Muhammad Abdul Munnaf

Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach [KA] and SNF-Robin Hood approach [RHA]) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha⁻¹) than the URN (2.64 t ha⁻¹) treatment, leading to increasing gross margins by EUR 91 ha⁻¹ (SNF–KA) and EUR 88.5 ha⁻¹ (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha⁻¹, SNF–RHA applied more N by 17.90 kg N ha⁻¹ than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis.

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...

Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R²p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R²p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R²p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R²p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.

Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.