Comparison of Organic and Integrated Nutrient Management Strategies for Reducing Soil N\u3csub\u3e2\u3c/sub\u3eO Emissions

To prevent nutrient limitations to crop growth, nitrogen is often applied in agricultural systems in the form of organic inputs (e.g., crop residues, manure, compost, etc.) or inorganic fertilizer. Inorganic nitrogen fertilizer has large environmental and economic costs, particularly for low-input smallholder farming systems. The concept of combining organic, inorganic, and biological nutrient sources through Integrated Nutrient Management (INM) is increasingly promoted as a means of improving nutrient use efficiency by matching soil nutrient availability with crop demand. While the majority of previous research on INM has focused on soil quality and yield, potential climate change impacts have rarely been assessed. In particular, it remains unclear whether INM increases or decreases soil nitrous oxide (N2O) emissions compared to organic nitrogen inputs, which may represent an overlooked environmental tradeoff. The objectives of this review were to (i) summarize the mechanisms influencing N2O emissions in response to organic and inorganic nitrogen (N) fertilizer sources, (ii) synthesize findings from the limited number of field experiments that have directly compared N2O emissions for organic N inputs vs. INM treatments, (iii) develop a hypothesis for conditions under which INM reduces N2O emissions and (iv) identify key knowledge gaps to address in future research. In general, INM treatments having low carbon to nitrogen ratio C:N (2O emissions

Simulated dataset of corn response to nitrogen over thousands of fields and multiple years in Illinois

Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of the reason is the lack of long-term data of crop responses to N over multiple fields since trial data is often limited in the number of soils and years it can explore. To overcome this limitation, we designed an analytical platform based on crop simulations run over millions of farming scenarios over extensive geographies. The database was calibrated and validated using data from more than four hundred trials in the region. This dataset can have an important role for research and education in N management, machine leaching, and environmental policy analysis. The calibration and validation procedure provides a framework for future gridded crop model studies. We describe dataset characteristics and provide thorough descriptions of the model setup

Editorial: Conservation agriculture: knowledge frontiers around the world

International audienc

Impact of cropping system diversification on productivity and resource use efficiencies of smallholder farmers in south-central Bangladesh: a multi-criteria analysis

Diversification of smallholder rice-based cropping systems has the potential to increase cropping system intensity and boost food security. However, impacts on resource use efficiencies (e.g., nutrients, energy, and labor) remain poorly understood, highlighting the need to quantify synergies and trade-offs among different sustainability indicators under on-farm conditions. In southern coastal Bangladesh, aman season rice is characterized by low inputs and low productivity. We evaluated the farm-level impacts of cropping system intensification (adding irrigated boro season rice) and diversification (adding chili, groundnut, mungbean, or lathyrus) on seven performance indicators (rice equivalent yield, energy efficiency, partial nitrogen productivity, partial potassium productivity, partial greenhouse gas footprint, benefit-cost ratio, and hired labor energy productivity) based on a comprehensive survey of 501 households. Indicators were combined into a multi-criteria performance index, and their scope for improvement was calculated by comparing an individual farmer’s performance to top-performing farmers (highest 20%). Results indicate that the baseline system (single-crop aman season rice) was the least productive, while double cropped systems increased rice equivalent yield 72–217%. Despite gains in productivity, higher cropping intensity reduced resource use efficiencies due to higher inputs of fertilizer and energy, which also increased production costs, particularly for boro season rice. However, trade-offs were smaller for diversified systems including legumes, largely owing to lower N fertilizer inputs. Aman season rice had the highest multi-criteria performance index, followed by systems with mungbean and lathyrus, indicating the latter are promising options to boost food production and profitability without compromising sustainability. Large gaps between individual and top-performing farmers existed for each indicator, suggesting significant scope for improvement. By targeting indicators contributing most to the multi-criteria performance index (partial nitrogen productivity, energy efficiency, hired labor energy productivity), results suggest further sustainability gains can be achieved through future field research studies focused on optimizing management within diversified systems

Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

Nitrogen (N) losses from cropping systems in the U.S. Midwest represent a major environmental and economic concern, negatively impacting water and air quality. While considerable research has investigated processes and controls of N losses in this region, significant knowledge gaps still exist, particularly related to the temporal and spatial variability of crop N uptake and environmental losses at the field-scale. The objectives of this study were (i) to describe the unique application of environmental monitoring and remote sensing technologies to quantify and evaluate relationships between artificial subsurface drainage nitrate (NO3-N) losses, soil nitrous oxide (N2O) emissions, soil N concentrations, corn (Zea mays L.) yield, and remote sensing vegetation indices, and (ii) to discuss the benefits and limitations of using recent developments in technology to monitor cropping system N dynamics at field-scale. Preliminary results showed important insights regarding temporal (when N losses primarily occurred) and spatial (measurement footprint) considerations when trying to link N2O and NO3-N leaching losses within a single study to assess relationship between crop productivity and environmental N losses. Remote sensing vegetation indices were significantly correlated with N2O emissions, indicating that new technologies (e.g., unmanned aerial vehicle platform) could represent an integrative tool for linking sustainability outcomes with improved agronomic efficiencies, with lower vegetation index values associated with poor crop performance and higher N2O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO3-N losses happened prior to early-season crop growth and image collection. Building on this work, we encourage future research to test the usefulness of remote sensing technologies for monitoring environmental quality, with the goal of providing timely and accurate information to enhance the efficiency and sustainability of food production

Robust spatial frameworks for leveraging research on sustainable crop intensification

Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. We hypothesize that this challenge can only be met with current and emerging technologies if guided by proactive use of a broad array of relevant data and geospatial scaling approaches to ensure local to global relevance for setting research priorities and implementing agricultural systems responsive to real-time status of weather, soils, crops, and markets. Despite increasing availability of field-scale agricultural data, robust spatial frameworks are lacking to convert these data into actionable knowledge. This commentary article highlights this knowledge gap and calls attention to the need for developing robust spatial frameworks that allow appropriate scaling to larger spatial domains by discussing a recently developed example of a data-driven strategy for estimating yield gaps of agricultural systems. To fully leverage research on sustainable intensification of cropping systems and inform policy development at different scales, we call for new approaches combining the strengths of top-down and bottom-up approaches which will require coordinated efforts between field scientists, crop modelers, and geospatial researchers at an unprecedented level

Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region

Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years

The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields

Conservation agriculture is widely promoted across sub-Saharan Africa as a sustainable farming practice that enhances adaptive capacity to climate change. The interactions between climate stress, management, and soil are critical to understanding the adaptive capacity of conservation agriculture. Yet conservation agriculture syntheses to date have largely neglected climate, especially the effects of extreme heat. For the sub-tropics and tropics, we use meta-regression, in combination with global soil and climate datasets, to test four hypotheses: (1) that relative yield performance of conservation agriculture improves with increasing drought and temperature stress; (2) that the effects of moisture and temperature stress exposure interact; (3) that the effects of moisture and temperature stress are modified by soil texture; and (4) that crop diversification, fertilizer application rate, or the time since no-till implementation will enhance conservation agriculture performance under climate stress. Our results support the hypothesis that the relative maize yield performance of conservation agriculture improves with increasing drought severity or exposure to high temperatures. Further, there is an interaction of moisture and heat stress on conservation agriculture performance and their combined effect is both non-additive and modified by soil clay content, supporting our second and third hypotheses. Finally, we found only limited support for our fourth hypothesis as (1) increasing nitrogen application rates did not improve the relative performance of conservation agriculture under high heat stress; (2) crop diversification did not notably improve conservation agriculture performance, but did increase its stability with heat stress; and (3) a statistically robust effect of the time since no-till implementation was not evident. Our meta-regression supports the narrative that conservation agriculture enhances the adaptive capacity of maize production in sub-Saharan Africa under drought and/or heat stress. However, in very wet seasons and on clay-rich soils, conservation agriculture yields less compared to conventional practices

An agenda for integrated system-wide interdisciplinary agri-food research

© 2017 The Author(s)This paper outlines the development of an integrated interdisciplinary approach to agri-food research, designed to address the ‘grand challenge’ of global food security. Rather than meeting this challenge by working in separate domains or via single-disciplinary perspectives, we chart the development of a system-wide approach to the food supply chain. In this approach, social and environmental questions are simultaneously addressed. Firstly, we provide a holistic model of the agri-food system, which depicts the processes involved, the principal inputs and outputs, the actors and the external influences, emphasising the system’s interactions, feedbacks and complexities. Secondly, we show how this model necessitates a research programme that includes the study of land-use, crop production and protection, food processing, storage and distribution, retailing and consumption, nutrition and public health. Acknowledging the methodological and epistemological challenges involved in developing this approach, we propose two specific ways forward. Firstly, we propose a method for analysing and modelling agri-food systems in their totality, which enables the complexity to be reduced to essential components of the whole system to allow tractable quantitative analysis using LCA and related methods. This initial analysis allows for more detailed quantification of total system resource efficiency, environmental impact and waste. Secondly, we propose a method to analyse the ethical, legal and political tensions that characterise such systems via the use of deliberative fora. We conclude by proposing an agenda for agri-food research which combines these two approaches into a rational programme for identifying, testing and implementing the new agri-technologies and agri-food policies, advocating the critical application of nexus thinking to meet the global food security challenge

The Replication Database:Documenting the Replicability of Psychological Science

In psychological science, replicability—repeating a study with a new sample achieving consistent results (Parsons et al., 2022)—is critical for affirming the validity of scientific findings. Despite its importance, replication efforts are few and far between in psychological science with many attempts failing to corroborate past findings. This scarcity, compounded by the difficulty in accessing replication data, jeopardizes the efficient allocation of research resources and impedes scientific advancement. Addressing this crucial gap, we present the Replication Database (https://forrt-replications.shinyapps.io/fred_explorer), a novel platform hosting 1,239 original findings paired with replication findings. The infrastructure of this database allows researchers to submit, access, and engage with replication findings. The database makes replications visible, easily findable via a graphical user interface, and tracks replication rates across various factors, such as publication year or journal. This will facilitate future efforts to evaluate the robustness of psychological research