Nutrient Management for Higher Productivity of Swarna Sub1 Under Flash Floods Areas

Two field experiments were conducted at Regional Agricultural Research Station, Tarahara, Nepal during 2012 and 2013 to determine the effect of agronomic management on growth and yield of Swarna Sub1 under flash floods. The first experiment was laid out in a split plot design with three replications; and four different nutrient combinations at nursery as main plots and three age groups of rice seedlings as sub plots. The second experiment was laid out in a randomized complete block design and replicated thrice; with three post flood nutrient doses at six and 12 days after de-submergence (dad). The experiments were complete submerged at 10 days after transplanting for 12 days. The survival percentage, at 21 dad, was significantly higher in plots planted with 35 (90.25%) and 40 (91.58%) days-old seedlings compared to 30 days-old seedlings (81.75%). Plots with 35 days-old seedlings produced 5.15 t ha-1 with advantage of 18.83% over 30 days-old seedlings. Plots with 100-50-50 kg N-P2O5-K2O/ha at nursery recorded the highest grain filling of 79.41% and grain yield of 5.068 t/ha with more benefit. Post flood application of 20-20 N-K20kg/ha at 6 dad resulted in higher plant survival and taller plants, leading to significantly higher grain yield of 5.183 t/ha and straw yield of 5.315 t/ha. Hence, 35-40 days old seedlings raised with 100-50-50 kg N-P2O5-K2O /ha in nursery and the additional application of20-20 kg N-K2O /ha at 6 dad improved plant survival and enhanced yield of Swarna Sub1 under flash flood conditions. The practice has prospects of saving crop loss with getting rice yield above national average yield leading to enhanced food security in the flood prone areas of Nepal

Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale

The prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without ‘spiking’ the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question

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

How well could one predict the growth of a leafy crop from refectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two felds was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Refectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering diferences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the efects of variety were considered. Predictions from the refectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their felds to maximize the net proft from the cro

Landscape and Micronutrient Fertilizer Effect on Agro-Fortified Wheat and Teff Grain Nutrient Concentration in Western Amhara

Agronomic biofortification, encompassing the use of mineral and organic nutrient resources which improve micronutrient concentrations in staple crops is a potential strategy to promote the production of and access to micronutrient-dense foods at the farm level. However, the heterogeneity of smallholder farming landscapes presents challenges on implementing agronomic biofortification. Here, we test the effects of zinc (Zn)- and selenium (Se)-containing fertilizer on micronutrient concentrations of wheat (Triticum aestivum L.) and teff (Eragrostis tef (Zucc.) Trotter) grown under different landscape positions and with different micronutrient fertilizer application methods in the western Amhara region of Ethiopia. Field experiments were established in three landscape positions at three sites, with five treatments falling into three broad categories: (1) nitrogen (N) fertilizer rate; (2) micronutrient fertilizer application method; (3) sole or co-application of Zn and Se fertilizer. Treatments were replicated across five farms per landscape position and over two cropping seasons (2018 and 2019). Grain Zn concentration ranged from 26.6 to 36.4 mg kg−1 in wheat and 28.5–31.2 mg kg−1 in teff. Grain Se concentration ranged from 0.02 to 0.59 mg kg−1 in wheat while larger concentrations of between 1.01 and 1.55 mg kg−1 were attained in teff. Larger concentrations of Zn and Se were consistently attained when a foliar fertilizer was applied. Application of ⅓ nitrogen (N) yielded significantly larger grain Se concentration in wheat compared to a recommended N application rate. A moderate landscape effect on grain Zn concentration was observed in wheat but not in teff. In contrast, strong evidence of a landscape effect was observed for wheat and teff grain Se concentration. There was no evidence for any interaction of the treatment contrasts with landscape position except in teff, where an interaction effect between landscape position and Se application was observed. Our findings indicate an effect of Zn, Se, N, landscape position, and its interaction effect with Se on grain micronutrient concentrations. Agronomic biofortification of wheat and teff with micronutrient fertilizers is influenced by landscape position, the micronutrient fertilizer application method and N fertilizer management. The complexity of smallholder environmental settings and different farmer socio-economic opportunities calls for the optimization of nutritional agronomy landscape trials. Targeted application of micronutrient fertilizers across a landscape gradient is therefore required in ongoing agronomic biofortification interventions, in addition to the micronutrient fertilizer application method and the N fertilizer management strategy