• Energy Research
• 2. Zero hunger close
• US close
• GB close
• FR close
• AU close
• FI close
• CO close

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Data was either measured in situ in the field (N2O flux, soil moisture, rainfall and air temperature) or samples were taken, processed, and analysed in the laboratory (soil pH, electrical conductivity (EC), ammonium, nitrate, microbial community composition and crop yield). N2O flux data was measured on a mobile gas chromatograph (GC) system and integrated to obtain peak areas on Peak490Win10Canabis programme. The times, peak areas and sample ID were then exported into a .CHR file and imported into Flux.NET.3.3 which calculated N2O flux as an output in Excel which was exported as .csv file for deposit in EIDC. N2O flux was used to calculate cumulative N2O flux using trapezoidal integration in Excel and saved in a separate .csv file for deposit in EIDC. Soil moisture was measured on Accilmas with data stored as a .csv on a DataSnap that was downloaded and sorted by treatment and saved as a .csv file. Rainfall and air temperature were downloaded from the weather station as .csv file. Soil pH and EC were recorded manually into a notebook and input into an Excel spreadsheet and exported as a .csv file. Soil ammonium and nitrate content was measured using the microplate method using a programme called Gen5. Date was exported into an Excel spreadsheet and absorbance units used to calculate ammonium/nitrate content in milligrams per kilogram using a calibration curve from a set of standards in an Excel spreadsheet. This was exported as a .csv file. Crop growth data was recorded in the field in a notebook and input into an Excel spreadsheet and exported as a .csv file. Crop yield was recorded in a notebook and input into an Excel spreadsheet and exported as a .csv file. Microbial community composition was measured using 16S gene sequencing on an Illumina MiSeq. This generated raw sequencing reads which were processed using Python and filtered using QIIME v1.3.1. creating asv.count.table.csv of counts of each Amplicon Sequence Variants (ASVs) per sample and taxa.table.csv of the taxonomic lineage for each ASVs. This dataset contains field data on nitrous oxide (N2O) emissions, microbial community composition, crop yield and growth and soil biochemical properties. The field trial consisted of three different treatments of control, conventional microplastic addition and biodegradable microplastic addition where winter barley was grown. The data presented are from field and laboratory measurements. Data was collected by the data authors. The field trial was carried out from September 2020 to July 2021 at Henfaes Field Centre, UK. Research was funded through NERC Grant NE/V005871/1. Do agricultural microplastics undermine food security and sustainable development in developing countries?

This paper develops a strategy for the continuing and improved supply of woodfuels to urban and industrial consumers in Sub-Sahara Africa. It argues that continued use of these fuels is not only a necessity, but is also in the best economic interest of most of the countries in this region. It shows that intensified and more orderly utilization of woodfuels can help to enhance, rather than impinge upon environmental parameters. Some examples are provided that illustrate how such strategies can be put into practice.

This chapter focuses on the evaluation of adaptive capacities of community-level human systems related to agriculture and food security. It highlights findings regarding approaches and domains to monitor and evaluate behavioral changes from CGIAR’s research program on climate change, agriculture and food security (CCAFS). This program, implemented in five West African countries, is intended to enhance adaptive capacities in agriculture management of natural resources and food systems. In support of participatory action research on climate-smart agriculture, a monitoring and evaluation plan was designed with the participation of all stakeholders to track changes in behavior of the participating community members. Individuals’ and groups’ stories of changes were collected using most significant change tools. The collected stories of changes were substantiated through field visits and triangulation techniques. Frequencies of the occurrence of characteristics of behavioral changes in the stories were estimated. The results show that smallholder farmers in the intervention areas adopted various characteristics of behavior change grouped into five domains: knowledge, practices, access to assets, partnership and organization. These characteristics can help efforts to construct quantitative indicators of climate change adaptation at local level. Further, the results suggest that application of behavioral change theories can facilitate the development of climate change adaptation indicators that are complementary to indicators of development outcomes. We conclude that collecting stories on behavioral changes can contribute to biophysical adaptation monitoring and evaluation.

Abstract Agricultural practice is facing multiple challenges under volatile commodity markets, inevitable climate change, mounting pest pressure and various other environment-related constraints. The objective of this research is to present a dynamic optimization model of crop rotations and farm management and show its suitability for economic analysis over a 30 year time period. In this model, we include management practices such as fertilization, fungicide treatment and liming, and apply it in a region in Southwestern Finland. Results show that (i) growing pest pressure favours the cultivation of wheat-oats and wheat-oilseeds combinations, while (ii) market prices largely determine the crops in the rotation plan and the specific management practices adopted. The flexibility of our model can also be utilized in evaluating the value of other management options such as new cultivars under different projections of future climate and market conditions.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under grassland cover, no-tillage, cover crops, residue return and organic amendments were compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include the following key descriptor variables and covariates useful for development of scoring functions: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class, pH, and SOC, 3) depth and timing of soil sampling, 4) analytical methods for SQI quantification, 5) units used in published works (i.e. equivalent mass, concentration), 6) SQI abundances, and 7) statistical significance of difference comparisons. *Note: Blank values in tables are considered unreported data.

The Buxton Climate Change Impacts study was established in 1992 on a steep daleside of calcareous grassland outside Buxton, Derbyshire, UK. In five replicate blocks of 3 x 3 m plots, the vegetation has been subjected to climate treatments of winter heating (3C above ambient, Nov-April), summer drought (no rain, July-Aug), summer augmented rainfaill (20% above the long-term average, June-Sept), and two interaction treatments (heating-drought, heating-watered) in addition to replicated controls. The grassland is maintained in a short turf to simulate sheep and cattle grazing each autumn (Oct). In addition to annual point quadrat touches conducted at the whole-plot (9 m2) scale, permanent microsite quadrats were established within each plot in 2008 at the 100 cm2 scale. Species cover and environmental parameters have been monitored in microsites annually (2009-2011 controls only). This data package contains microsite soil depth and pH data; another package contains species cover data.

Urea is the most common nitrogen (N) fertilizer in agriculture, due to its cheaper price and high N content. Although the reciprocal influence between NO3- and NH4+ nutrition are well known, urea (U) interactions with these N-inorganic forms are poorly studied. Here, the responses of two tomato genotypes to ammonium nitrate (AN), U alone or in combination were investigated. Significant differences in root and shoot biomass between genotypes were observed. Under AN+U supply, Linosa showed higher biomass compared to UC82, exhibiting also higher values for many root architectural traits. Linosa showed higher Nitrogen Uptake (NUpE) and Utilization Efficiency (NUtE) compared to UC82, under AN+U nutrition. Interestingly, Linosa exhibited also a significantly higher DUR3 transcript abundance. These results underline the beneficial effect of AN+U nutrition, highlighting new molecular and physiological strategies for selecting crops that can be used for more sustainable agriculture. The data suggest that translocation and utilization (NUtE) might be a more important component of NUE than uptake (NUpE) in tomato. Genetic variation could be a source for useful NUE traits in tomato; further experiments are needed to dissect the NUtE components that confer a higher ability to utilize N in Linosa.

Dataset compiled by Yushu Xia and Michelle Wander for the Soil Health Institute. Data were recovered from peer reviewed literature reporting results for three ‘Tier 2’ indicators (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) in terms of their relative response to management where soils under cover crops, grassland cover, organic amendments and residue return compared to conventionally managed controls. Peer-reviewed articles published between January of 1990 and December 2017 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, and “fluorescein diacetate hydrolysis”, together with one or more of the following: “management practice”, “tillage”, “cover crop”, “residue”, “organic fertilizer”, or “manure”. Records were tabulated to compare SQI abundance in soil maintained under a control (conventional cropping with that found under soil health promoting practice) and soil aggrading practice with the intent to contribute to SQI databases that will support development of interpretive frameworks and/or algorithms including pedo-transfer functions relating indicator abundance to management practices and site specific factors. Meta-data include key descriptor variables and covariates useful for development of scoring functions which include: 1) identifying factors for the study site (location, year of initiation of study and year in which data was reported), 2) soil textural class and pH, 3) depth of sampling, 4) analytical methods for quantification (i.e.: loss on ignition, combustion), 5) units used in published works (i.e.: equivalent mass, concentration), 6) SOC class (L,M,H), and 7) statistical significance of difference comparisons.