Chlordecone (CLD), was widely applied in banana fields in the French West Indies from 1972 to 1993. The WISORCH model was constructed to assess soil contamination by CLD and estimated that it lasts from 100 to 600 years, depending on leaching intensity and assuming no degradation. However, recent studies demonstrated that CLD is degraded in the environment, hence questioning the reliability of previous estimations. This paper shows how to improve the model and provides insights into the long-term dissipation of CLD. In-situ observations were made in nearly 2545 plots between 2001 and 2020, and 17 plots were sampled at two dates. Results of soil analyses showed an unexpected 4-fold decrease in CLD concentrations in the soil, in contrast to simulations made using the first version of WISORCH at the time. Neither erosion, nor CLD leaching explained these discrepancies. In a top-down modeling approach, these new observations of CLD concentrations led us to implement a new dissipation process in the WISORCH model that corresponds to a DT50 dissipation half-life of 5 years. The new version of the improved model allowed us to update the prediction of the persistence of soil pollution, with soil decontamination estimated for the 2070s. This development calls for re-evaluation of soil pollution status. Further validation of the new version of WISORCH is needed so it can contribute to crop management on contaminated soil.

Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information.

The present study aimed to investigate the water quality characteristics and the ecological indicators of wastewater of white legged shrimp Penaeus vannamei ponds along the Southeast coast of India. The wastewater samples were collected from 15 shrimp farms covering 11 districts located along the coastal line of Tamil Nadu and Puducherry, India. By adopting standard methods, the collected samples were subjected to analyses of physico-chemical and biological characteristics, especially the microbial load and metal and plankton composition. The nitrate-nitrogen, ammonia-nitrogen, THB, TCB, and Cu concentrations of the samples were found to exceed the permissible limit as recommended by WHO, USEPA, CPCB, and CAA. Principal component analysis and canonical correspondence analysis have suggested that the phosphate, nitrate, silicate, ammonia, and total phosphorus are the important chemical factors. The generated data would be of interest to farmers for their shrimp crop management vis-à-vis culture pond wastewater treatment.

The EU directive has addressed ambitious targets concerning the quality of water bodies. Predicting water quality as affected by land use and management requires using dynamic agro-hydrogeological models. In this study, an agronomic model (STICS) and a hydrogeological model (MODCOU) have been associated in order to simulate nitrogen fluxes in the Seine-Normandie Basin, which is affected by nitrate pollution of groundwater due to intensive farming systems. This modeling platform was used to predict and understand the spatial and temporal evolution of water quality over the 1971–2013 period. A quality assurance protocol (Refsgaard et al. Environ Model Softw 20: 1201–1215, 2005) was used to qualify the reliability of STICS outputs. Four iterative runs of the model were carried out with improved parameterization of soils and crop management without any change in the model. Improving model inputs changed much more the spatial distribution of simulated N losses than their mean values. STICS slightly underestimated the crop yields compared to the observed values at the administrative district scale. The platform also slightly underestimated the nitrate concentration at the outlet level with a mean difference ranging from −1.4 to −9.2 mg NO₃ L⁻¹ according to the aquifer during the last decade. This outcome should help the stakeholders in decision-making to prevent nitrate pollution and provide new specifications for STICS development.

Even if it is less polluting than other farm sectors, grape growing management has to adopt measures to mitigate greenhouse gas (GHG) emissions and to preserve the quality of grapevine by-products. In viticulture, by land and crop management, GHG emissions can be reduced through adjusting methods of tillage, fertilizing, harvesting, irrigation, vineyard maintenance, electricity, natural gas, and transport until wine marketing, etc. Besides CO₂, nitrous oxide (N₂O) and methane (CH₄), released from fertilizers and waste/wastewater management are produced in vineyards. As the main GHG in vineyards, N₂O can have the same harmful action like large quantities of CO₂. Carbon can be found in grape leaves, shoots, and even in fruit pulp, roots, canes, trunk, or soil organic matter. C sequestration in soil by using less tillage and tractor passing is one of the efficient methods to reduce GHG in vineyards, with the inconvenience that many years are needed for detectable changes. In the last decades, among other methods, cover crops have been used as one of the most efficient way to reduce GHG emissions and increase fertility in vineyards. Even if we analyze many references, there are still limited information on practical methods in reducing emissions of greenhouse gases in viticulture. The aim of the paper is to review the main GHG emissions produced in vineyards and the approached methods for their reduction, in order to maintain the quality of grapes and other by-products.

Agricultural soil acts as a source and sink of important greenhouse gases (GHGs) like methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Rice paddies have been a major concern to scientific community, because they produce the threatening and long-lasting GHGs mainly CH₄ and N₂O. Around 30% and 11% of global agricultural CH₄ and N₂O, respectively, emitted from rice fields. Thus, it is urgent to concurrently quantify the fluxes of CH₄ and N₂O to improve understanding of both the gases from rice fields and to develop mitigation strategies for upcoming climate change reduction. An effort is being made in this review to discuss exclusively the emission of CH₄ and N₂O under normal and controlled conditions in different locations of India and also addresses the current synthesis of available data on how field and crop management activities influence CH₄ and N₂O emissions in rice fields. Making changes to conventional crop management regimes could have a significant impact on reducing GHG emissions from rice field. Environmental and agricultural factors related to soil could be easily altered by management practices. So, knowing the mechanism of CH₄ and N₂O production and release in the rice field and factors controlling the emissions is fundamental to develop well-organized strategies to reduce emissions from rice cultivated soil. This will help the regulatory bodies or policy makers to formulate adequate policies for agricultural farmers to refine the GHG emissions as well as minimize the global climate change.

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha⁻¹, respectively. Model was further used to assess climate variability. Historical climate data (1980–2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha⁻¹ for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.

Global warming is one of the gravest threats to crop production and environmental sustainability. Rice, the staple food of more than half of the world’s population, is the most prominent cause of greenhouse gas (GHG) emissions in agriculture and gives way to global warming. The increasing demand for rice in the future has deployed tremendous concerns to reduce GHG emissions for minimizing the negative environmental impacts of rice cultivation. In this review, we presented a contemporary synthesis of existing data on how crop management practices influence emissions of GHGs in rice fields. We realized that modifications in traditional crop management regimes possess a huge potential to overcome GHG emissions. We examined and evaluated the different possible options and found that modifying tillage permutations and irrigation patterns, managing organic and fertilizer inputs, selecting suitable cultivar, and cropping regime can mitigate GHG emissions. Previously, many authors have discussed the feasibility principle and the influence of these practices on a single gas or, in particular, in the whole agricultural sector. Nonetheless, changes in management practices may influence more than one gas at the same time by different mechanisms or sometimes their effects may be antagonistic. Therefore, in the present attempt, we estimated the overall global warming potential of each approach to consider the magnitude of its effects on all gases and provided a comprehensive assessment of suitable crop management practices for reducing GHG emissions in rice culture.