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Both Kenya and China are facing great challenges in feeding their populations; this is particularly problematic in Kenya, where the population will be projected to increase by 1.4 times from 2018 to 2100. Food production has been greatly improved in China, but it still lags behind in Kenya. In this study, we systematically compared the changes in agricultural resources and crop/livestock productivity, as well as their relationships with the resource input levels and agricultural production structure, to try to provide insights into reducing food insecurity and poverty in Kenya. Our results revealed that Kenya had 2–3 times more natural resources, such as cropland, grassland, and annual precipitation, per capita than did China in the 1960s, which was similar to the daily food energy and protein supply. Currently, Kenya still has higher natural resources per capita, but has lower food security and quality when compared to China. This is due to the continued rapid increase in crop and livestock productivity regarding energy and protein production in China. From 1961 to 2017, crop protein productivity increased by 44% in Kenya, while in China it increased by 282%. Our results showed that crop and livestock productivity positively correlated with the input of fertilizers, concentrate feeds, machinery, and pesticides, as seen in China. Meanwhile, the structure of crop and livestock production also showed a large impact on the changes in productivity, such as the harvest area of vegetables/fruits to the total harvest area and the ratio of monogastric animals for livestock production. Overall, both agrochemicals and structure have strong impacts on the increase in productivity, and these could be potential options in Kenya to improve productivity due to the low input of resources into crop and livestock production. Both Kenya and China are facing great challenges in feeding their populations; this is particularly problematic in Kenya, where the population will be projected to increase by 1.4 times from 2018 to 2100. Food production has been greatly improved in China, but it still lags behind in Kenya. In this study, we systematically compared the changes in agricultural resources and crop/livestock productivity, as well as their relationships with the resource input levels and agricultural production structure, to try to provide insights into reducing food insecurity and poverty in Kenya. Our results revealed that Kenya had 2–3 times more natural resources, such as cropland, grassland, and annual precipitation, per capita than did China in the 1960s, which was similar to the daily food energy and protein supply. Currently, Kenya still has higher natural resources per capita, but has lower food security and quality when compared to China. This is due to the continued rapid increase in crop and livestock productivity regarding energy and protein production in China. From 1961 to 2017, crop protein productivity increased by 44% in Kenya, while in China it increased by 282%. Our results showed that crop and livestock productivity positively correlated with the input of fertilizers, concentrate feeds, machinery, and pesticides, as seen in China. Meanwhile, the structure of crop and livestock production also showed a large impact on the changes in productivity, such as the harvest area of vegetables/fruits to the total harvest area and the ratio of monogastric animals for livestock production. Overall, both agrochemicals and structure have strong impacts on the increase in productivity, and these could be potential options in Kenya to improve productivity due to the low input of resources into crop and livestock production.

The data resource comprises of two datasets. The first dataset comprises of fortnightly measurements soil respiration, soil temperature, soil moisture and photosynthetic activity. The second data set comprises of fortnightly measurements of rainfall, throughfall and water table depth. Data were collected from the climate change field site Climoor that is located in Clocaenog forest, Northeast Wales during 2015 and 2016. The experimental field site consists of three untreated control plots, three plots where the plant canopy air is artificially warmed during night time hours and three plots where rainfall is excluded from the plots at least during the plants growing season (March to September,) All measurements of this dataset have been carried out every fortnight if not indicated otherwise. Rainfall in millimetres (mm) was measured at the site using a ground-level rain gauge. Rain throughfall (in mm) was measured in each plot using a funnel-bottle construction to collect rain water in the plant canopy. Water table depth was measured for each plot using a measuring tape. Soil respiration and related soil temperature and soil moisture were measured in three areas of each plot. Soil respiration was measured in pre-installed opaque soil collars (20 centimetre diameter) that were installed in 1999. An infra-red gas analyser (EGM-4) was used. Photosynthetic active radiation was measured above the canopy while the soil respiration measurement was conducted. The measurements were carried out by different groups of CEH Bangor staff. The Climoor field experiment intends to answer questions regarding the effects of warming and drought on ecosystem processes. Plot level soil respiration measurements are important to investigate soil carbon dynamics and changes in soil carbon cycling and storage under the imposed climatic treatments. More detailed information about the field site, measurements and related datasets can be found in the supporting documentation. Soil respiration data for 1999-2015 are available from https://doi.org/10.5285/4ed6f721-b23b-454e-b185-02ba54d551f0 Rainfall was collected using a ground-level rain gauge at the site that was emptied fortnightly. Volumes were recorded in millilitres (mL) and converted to millimetres (mm). Throughfall was measured in each plot using a funnel-bottle construction. Volumes were recorded in mL and were converted to mm. Water table depths was measured in pre-installed tubes using a measuring tape. The distance from the water surface to the soil surface was measured and subsequently converted to water table depth in centimetres.. Soil respiration was measured in pre-installed soil collars in three location in each plot using an infra-red gas analyser. The soil respiration measurement took 120 seconds and was recorded in grammes of Carbon dioxide per square metre per hour (g CO2-C m-2 hr-1). Values were then converted to mg CO2-C m-2 hr-1 and the three plot measurements were averaged to a plot average. Soil temperature and soil moisture were measured alongside the soil respiration measurement close to the pre-installed soil respiration collars. Soil temperature was measured using a thermometer, soil moisture was measured with a hand-held Theta-probe. Photosynthetic active radiation was measured above the canopy using a pyranometer. All results were entered into Excel spreadsheets. Results from all the analyses were combined into one Excel spreadsheet. Data were then exported from this combined Excel spreadsheet as .csv files for ingestion into the EIDC.

Global change has dramatic impacts on grassland diversity. However, little is known about how fast species can adapt to diversity loss and how this affects their responses to global change. Here, we performed a common garden experiment testing whether plant responses to global change are influenced by their selection history and the conditioning history of soil at different plant diversity levels. Using seeds of four grass species and soil samples from a 14-year-old biodiversity experiment, we grew the offspring of the plants either in their own soil or in soil of a different community, and exposed them either to drought, increased nitrogen input, or a combination of both. Under nitrogen addition, offspring of plants selected at high diversity produced more biomass than those selected at low diversity, while drought neutralized differences in biomass production. Moreover, under the influence of global change drivers, soil history, and to a lesser extent plant history, had species-specific effects on trait expression. Our results show that plant diversity modulates plant-soil interactions and growth strategies of plants, which in turn affects plant eco-evolutionary pathways. How this change affects species' response to global change and whether this can cause a feedback loop should be investigated in more detail in future studies.

The ‘‘Doly’’ ecosystem in the Prioksko-Terrasny Biosphere Reserve (PTBR) is the most north-western site of the meadow steppes. We studied the changes in functional parameters of the ecosystem under the influence of global warming. In the Southern Moscow Region the global warming has manifested itself in the average annual air temperature increase of about 2 degrees. Despite the aridization of climate the annual aboveground production of steppe ecosystem ‘‘Doly’’ has increased by more than a third over the period from 1998 to 2011, reaching 330 g/m2 . It is important to know the contribution of the dominant species in the grass cover, since the ecosystem is affected by the Oka river floods once in 40 years. The mesophytic association Phleum phleoides – (Festuca valesiaca) – multigrass is most susceptible to flooding. However, the period of warming is accompanied by a significant drop in the Oka-river water level. This allowed us to fix the recoverable succession (demutation) of cereal dominants. In recent years even in the most mesophytic phytocenoses a tendency of transition from Phleum phleoides dominance to Festuca valesiaca dominance is observed. The structure of two more xerophytic phytocenoses virtually unchanged: in the projective cover Festuca valesiaca and Stipa pennata dominate. From 2005 to 2011 the projective cover of Stipa pennata has increased so that now the projective cover of Festuca valesiaca and Stipa pennata doubled to 20% and 8% respectively. According to the three stationary herbal field, the average cover of the Gramineae in ‘‘Doly’’ ecosystem makes up 17% of the total projective cover of herbage (by Ramensky method). Thus, in the period of global warming the isolated ecosystem ‘‘Doly’’ (in Moscow region) became closer to the reference meadow steppes ecosystem (in Kursk region) by functional parameters, such as productivity and projective cover of the basic dominant grasses.

The short list of practices is the result of literature review and expert panel input and a step wise process of considering certain critical measures (e.g. increase of soil organic carbon (SOC); greenhouse gas (GHG) emission reduction e.g. carbon dioxide, nitrous oxide, methane; system integration). The list is based on a range of practices already proposed to deliver soil carbon sequestration (SCS). First, specific practices were identified with potential for both a positive impact on SCS at farm level and an uptake rate compatible with global impact. These focus on: (a) optimising crop primary productivity; (b) reducing soil disturbance and managing soil physical properties; (c) minimising deliberate removal of C or lateral transport via erosion; (d) addition of C produced outside the system; (e) provision of additional C inputs within the cropping system. Then, economic and non‐cost barriers and incentives for land managers are considered, along with the potential externalised impacts of implementation. The provided data presents a list of greenhouse gas removal practices for soil organic carbon sequestration, which are suitable under biophysical, economic and social consideration. The list is the result of the first step in analysing the potential of agricultural soils to sequester carbon globally and is part of the NERC funded project Soils-R-GGREAT (NE/P019455/1). The work is based on literature research and expert panel and judgements. The work was supported by the Natural Environment Research Council (NE/P019455/1)

The above ground herbaceous biomass is a very small fraction of the total terrestrial organic carbon stocks and it was calculated with the same model as in the 2014 NTCSA dataset. The equation incorporates Rain Use Efficiency, amount of rain required to enable production per year and tree cover fraction. A mean herbaceous layer is modelled based on mean long term rainfall. The above ground herbaceous biomass was based on harvest factors obtained from the 2002 Agricultural Census. A proportional approach per land use, based on 1990 National Land Cover (NLC) data was used for the soil organinc carbon (SOC) analysis.

This study used a combination of stratified sampling and random sampling. From July 2016 to April 2017, a questionnaire survey was conducted with farmers in Xuyi County, Guanyun County, Xinghua City, Jingjiang City, Lishui District, and Jiangning District of Jiangsu Province. A total of 714 questionnaires were issued to farmers; 688 valid questionnaires were obtained. The study subjects have the following basic characteristics: the average age of the respondents is 53 and the average contracted land area of the respondents is 5.33 mu; 14.68% of respondents have a primary education, and 32.27% have a junior high school education; the average household count is 4.54, and the average labor force is 3.25; the minimum annual household income of the respondents is 6,500 yuan, and the maximum annual household income is 1,900,000 yuan. The average agricultural income ratio is 22.38%, and the average non-agricultural labor force ratio is 70.89%.

This data set is related to the SoyFACE experiments, which are open-air agricultural climate change experiments that have been conducted since 2001. The fumigation experiments take place at the SoyFACE farm and facility in Champaign County, Illinois during the growing season of each year, typically between June and October. - The "SoyFACE Plot Information 2001 to 2021" file contains information about each year of the SoyFACE experiments, including the fumigation treatment type (CO2, O3, or a combination treatment), the crop species, the plots (also referred to as 'rings' and labeled with numbers between 2 and 31) used in each experiment, important experiment dates, and the target concentration levels or 'setpoints' for CO2 and O3 in each experiment. - This data set includes files with minute readings of the fumigation levels ("SoyFACE 1-Minute Fumigation Data Files" folder) from the SoyFACE experiments. The "Soyface 1-Minute Fumigation Data Files" folder contains sub-folders for each year of the experiments, each of which contains sub-folders for each ring used in that year's experiments. This data set also includes hourly data files for the fumigation experiments ("SoyFACE Hourly Fumigation Data Files" folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments ("Hourly Weather and Ambient Data Files" folder). The ambient CO2 and O3 data are collected at SoyFACE, and the weather data are collected from the SURFRAD and WARM weather stations located near the SoyFACE farm. - The "Fumigation Target Percentages" file shows how much of the time the CO2 and O3 fumigation levels are within a 10 or 20 percent margin of the target levels when the fumigation system is turned on. - The "Matlab Files" folder contains custom code (Aspray, E.K.) that was used to clean the "SoyFACE 1-Minute Fumigation Data" files and to generate the "SoyFACE Hourly Fumigation Data" and "Fumigation Target Percentages" files. Code information can be found in "SoyFACE Hourly Fumigation Data Explanation". - Finally, the " * Explanation" files contain information about the column names, units of measurement, and other pertinent information for each data file.