Genetic mitigation strategies to tackle agricultural GHG emissions: The case for biological nitrification inhibition technology

Accelerated soil-nitrifier activity and rapid nitrification are the cause of declining nitrogen-use efficiency (NUE) and enhanced nitrous oxide (N2O) emissions from farming. Biological nitrification inhibition (BNI) is the ability of certain plant roots to suppress soil-nitrifier activity through production and release of nitrification inhibitors. The power of phytochemicals with BNI-function needs to be harnessed to control soil-nitrifier activity and improve nitrogen-cycling in agricultural systems. Transformative biological technologies designed for genetic mitigation are needed so that BNIenabled crop-livestock and cropping systems can rein in soil-nitrifier activity to help reduce greenhouse gas (GHG) emissions and globally make farming nitrogen efficient and less harmful to environment. This will reinforce the adaptation or mitigation impact of other climate-smart agriculture technologies

Meeting the challenges facing wheat production: The strategic research agenda of the Global Wheat Initiative

Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat Initiative was tasked with supporting the wheat research community by facilitating collaboration, information and resource sharing and helping to build the capacity to address challenges facing production in an increasingly variable environment. Many countries invest in wheat research. Innovations in wheat breeding and agronomy have delivered enormous gains over the past few decades, with the average global yield increasing from just over 1 tonne per hectare in the early 1960s to around 3.5 tonnes in the past decade. These gains are threatened by climate change, the rapidly rising financial and environmental costs of fertilizer, and pesticides, combined with declines in water availability for irrigation in many regions. The international wheat research community has worked to identify major opportunities to help ensure that global wheat production can meet demand. The outcomes of these discussions are presented in this paper

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content

This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI—Ri, 750 to 840 nm and Rj, ±720–736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: −0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.This work was funded partially by the CGIAR Research Program on Wheat (www.wheat.org) and by the Spurring Transformation in Agriculture Research (STARS) project—under number 1094229-2014 (www. starsproject.org). Blasch and Taylor were supported by United Kingdom Government funding through the Newton Fund for this work

Energy-dispersive X-ray fluorescence spectrometry as a tool for zinc, iron and selenium analysis in whole grain wheat

Background and aims: Crop biofortification programs require fast, accurate and inexpensive methods of identifying nutrient dense genotypes. This study investigated energy-dispersive X-ray fluorescence spectrometry (EDXRF) for the measurement of zinc (Zn), iron (Fe) and selenium (Se) concentrations in whole grain wheat. Methods: Grain samples were obtained from existing biofortification programs. Reference Zn, Fe and Se concentrations were obtained using inductively coupled plasma optical emission spectrometry (ICP-OES) and/or inductively coupled plasma mass spectrometry (ICP-MS). One set of 25 samples was used to calibrate for Zn (19–60 mg kg–1) and Fe (26–41 mg kg–1), with 25 further samples used to calibrate for Se (2–31 mg kg–1 ). Calibrations were validated using an additional 40–50 wheat samples. Results: EDXRF limits of quantification (LOQ) were estimated as 7, 3 and 2 mg kg–1 for Zn, Fe, and Se, respectively. EDXRF results were highly correlated with ICP-OES or -MS values. Standard errors of EDXRF predictions were ±2.2 mg Zn kg–1, ±2.6 mg Fe kg–1, and ±1.5 mg Se kg–1. Conclusion: EDXRF offers a fast and economical method for the assessment of Zn, Fe and Se concentration in wheat biofortification programs