  
Vol 24 (15) ◽  
pp. 1939-1942 ◽  
Author(s):  
R. Meerkoetter ◽  
B. Wissinger ◽  
G. Seckmeyer
Keyword(s):  

2020 ◽  
Vol 12 (6) ◽  
pp. 1
Author(s):  
Tyler Pittman ◽  
Rory Pittman ◽  
Jeremy Pittman

The production of cereal, legume and oilseed crops on the prairie region of Canada is largely rainfed, with high variability in the accumulation and timing of precipitation. In turn, the fluctuation of climate imparts change in farming practice. The objective of the current study is to measure the effect of rainfall and temperature on grain yield, based on longitudinal data for multiple crops on a Saskatchewan farming operation. Adjustment was made for days to maturity, fertilizer management, crop inputs, and procedures (e.g., harvest method). Detailed and thorough records of rainfall and farming routine were obtained from a farm operator on different field plots over 33 consecutive growing seasons from 1986 to 2018. The efficacy of multiple adaptive farming practices to crop yield were also evaluated, and included seed treatment, swathing, desiccation, and in-crop spraying of fungicide or pesticide. Statistical models were formulated for the association of these factors to crop yield for canaryseed (Phalaris canariensis L.), canola (Brassica napus L.), lentil (Lens culinaris Medik.) and wheat (Triticum turgidum L.). Results from this study show that temperature and rainfall above the long-term average were negatively associated with wheat yield, although the effect modification between average temperature and cumulative rainfall was positively associated with wheat yield. Over 63% of the observed variation in crop yield was attributable to planting year on this farming operation. Crop diversification is key to mitigate the effects of extreme rainfall and temperature variation on yield in this agroregion.


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