2020 ◽  
pp. 100464
Author(s):  
P. Bostan ◽  
A. Stein ◽  
F. Alidoost ◽  
F. Osei

2021 ◽  
Vol 304-305 ◽  
pp. 108422
Author(s):  
David L. Gobbett ◽  
Uday Nidumolu ◽  
Huidong Jin ◽  
Peter Hayman ◽  
John Gallant

2006 ◽  
Vol 137 (1-2) ◽  
pp. 15-24 ◽  
Author(s):  
U. Chung ◽  
H.H. Seo ◽  
K.H. Hwang ◽  
B.S. Hwang ◽  
J. Choi ◽  
...  

2015 ◽  
Vol 77 ◽  
pp. 29-34 ◽  
Author(s):  
P.C. Beukes ◽  
S. Mccarthy ◽  
C.M. Wims ◽  
A.J. Romera

Paddock selection is an important component of grazing management and is based on either some estimate of pasture mass (cover) or the interval since last grazing for each paddock. Obtaining estimates of cover to guide grazing management can be a time consuming task. A value proposition could assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock cover on profitability: 1) "perfect knowledge", where cover per paddock is known with perfect accuracy, 2) "imperfect knowledge", where cover per paddock is estimated with an average error of 15%, 3) "low knowledge", where cover is not known, and paddocks are selected based on longest time since last grazing. Grazing management based on imperfect knowledge increased farm operating profit by approximately $385/ha compared with low knowledge, while perfect knowledge added a further $140/ha. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improving knowledge of pasture availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residuals to maximise pasture regrowth. Keywords: modelling, paddock selection, pasture cover


2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.


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