Unraveling the Interactions between Flooding Dynamics and Agricultural Productivity in a Changing Climate
Bibliographic record
Abstract
Extreme precipitation and flooding frequency associated with global climate change are expected to increase worldwide, with major consequences in floodplains and areas susceptible to flooding. The purpose of this review was to examine the effects of flooding events on changes in soil properties and their consequences on agricultural production. Flooding is caused by natural and anthropogenic factors, and their effects can be amplified by interactions between rainfall and catchments. Flooding impacts soil structure and aggregation by altering the resistance of soil to slaking, which occurs when aggregates are not strong enough to withstand internal stresses caused by rapid water uptake. The disruption of soil aggregates can enhance soil erosion and sediment transport during flooding events and contribute to the sedimentation of water bodies and the degradation of aquatic ecosystems. Total precipitation, flood discharge, and total water are the main factors controlling suspended mineral-associated organic matter, dissolved organic matter, and particulate organic matter loads. Studies conducted in paddy rice cultivation show that flooded and reduced conditions neutralize soil pH but changes in pH are reversible upon draining the soil. In flooded soil, changes in nitrogen cycling are linked to decreases in oxygen, the accumulation of ammonium, and the volatilization of ammonia. Ammonium is the primary form of dissolved inorganic nitrogen in sediment porewaters. In floodplains, nitrate removal can be enhanced by high denitrification when intermittent flooding provides the necessary anaerobic conditions. In flooded soils, the reductive dissolution of minerals can release phosphorus (P) into the soil solution. Phosphorus can be mobilized during flood events, leading to increased availability during the first weeks of waterlogging, but this availability generally decreases with time. Rainstorms can promote the subsurface transport of P-enriched soil particles, and colloidal P can account for up to 64% of total P in tile drainage water. Anaerobic microorganisms prevailing in flooded soil utilize alternate electron acceptors, such as nitrate, sulfate, and carbon dioxide, for energy production and organic matter decomposition. Anaerobic metabolism leads to the production of fermentation by-products, such as organic acids, methane, and hydrogen sulfide, influencing soil pH, redox potential, and nutrient availability. Soil enzyme activity and the presence of various microbial groups, including Gram+ and Gram− bacteria and mycorrhizal fungi, are affected by flooding. Waterlogging decreases the activity of β-glucosidase and acid phosphomonoesterase but increases N-acetyl-β-glucosaminidase in soil. Since these enzymes control the hydrolysis of cellulose, phosphomonoesters, and chitin, soil moisture content can impact the direction and magnitude of nutrient release and availability. The supply of oxygen to submerged plants is limited because its diffusion in water is extremely low, and this impacts mitochondrial respiration in flooded plant tissues. Fermentation is the only viable pathway for energy production in flooded plants, which, under prolonged waterlogging conditions, is inefficient and results in plant death. Seed germination is also impaired under flooding stress due to decreased sugar and phytohormone biosynthesis. The sensitivity of different crops to waterlogging varies significantly across growth stages. Mitigation and adaptation strategies, essential to the management of flooding impacts on agriculture, enhance resilience to climate change through improved drainage and water management practices, soil amendments and rehabilitation techniques, best management practices, such as zero tillage and cover crops, and the development of flood-tolerant crop varieties. Technological advances play a crucial role in assessing flooding dynamics and impacts on crop production in agricultural landscapes. This review embarks on a comprehensive journey through existing research to unravel the intricate interplay between flooding events, agricultural soil, crop production, and the environment. We also synthesize available knowledge to address critical gaps in understanding, identify methodological challenges, and propose future research directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".