A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha^?1 for P and up to £81 ha^?1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha^?1 applied P fertiliser when compared with uniform application.

     

Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management

Precision Agriculture - How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions?...     

Influence of vegetative cycle of asparagus (Asparagus officinalis L.) on copper,iron, zinc and manganese content

The essential elements copper (Cu), iron (Fe), zinc (Zn) and manganese (Mn) were analyzed in fresh asparagus to determine the effects of the vegetative cycle of the plant on the micronutrient content. Asparagus samples were classified in two groups by diameter (<11 mm and >14 mm). Asparagus from a sample group with the same diameter were divided into two portions (apical and basal) according to distance from the tip. The concentrations of copper, iron, zinc and maganese increased during the vegetative cycle of the asparagus, mainly in the apical portion which showed significantly greater concentrations with respect to the basal portion. The >14 mm diameter asparagus presented higher levels of copper, zinc and manganese, whereas the concentration of iron was greater in the <11 mm diameter asparagus. The mean element levels were (mg/kg dry weight): Cu, 18.9±3.9; Fe, 91.7±33.7; Zn, 69.5±24.6 and Mn, 20.9±5.0).     

Authentication of farmed and wild turbot (Psetta maxima) by fatty acid and isotopic analyses combined with chemometrics

Fatty acid composition and stable isotope ratios of carbon (delta(13)C) and nitrogen (delta(15)N) were determined in muscle tissue of turbot (Psetta maxima). The multivariate analysis of the data was performed to evaluate their utility in discriminating wild and farmed fish. Wild (n=30) and farmed (n=30) turbot of different geographical origins (Denmark, The Netherlands, and Spain) were sampled from March 2006 to February 2007. The application of linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA) to analytical data demonstrated the combination of fatty acids and isotopic measurements to be a promising method to discriminate between wild and farmed fish and between wild fish of different geographical origin. In particular, IRMS (Isotope Ratio Mass Spectrometry) alone did not permit us to separate completely farmed from wild samples, resulting in some overlaps between Danish wild and Spanish farmed turbot. On the other hand, fatty acids alone differentiated between farmed and wild samples by 18:2n-6 but were not able to distinguish between the two groups of wild turbot. When applying LDA isotope ratios, 18:2n-6, 18:3n-3, and 20:4n-6 fatty acids were decisive to distinguish farmed from wild turbot of different geographical origin, while delta(15)N, 18:2n-6, and 20:1n-11 were chosen to classify wild samples from different fishing zones. In both cases, 18:2n-6 and delta(15)N were determinant for classification purposes. We would like to emphasize that IRMS produces rapid results and could be the most promising technique to distinguish wild fish of different origin. Similarly, fatty acid composition could be used to easily distinguish farmed from wild samples.