Ecology

Distribution of nitrogen-cycling pathways in groundwaterDifferences in nitrogen-cycling processes based on oxygen and nitrate concentrationsSixteen metagenomes (Table S4) were obtained from duplicate wells at four sites (A–D) from two unconfined alluvial aquifers (Canterbury, Fig. S1). These sites encompassed varied nitrate (0.45–12.6 g/m3), DO (0.37–7.5 mg/L), and dissolved organic carbon (DOC) (0–26 g/m3) concentrations (Fig. 1A; Table S1). Nitrate concentrations were pristine (site C) to N-contaminated (sites A, B, D) [4]. Sites A–C were oxic and had low DOC (typical of groundwaters), whereas site D was dysoxic with relatively high DOC. Metagenomes from groundwater wells comprised pairs, representing the planktonic and sediment-attached fractions. Over 70 Gbp of raw sequence was generated per site (390 Gbp overall, 322 Gbp trimmed). However, 2Kb long and only 0.64–8.14% of reads (3.8% on average) mapped to MAGs (Table S4), reflecting the complexity of microbial communities in the terrestrial subsurface [11]. To capture this diversity, metagenomic reads are first used here to determine the distribution of N metabolisms.Fig. 1: Geochemistry and protein-coding sequences (based on reads) involved in nitrogen cycling that are significantly different among sites used for metagenomics.A Bar plots showing geochemical data from groundwater samples, coloured according to site. Solid bar colour = groundwater samples. Grid lines = attached-fraction enriched groundwater. All samples from site D were characterized as dysoxic, although gwj15-16 contained 0.37 mg/L DO, which are near suboxic levels (i.e. More

All crops have been modified through some form of improvement, whether to enhance yield, taste, resilience or another factor. My passion is to continue accelerating the development of crop varieties that are more resistant to climate change and pests. This will make food supplies more secure and will also improve the quality of life for small-hold farmers in Africa and Asia, whose livelihoods can be devastated by crop failure.The goal of crop breeding is not only to develop new varieties, but also to produce genetically superior parents with a range of desirable traits that will be useful in future generations. Complex traits, such as yield or climate resilience, are often regulated by many genes. To speed up crop breeding for those traits, we use genomic data to select the best parental combinations, and then cameras and digital tools to identify the best progeny.In this photo, I’m in a greenhouse in Peru owned by my employer, the International Potato Center (CIP), inspecting potential sweet potato (Ipomoea batatas) breeding parents for cross-pollination. CIP is one of 13 gene banks and research facilities around the world, known collectively as One CGIAR, which protect and utilize crop genetic diversity. I’ve worked at CIP since 2016; before then, I worked in industry, where I developed crops such as drought-tolerant corn hybrids.Because potatoes don’t have seeds that can be preserved for decades, we must reproduce them by growing small parts of plant organs, such as a root, a tuber or part of a stem, in tissue culture. Nearly 85% of the unique potato populations stored at CIP are also cryopreserved in liquid nitrogen to maintain a long-term backup.I can’t think of a nobler mission than working on food security. I hope that more young scientists — especially women — will focus their talents on crop breeding for the future. More

Nature Positive is an aspirational term that is increasingly being used by businesses, governments and NGOs, but there is a danger that its meaning is being diluted away from measurable overall net gain in biodiversity towards merely any action that benefits nature, argues E.J. Milner-Gulland.The term is appealing because it suggests an optimistic, intuitive and clear summary of where society needs to get to, and it can be used equally by business, government and civil society to describe their aspirations to protect and recover nature. However, once terms start gaining traction, particularly relatively general terms like Nature Positive, there is a risk of slippage and loss of meaning. It is already starting to feel like any actions that increase biodiversity anywhere, and by any amount, can be called Nature Positive. This trend has to be resisted. More

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Study areaThere is a long history of diversified cropping patterns due to the climatic and topographic complexity in China4. Cropping intensity increases from north to south, and multiple cropping dominates in regions south of 400N4. For example, multiple cropping systems of double rice and winter wheat plus maize are popular in the Middle-lower Yangtze river plain and the Huang-Huai-Hai plain, respectively (Fig. 1)22. Three staple crops, maize, paddy rice, and wheat, are widely distributed across the country (Figure S1). These three major crops contributed to more than half (57.08%) of the total sown area in China in 2020 (http://www.stats.gov.cn/english/).Fig. 1The distribution map of cropping patterns in 2021, 9 agricultural regions and validation sites in China. Notes: A to I represented nine agricultural regions in China. (A) Middle-lower Yangtze River Plain; (B) Huang-Huai-Hai plain; (C) Northeast China; (D) Inner Mongolia and along the Great Wall; (E) Loess plateau; (F) Southwest China; (G) Southern China; (H) Gansu-Xinjiang region; (I) Qinghai-Tibet region.Full size imageMODIS images and pre-processingWe used the 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products (MOD09A1) from 2015 to 2021. Three spectral indices were calculated: the 2-band Enhanced Vegetation Index (EVI2)23, LSWI16, and Normalized Multi-band Drought Index (NMDI)24 (Fig. 2). The functions of EVI2, LSWI, and NMDI are provided in Eqs. 1–3 as follows.$${rm{EVI2}}=2.5times left({rho }_{NIR}-{rho }_{{rm{Red}}}right)/left({rho }_{NIR}+2.4times {rho }_{{rm{Red}}}+1right)$$
(1)
$${rm{LSWI}}=left({rho }_{NIR}-{rho }_{SWIR6}right)/left({rho }_{NIR}+{rho }_{SWIR6}right)$$
(2)
$$NMDI=frac{{rho }_{NIR}-left({rho }_{SWIR6}-{rho }_{SWIR7}right)}{{rho }_{NIR}+left({rho }_{SWIR6}-{rho }_{SWIR7}right)}$$
(3)
where, ρNIR, ρRed, ρSWIR6 and ρSWIR7 represented the surface reflectance values from the red (620–670 nm), Near-infrared (841–875 nm), short wave infrared band centered at 1640 nm (1628–1652 nm) and 2130 nm (2105–2155 nm), respectively.Fig. 2The workflow of the methodology: Data preprocessing, deriving cropping intensity, mapping three staple crops and obtaining annual maps of cropping patterns in China.Full size imageFor each spectral index (EVI2, LSWI, and NMDI), a daily continuous time series was developed based on the cloud-free observations using the Whittaker Smoother (WS)25. The WS smoother performed well in multiple cropping regions and therefore was applied here26.Validation data and other datasetsThe validation data in this study included the ground truth reference data and agricultural census data. The ground truth reference data were collected in major agricultural regions with GPS receivers and digital cameras during the study period (2015–2021) (Fig. 1, Table S1). For each sampling site, the geographic location and crop types were recorded. The reliability of ground survey data was improved through visual confirmation based on high-resolution images in Google Earth. Some reference sites with small field sizes were removed to considering the mixed-pixel problems of MODIS images. Finally, we obtained a total of 18,379 ground samples collected during 2015–2021 (Table S1). All the ground truth reference data were used to validate the cropping pattern data in its corresponding year. Agricultural census data were obtained from the National Statistical Bureau of China (NSBC) (http://www.stats.gov.cn/english/), which was collected through sampling statistics. The crop calendar data from agro-meteorological stations recorded the sowing, seedling, tillering, heading, and harvesting dates of winter wheat (210 sites) or spring wheat (90 sites). The calendar data were applied to establish the trend surfaces of key phenological stages of winter wheat and spring wheat, respectively. The crop calendar data were provided by the National Meteorological Information Center, China Meteorological Administration.The cropland distribution data were derived from the 30 m GlobeLand30 global land cover data in 202027. The total accuracy of GlobeLand30 in 2020 is 85.72%, and the Kappa coefficient is 0.82 (www.globallandcover.com). To correspond to MODIS images, the 30 m cropland pixels from GlobeLand30 data were spatially aggregated to a 500 m cropland fraction map. For simplification, we classified pixel purity of MODIS pixels into three groups: cropland percentages of >90%, 50–90%, and More