Quantifying the costs of soil constraints to Australian agriculture: a case study of wheat in north-eastern Australia

Soil Research ◽  
2016 ◽  
Vol 54 (6) ◽  
pp. 700 ◽  
Author(s):  
Y. P. Dang ◽  
P. W. Moody

Soil salinity, sodicity, acidity and alkalinity, elemental toxicities, such as boron, chloride and aluminium, and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. Determination of the cost of soil constraints requires measuring the magnitude and causes of yield gap (Yg) between yield potential and actual yield. We propose a ‘hybrid approach’ consisting of determining the magnitude of Yg and the cause(s) of Yg for spatiotemporal representation of Yg that can be apportioned between management and soil constraint effects, thereby allowing a better estimate of the cost of mitigation of the constraints. The principles of this approach are demonstrated using a 2820-ha wheat-growing farm over a 10-year period to quantify the costs of the proportion of forfeited Yg due to soil constraints. Estimated Yg over the whole farm varied annually from 0.6 to 2.4Mgha–1, with an average of 1.4Mgha–1. A multiyear spatiotemporal analysis of remote sensing data identified that 44% of the farm was consistently poor performing, suggesting the potential presence of at least one soil constraint. The percentage decrease in productivity due to soil constraints varied annually from 5% to 24%, with an average estimated annual loss of wheat grain production of 182 Mg per year on 1069ha. With the 2015 season’s average wheat grain price (A$0.29kg–1), the estimated annual value of lost agricultural production due to soil constraints was estimated at A$52780 per year. For successful upscaling of the hybrid approach to regional or national scale, Australia has reliable data on the magnitude of Yg. The multiyear spatiotemporal analysis of remote sensing data would identify stable, consistently poor performing areas at a similar scale to Yg. Soil maps could then be used to identify the most-limiting soil constraints in the consistently poor performing areas. The spatial distribution of soil constraint at similar scale could be used to obtain the cost of lost production using soil constraint–grain yield models.

2009 ◽  
Vol 33 (3) ◽  
pp. 179-188 ◽  
Author(s):  
H. Taubenböck ◽  
M. Wegmann ◽  
A. Roth ◽  
H. Mehl ◽  
S. Dech

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3132
Author(s):  
Emmanouil A. Varouchakis ◽  
Anna Kamińska-Chuchmała ◽  
Grzegorz Kowalik ◽  
Katerina Spanoudaki ◽  
Manuel Graña

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.


2020 ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Lizhi Wang

AbstractLarge scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.


Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3492
Author(s):  
Jochen Aberle ◽  
Pierre-Yves Henry ◽  
Fabian Kleischmann ◽  
Christy Ushanth Navaratnam ◽  
Mari Vold ◽  
...  

The friction loss in a part of the rock-blasted unlined tunnel of the Litjfossen hydropower plant in Norway was determined from experimental and numerical studies. Remote sensing data from the prototype tunnel provided the input data for both the numerical model and the construction of a 1:15 scale model with an innovative milling approach. The numerical simulations were based on the solution of the Reynolds-averaged Navier–Stokes equations using the CFD program OpenFoam. Head loss measurements in the scale model were carried out by means of pressure measurements for a range of discharges and were compared against the results of the numerical model. The measured data were used to determine the Darcy–Weisbach and Manning friction factors of the investigated tunnel reach. The high-resolution remote sensing data were also used to test the applicability of existing approaches to determine the friction factor in unlined rock blasted tunnels. The results of the study show the usefulness of the chosen hybrid approach of experimental investigations and numerical simulations and that existing approaches for the determination of head losses in unlined tunnels need to be further refined.


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