Daniel Kindred

Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency

The efficient use of fertilizer nitrogen (N) is crucial to sustainable human nutrition. All crops receive significant amounts of additional N in temperate environments, through fixation or fertilizer use. This paper reviews progress towards the efficient use of fertilizer N by winter wheat (Triticum aesitivum L.) and spring barley (Hordeum vulgare L.) in the UK, acknowledging that on-farm this is governed by economics. Recent multi-site N response experiments on old and modern varieties show that yield improvements since the 1980s have been accompanied by increases in economic optimum N amounts for wheat but not for spring barley. On-farm N use efficiency (NUE) has increased for barley because increased yields with optimum N were associated with compensatory decreases in grain N concentration, whereas on-farm NUE has not increased for wheat because grain N concentration has not changed and improvements in N capture were insufficient to make up for the increased yield. Genetic effects on NUE are shown to differ markedly depending on whether they are determined at a single N rate, as in variety trials, or with optimum N amounts. It is suggested that, in order to elicit faster improvement in NUE on farms, breeding and variety testing should be conducted at some sites with more than one level of applied N, and that grain N%, N harvest index, and perhaps canopy N ratio (kg N ha(-1) green area) should be measured more widely. It is also suggested that, instead of using empirical functions, N responses might be analysed more effectively using functions based on explanations of yield determination for which the parameters have some physiological meaning.

Opportunities for avoidance of land‐use change through substitution of soya bean meal and cereals in European livestock diets with bioethanol coproducts

An analysis is presented which quantifies the potential for distillers dried grains with solubles (DDGS, a coproduct of wheat bioethanol production) to replace soya bean meal (SBM) and cereals in livestock rations. A major proportion of the SBM imported into Europe as a protein‐rich feedstuff for livestock comes from South America, where land‐use change (LUC) is associated with high carbon emissions. Production of DDGS can therefore reduce LUC in South America by substitution of SBM in animal feed. The analysis indicates that a single bioethanol distillery processing 1 million tonnes of wheat, and producing ca. 330 000 tonnes of DDGS per annum, would substitute at least 136 493 tonnes of whole soya beans grown on 47 725 ha of land, and save greenhouse gas emissions equivalent to 0.63 million tonnes CO2 per annum. By growing sugar beet and wheat in an average ratio of 0.06 : 0.94 on 1 ha of land in Europe, the net area of agricultural land required to produce feed ingredients equivalent to 6.08 t of sugar beet pulp (SBP) and 1.72 t of DDGS associated with 2363 L of bioethanol, is reduced to 0.40 ha. This accounts for 0.42 ha of soya that is not required when DDGS displaces SBM, and 0.18 ha of wheat that is not required when DDGS and SBP displace wheat in livestock rations.

Exploring the spatial variation in the fertilizer-nitrogen requirement of wheat within fields

The fertilizer-nitrogen (N) requirement for wheat grown in the UK varies from field to field. Differences in the soil type, climate and cropping history result in differences in (i) the crops’ demands for N, (ii) the supply of N from the soil (SNS) and (iii) the recovery of the fertilizer by the crops. These three components generally form the basis of systems for N recommendation. Three field experiments were set out to investigate the variation of the N requirement for wheat within fields and to explore the importance of variation in the crops’ demands for N, SNS and fertilizer recovery in explaining the differences in the economic optima for N. The N optima were found to vary by >100 kg N/ha at two of the sites. At the other site, the yield response to N was small. Yields at the optimum rate of N varied spatially by c. 4 t/ha at each site. Soil N supply, which was estimated by the unfertilized crops’ harvested N, varied spatially by 120, 75 and 60 kg/ha in the three experiments. Fertilizer recovery varied spatially from 30% to >100% at each of the sites. There were clear relationships between the SNS and the N optima at all the three sites. The expected relationship between the crops demand for N and N optima was evident at only one of the three sites. There was no consistent relationship between the N recovery and the N optima. A consistent relationship emerged, however, between the optimal yield and SNS; areas with a greater yield potential tending to also supply more N from the soil. This moderated the expected effect of the SNS and the crops demand for N on the N optima.

Stability, across environments, of grain and alcohol yield, in soft wheat varieties grown for grain distilling or bioethanol production

BACKGROUND Soft-milling wheat has potential use for both grain whisky distilling and bioethanol production. Varietal comparisons over wide-ranging environments would permit assessment of both grain and alcohol yield potential and also permit the stability across environments, for these parameters, to be compared. RESULTS For 12 varieties, analysis of variance showed highly significant effects of variety, site, season and fertiliser application on grain and alcohol yield. There were also significant interactions between these factors and, consequently, varieties varied in stability across environments as well as in mean values for the parameters assessed. Alcohol production per hectare was affected more strongly by variation in grain yield than alcohol yield, but increasing grain protein content reduced alcohol yield and, therefore, utility for grain distilling. CONCLUSION To maximise energy production, the best varieties for bioethanol would combine high and stable grain yield with slower reduction of alcohol yield as grain protein increases. For grain distilling, where the energy balance is less important, high alcohol yield will remain the key factor. Data derived using near infrared spectroscopy can be valuable in assessing stability of quality traits across environments.

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

ABSTRACT Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities

Abstract Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China.

Triticale out-performs wheat on range of UK soils with a similar nitrogen requirement

Triticale has a reputation for performing well on poor soils, under drought and with reduced inputs, but there has been little investigation of its performance on the better yielding soils dominated by wheat production. The present paper reports 16 field experiments comparing wheat and triticale yield responses to nitrogen (N) fertilizer on high-yielding soils in the UK in harvest years 2009–2014. Each experiment included at least two wheat and at least two triticale varieties, grown at five or six N fertilizer rates from 0 to at least 260 kg N/ha. Linear plus exponential curves were fitted to describe the yield response to N and to calculate economically optimal N rates. Normal type curves with depletion were used to describe protein responses to N. Whole crop samples from selected treatments were taken prior to harvest to measure crop biomass, harvest index, crop N content and yield components. At commercial N rates, mean triticale yield was higher than the mean wheat yield at 13 out of 16 sites; the mean yield advantage of triticale was 0·53 t/ha in the first cereal position and 1·26 t/ha in the second cereal position. Optimal N requirement varied with variety at ten of the 16 sites, but there was no consistent difference between the optimal N rates of wheat and triticale. Triticale grain had lower protein content and lower specific weight than wheat grain. Triticale typically showed higher biomass and straw yields, lower harvest index and higher total N uptake than wheat. Consequently, triticale had higher N uptake efficiency and higher N use efficiency. Based on this study, current N fertilizer recommendations for triticale in the UK are too low, as are national statistics and expectations of triticale yields. The implications of these findings for arable cropping and cereals markets in the UK and Northern Europe are discussed, and the changes which would need to occur to allow triticale to fulfil a role in achieving sustainable intensification are explored.