•Biochar studies were assessed relating to experiment and crop types, biochar and soil condition.•Despite most studies in developed countries soils are less impaired than in developing ...countries.•Wood and municipal wastes were major biochar sources compared to crop residues and manures.•Averaged across many scientific studies, biochar increases crop yields ∼20% with about 10tha−1.•Strong collaboration is required globally to advance the research and adoption of biochar.
Multiple nutrient deficiencies related to severe soil fertility depletion have emerged as the major constraint to the sustainability of agriculture on a global scale. Use of biochar and biochar-compost mixtures from different alternative organic sources have been proposed as an option for improving soil fertility, restoring degraded land, and mitigating the emissions of greenhouse gasses associated with agriculture. We review the findings of 634 publications in the last decade on biochar and biochar-compost mixtures as soil amendments in order to identify the potential gaps in our understanding of the role of these amendments in agriculture. We found that: i) the majority of published studies have been carried out in developed countries where soils are less impaired in terms of food production capacity than in many developing countries; ii) studies on biochar produced in small kilns are more common than biochars produced at commercial scale in developed countries, whereas biochars produced using traditional techniques are more commonly used than biochars produced in modern pyrolysis units in developing countries; iii) laboratory and greenhouse studies are more common than field studies; and iv) wood and municipal wastes were the major feedstock for the preparation of biochar compared to crop residues and manures. Although, biochar-compost application proved to be more generally effective in improving soil properties and crop yields (field crops and horticulture crops) than biochar alone, along with desired soil properties, could be a feasible alternative to remediate the degraded soils and improve their productivity potential in the long-term. Overall, a lack of long-term, well-designed field studies on the efficacy of biochar and biochar-compost mixtures on different soil types and agro-climatic zones are limiting our current understanding of biochar's potential to enhance crop production and mitigate climate change. We further suggest that greater collaboration between researchers, biochar producers, and policy makers is required to advance the research and uptake of this important technology at a global scale.
Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper ...presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.
Tropospheric ozone (O3) is an important air pollutant which causes substantial losses in crop production. Increasing O3 levels in India particularly in Indo-Gangetic Plain (IGP) is a major issue as ...it is reducing the crop yield. The present study is an attempt to determine the O3 and its precursor trend using continuous ground-based observations at a suburban site in IGP. The study focuses on the overall characteristics of annual, monthly, diurnal and hourly measurements of O3. Annual mean values of O3 have shown an increment of 19.2% from 2010 to 2015. Similarly, nitrogen oxide (NOx) levels increased by 30.2%. O3 levels at the study site showed a significant increasing trend of 0.7 ppb/yr. The observed O3 trend was analyzed in terms of changes in NOx levels and meteorological parameters. No significant difference in meteorological parameters was observed during 2010–15, however, NOx levels have shown an increasing trend of 0.9 ppb/yr. Further to quantify the impact of increasing O3 on crops, ozone-related crop yield losses for rice and wheat crop were determined for the period 2010–15. AOT40 (accumulated ozone exposure over a threshold of 40 ppb) and M7 (mean 7-h O3 mixing ratio from 09:00 to 15:59 LT) O3 exposure metrics were used to calculate the reduction in crop yield during major crop growing seasons: Rabi and Kharif.
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•Surface O3 increased at a rate of 0.7 ppb/yr from 2010 to 2015.•Increased NOx emission by anthropogenic activities contributed to the increase in surface O3 levels.•Higher O3 induced crop-yield losses observed for wheat than rice due to higher O3 levels during Rabi season.•Comparison of US and Europe-specific and Asia-specific O3 exposure crop-yield relationships for wheat and rice crops.
•Crop yield forecasting models were developed for the Canadian Prairies.•Linear and nonlinear machine learning methods for crop forecasting were compared.•Satellite-derived vegetation indices (NDVI ...and EVI) were used to predict crop yields.•NDVI was found to be the better predictor, though EVI generally added extra skill.•The nonlinear models did not show an advantage over the linear models.
Crop yield forecast models for barley, canola and spring wheat grown on the Canadian Prairies were developed using vegetation indices derived from satellite data and machine learning methods. Hierarchical clustering was used to group the crop yield data from 40 Census Agricultural Regions (CARs) into several larger regions for building the forecast models. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), and NDVI derived from the Advanced Very High Resolution Radiometer (AVHRR) were considered as predictors for crop yields. Multiple linear regression (MLR) and two nonlinear machine learning models – Bayesian neural networks (BNN) and model-based recursive partitioning (MOB) – were used to forecast crop yields, with various combinations of MODIS-NDVI, MODIS-EVI and NOAA-NDVI as predictors. Crop yield forecasts made using predictors from July and earlier were evaluated by the cross-validated mean absolute error skill score (in reference to climatological forecasts) during 2000–2011. While MODIS-NDVI was found to be the most effective predictor for all three crops, having MODIS-EVI as an additional predictor enhanced the forecast skills. While MLR, BNN and MOB all showed significantly higher skills than climatological forecasts for all three crops, barley was the only case where the nonlinear BNN and MOB models showed slightly higher skills than MLR. The lack of skill improvement by nonlinear models over MLR is likely due to the short (12 years) record available for MODIS data, which limits our study to 2000–2011, with very low yields coming from a single severe drought year (2002).
•The model provides the best configuration to maximize crop yield and energy output.•The model gives the optimal height, spacing between tables, table size, and tilt.•The amount of solar irradiation ...available for crops under the panels are calculated.•The energy output and crop yield for each configuration of the system is evaluated.•The increase in crop yield is more sensitive to the expansion of panel row spacing.
The global population is experiencing rapid growth, leading to increased demand for energy and food resources, necessitating the expansion of cultivated land. The construction of photovoltaic power plants to meet energy needs may result in competition for land between the agriculture and energy sectors. To address this issue, agrivoltaics systems are perceived as a solution, allowing for the coexistence of agricultural and energy production in the same area. However, the shading caused by solar panels can potentially. Therefore, a model has been developed to determine the best configuration for maximising both crop yields and energy production from the photovoltaic field. The purpose of this paper is to develop a model that optimizes energy production and crop yield within an agrivoltaics system. The model integrates factors such as elevation, spacing, tilt, panel technology and size to enhance the radiation under the photovoltaic panels, as well as to increase crop yield and the efficiency of photovoltaic array. It is constructed based on the climatic condition and the relationship between the shaded area and the sunlight distribution below the photovoltaic panels. Furthermore, the model relies on the correlation between the configuration used and the energy power delivered by the photovoltaic array. A set of equations that link configuration, sunlight, crop yield, and photovoltaic panel power was developed, and the model was implemented in MATLAB, using genetic algorithm optimisation techniques. The initial step involves the determination of radiation values under the panels, followed by the identification of the best scenarios for subsequent simulations aimed at evaluating crop yield and power generation from the photovoltaic array. A case study was conducted in Kamboinsin village (12°27′ N, 1°33′ W), in Burkina Faso, focusing onusing corn cultivation to validate the model. The results show that the model effectively identifies the optimal configuration for maximizing both crop yield and photovoltaic field output. The simulation results reveals that the distribution of radiation under the panels is significantly influenced by factors such as panel elevation height, spacing between table, and spacing between rows of table. Notably, the yield is more sensitive to the spacing between rows of panels. When comparing the effects of the different panel sizes, it is evident that utilizing smaller tables leads to higher crop yields. However, this approach results in a decrease in energy production from the photovoltaic field. For instance, on 1 ha of land, a table consisting of a single 100 Wp panel generated 92.8 % of the crop yield achieved in full sun with a nominal power of 96.9 kWp, whereas a table comprising 2 panels of 260 Wp produced 80.1 % of the yield with a nominal power of 378.56 kWp.
Achieving global food security and ensuring sustainable agriculture, the dual objectives of the second Sustainable Development Goal (SDG 2), necessitate immediate and collaborative efforts from ...developing and developed nations. The adverse effects of ozone on crop yields have the potential to significantly undermine the United Nations' ambitious target of attaining food security and ending hunger by 2030. This review examines the causes of growing tropospheric ozone, especially in India and China which lead to a substantial reduction in crop yield and forest biomass. The findings show that a nexus of high population, rapid urbanization and regional pollution sources aggravates the problem in these countries. It elucidates that when plants are exposed to ozone, specific cellular pathways are triggered, resulting in changes in the expression of genes related to hormone production, antioxidant metabolism, respiration, and photosynthesis. Assessing the risks associated with ozone exposure involves using response functions that link exposure-based and flux-based measurements to variables like crop yield. Precisely quantifying the losses in yield and economic value in food crops due to current ozone levels is of utmost importance in comprehending the risks ozone poses to global food security. We conclude that policymakers should focus on implementing measures to decrease the emissions of ozone precursors, such as enhancing vehicle fuel efficiency standards and promoting the use of cleaner energy sources. Additionally, efforts should be directed toward mapping or developing crop varieties that can tolerate ozone, applying protective measures at critical stages of plant growth and establishing ozone-related vegetation protection standards.
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•Tropospheric ozone jeopardises food security.•Use of OTC and FACE systems to evaluate O3-crop response.•Exposure-based index AOT40 widely used in studies.•Use of emission control, chemical protectants, and bioengineering techniques to prevent yield losses.
Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables ...the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.
There is global interest in understanding the prospects for biochar application to agricultural soils. If biochar enhances the availability of nitrogen (N) and phosphorus (P) to crops, this could be ...pivotal in reducing N and P fertilizer inputs to agricultural soils. This review evaluates the soil biochemical cycling of N and P as influenced by biochars with diverse characteristics, and describes the consequences for plant nutrition with respect to the N use efficiency (NUE) and P use efficiency (PUE) of crops grown in biochar-amended soils. Fundamentally, biochar can alter microbial-mediated reactions in the soil N and P cycles, i.e. N2 fixation, mineralization of N and P, nitrification, ammonia volatilization and denitrification. As well, biochar provides reactive surfaces where N and P ions are retained in soil microbial biomass and in exchange sites, both of which modulate N and P availability to crops. Distinctions must be made between biochars derived from manure- and crop residue-based feedstocks versus biochars derived from ligno-cellulosic feedstock, as well as biochars produced at a lower production temperature (<400 °C) versus biochars generated at a higher production temperature (≥600 °C). These factors determine the nutrient retention capacity of biochars when they are applied to soil. For example, low bioavailable N and P concentrations are expected when coarse-textured soil is amended with biochar having a high surface area, necessitating fertilizer application to avoid N and P deficiencies in the crop. Since the biochemical cycling of N and P in biochar-amended soil is affected strongly by biochar × soil interactions, detailed assessment of biochar-induced changes in soil physico-chemical properties and biological processes may improve predictions of how diverse biochars will affect soil fertility and crop nutrition under site-specific conditions.
•Biochar alters soil physico-chemical and biological properties, and nutrient cycling.•Soil N and P dynamics are a function of interactions between biochar type and soil.•N and P cycling is modulated by biochar-induced changes in microbial processes.•Biochar-induced N and P immobilization can be overcome by applying fertilizer.•There is a research gap regarding nutrient use efficiency in biochar-amended soil.
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