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Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]

Certain soils promote crop health because they are pathogen-suppressive (i.e., fungistatic) or disease-suppressive, but the effect of soil management on these properties is not fully understood. Here, we tested the hypothesis that manure could favor fungistasis by screening 26 manured or non-manured wheat fields from Serbia for their ability to control survival/growth of the fungal plant pathogen Fusarium graminearum Fg1. Quantitative PCR showed that the pathogen grew after inoculation in all 26 autoclaved soils. In absence of autoclaving, the pathogen was stable or grew in 16 soils (37 % manured) but declined in the 10 others (70 % manured). For most soils, there was no significant link between soil chemistry and fungistasis, except with Mionica in western/central Serbia. Mionica soils MI2 and MI3, which had received manure, exhibited higher levels of organic matter and potassium compared with soils MI4 and MI5, which had not received manure and were non-fungistatic. Using Mionica soils, we then tested the hypothesis that fungistatic (manured) soils rather than non-fungistatic (non-manured) soils would protect wheat from F. graminearum disease. Indeed, fungistatic soils were suppressive to wheat damping-off. Non-fungistatic soil MI4 was conducive, as expected, but non-fungistatic soil MI5 turned out to be suppressive. Metabarcoding showed that the structure of prokaryotic and fungal rhizosphere communities depended mostly on field location, with a significant effect of F. graminearum inoculation. In conclusion, our findings show that certain farming practices (here, manure amendments) may promote soil fungistasis towards F. graminearum. However, both fungistatic and non-fungistatic soils can be suppressive to F. graminearum disease in wheat, and their differences in rhizosphere microbiota suggest different phytoprotection mechanisms. [ABSTRACT FROM AUTHOR]

The concept of agrivoltaics (APV) has made progress in recent years but has not yet been widely promoted and applied on a larger scale. The main hindrance lies in the fact that the effects on crop cultivation have not been optimal. This study establishes two sets of single-axis dynamic tracking photovoltaic (PV) systems utilizing bifacial modules: Solar tracking (ST) employing the maximum power generation strategy, and Reverse tracking (RT) employing the minimum shadow area tracking strategy for ground-mounted tracking. In Xiongan (N39.02°, E116.11°), ST increases power generation by 3.9% compared to fixed-axis PV systems (Tilt = 25°), while RT decreases power generation by 34.7%. The discrepancy in power generation between ST and RT is most noticeable on clear sky and partly cloudy days, with minimal difference on cloudy days. We investigate the impact of these systems on crop growth by analyzing changes in canopy area during the lettuce growing period and compare the net photosynthetic rates (P n) of spring wheat (C 3 crop) and millet (C 4 crop) using canopy photosynthesis and transpiration measurement systems (CAPTS). The results indicate that lettuce shows a larger leaf area under ST throughout the growing period, millet exhibits a higher net photosynthetic rate under RT, while there is marginal difference in the P n of spring wheat under both systems. This study integrates the above research configuration with Chinese Solar Greenhouses (CSGs) located in Yufa, Beijing. During the production season, the dynamic PV systems switch to the shadow-free mode to ensure the photosynthesis of crops within the CSGs. Conversely, during the idle season of the CSGs, the system shifts to ST mode to ensure PV power generation. By integrating power generation and planting data, the Land Equivalent Ratio (LER) can reach 1.46, with the Return on Investment (ROI) of 20% based on construction costs. Through experiments and practical case studies, it is evident that dynamic PV systems can be effectively integrated with CSGs, providing sufficient land and space for both PV power generation and crop cultivation. • Analyzing the spatial and temporal distribution patterns of light fields under agrivoltaics (APV). • Applying phenotypic and canopy photosynthesis techniques to the study of plants under APV. • Integrating dynamic photovoltaic strategies with crop stubble. • A large-scale case comprising 60 photovoltaic greenhouses was completed. [ABSTRACT FROM AUTHOR]

Climate variability plays a pivotal role in rainfed agriculture, especially within arid regions. Analyzing these fluctuations across diverse climatic conditions establishes a foundation for subsequent investigations. In Iran, the FAO 56 aridity index categorizes the nation into very dry, dry, semidry, and humid climate classifications. This study aimed to explore equations derived from multiple linear regression (MLR) and the disparities between predicted and observed yields of rainfed wheat and barley across Iran's varying climates. Meteorological data, encompassing rainfall (R), mean temperature (T mean), solar radiation (S), and wind speed (U 2), were compiled from 44 synoptic stations spanning 1981–2020. These data constituted inputs for the MLR models employed to simulate rainfed wheat and barley yields. The Global Performance Indicator (GPI), a 5-point statistical criteria index, was utilized to assess MLR model performance. The findings unveiled superior MLR model performance in dry climates (R2=0.84 for wheat and R2=0.9 for barley) compared to humid climates (R2=0.69 for wheat and R2=0.66 for barley), evidenced by lower statistical error criteria values. Moreover, across all climates, the MLR models exhibited more accurate predictions for rainfed wheat yield (GPI=1559.3) in contrast to rainfed barley (GPI=1536). In conclusion, this study sheds light on the notable role of climate in rainfed agriculture, showcasing the efficacy of MLR models in predicting yields across varying climatic contexts. • Strong correlation between rainfall and crop yield underscores water's impact on productivity. • MLR models demonstrate higher accuracy in drier climates compared to humid ones. • Rainfall and wind speed emerge as crucial predictors of crop yields, overestimating across climates. • GPI analysis reveals MLR's excellence in estimating yields in dry climates. • Importance of tailored models for accurate yield predictions, with GPI introduced for comprehensive evaluation. [ABSTRACT FROM AUTHOR]

The accumulation of antimony (Sb) in plants and its potential effects on human health are of increasing concern. Nevertheless, only a few countries or regions have established soil Sb thresholds for agricultural purposes, and soil properties have not been taken into account. This study investigated the accumulation of Sb in the edible parts of pakchoi and wheat grain by adding exogenous Sb to 21 soils with varying properties. The results revealed a positive correlation between bioavailable Sb (Sb ava , extracted by 0.1 M K 2 HPO 4) in soil and Sb in the edible parts of pakchoi (R2 = 0.77, p < 0.05) and wheat grain (R2 = 0.54, p < 0.05). Both machine learning and traditional multiple regression analysis indicated Sb ava was the most critical feature and the main soil properties that contributed to Sb uptake by pakchoi and wheat were CaCO 3 and clay, respectively. The advisory food limits for Sb in pakchoi and wheat were estimated based on health risk assessment, and used to derive soil thresholds for safe pakchoi and wheat production based on Sb tot and Sb ava , respectively. These findings hold potential for predicting Sb uptake by crops with different soil properties and informing safe production management strategies. [Display omitted] • Pakchoi has a higher accumulation potential of Sb than wheat. • Phytoavailability of Sb in soils was predicted by phosphate-extraction. • Machine learning and multiple regression models were used to identify key factors. • CaCO 3 and pH was identified as major factors influencing Sb accumulation in pakchoi. • Clay content was a primary factor in controlling Sb uptake in wheat. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) are widely considered as ecosystem engineers to positively affect the adaptation of plants to adverse environments, but whether AMF can commonly promote root-soil relationships and reproductive allocation is unclear in dryland crops. It is crucial to reveal if AMF can promote grain filling via mediating rhizosphere interactions regardless of soil water regimes and planting densities in dryland crops for the application of AMF regarding more precise management. To address this issue, Funneliformis mosseae was inoculated in pot-cultured wheat to examine the inoculation effects on wheat growth, yield formation, rhizospheric moisture and nutrient status across four planting densities under the drought and well-watered conditions. As expected, under drought stress, wheat grain filling, shoot biomass, rhizospheric soil water content (SWC), water use efficiency (WUE), soil organic carbon (SOC) content and the ratio of SOC to soil total nitrogen (TN) contents were significantly lowered in the soils without AMF inoculation across four planting densities. Under well-watered conditions, the SWC ranged from 10.30 % to 16.64 %, while under water stress conditions, the values ranged from 5.77 % to 9.61 %. Soil mineralized nitrogen (NO 3 −-N and NH 4 +-N) contents were decreased with AMF regardless of soil water status and planting densities. Under drought stress, AMF inoculation increased wheat productivity, WUE, SWC, SOC content, the ratio of SOC to TN, and promoted the absorption of mineralized nitrogen. Particularly, AMF inoculation substantially promoted the soil easily oxidizable organic carbon (EOC) content by 58.5 % and 55.6 % in the two highest densities, respectively. The soil microbial biomass carbon (MBC) content was totally >50 mg kg−1, whereas that of the non-AMF group was <40 mg kg−1. AMF inoculation significantly promoted grain yield up to 28.5 % under drought stress. Such a trend was tightly associated with higher WUE, rhizosphere C/N ratio and labile SOC (EOC and MBC) content owing to the enhanced absorption of soil mineralized nitrogen under drought stress with AMF. The above trend tended to become weaker with increasing densities. However, under a sufficient water supply, AMF inoculation had no such significant effect on the above parameters in low densities, and turned to generate negative effects under high densities (P < 0.05). For example, compared with well-watered, AMF inoculation reduced wheat grain yield and shoot weight by 9.78 % and 4.83 % in high planting density treatment (HC), respectively. Therefore, the inoculation effects of AMF were highly dependent on planting density and soil moisture, and the mechanism was that AMF optimized the rhizospheric interactions for better water and nutrient status under drought and low density. Our findings may provide novel insights into the application of AMF regarding agricultural sustainability under climate change. Conceptual framework depicting the responses of soil nitrogen (N) and carbon (C) cycling processes to different AMF and plant density treatments. The soil N and C cycling processes a) without and with b) AMF inoculation are shown on the left and right, respectively. Solid arrows, C and N fluxes from one pool to another, microbial biomass and activities that mediate the mineralization of soil organic matter (SOM) and soil organic carbon (SOC). The thickness of the arrows indicates the magnitude of the flux or activity. Rectangles, the pool sizes of microbial biomass and mineral N (ammonium and nitrate). Red line, AMF hyphae. Green arrows, root exudation; blue arrows, mineral N flow; orange arrows, microbial respiration; black arrows, the root water uptake; purple arrows, N competition between root and microorganism; yellow arrows, microbial activities that mediate the mineralization of SOM; brown arrows, the interactions. Further explanations are shown in the main text. [Display omitted] • AMF helped the wheat adapt to an arid environment and enhance reproduction. • AMF fostered wheat reproductive derived from optimized WUE and nutrient provision. • The effects of AMF were dependent on water level and plant density. • Our study provides novel insights into the agro-ecosystem sustainable development. [ABSTRACT FROM AUTHOR]

The Mediterranean region is a climate change hotspot due to projected precipitation decreases, soil erosion, and rising temperatures in the coming decades. In such a context, wheat productivity is expected to decrease; thus, all available croplands will need to be farmed to meet food demands. It is therefore crucial to have adequate management to help balance wheat production and environmental protection in specific target environments. With this aim, we present a study in Castile and Leon (Spain) in which we assess conventional and certified organic wheat grain yield and quality. Moreover, we spatially define wheat cultivation suitability categories and monitor crop rotation practices. For wheat suitability mapping, a fuzzy interference system was used to standardize topographic (height and slope), edaphic (pH, soil erosion, organic matter, and texture), and climatic (rainfall and temperature) variables and create marginal, suitable, and very suitable wheat cultivation suitability categories. We calculated the area of wheat suitability categories with confidence intervals and discussed factors affecting productivity. Additionally, we assessed the performance of certified organic and conventionally managed fields with organic and chemical fertilization. We also examined crop rotations using Sentinel-2 data over two seasons (2020−21). Certified organic management yielded 50% less than conventional management in similar environments while maintaining grain quality. Besides management, the main causes of the yield gap are the growing environment and variety. In conventional wheat management, organic fertilization achieves comparable yields to chemical fertilization. Crop rotation practices are uncommon, and over 50% of fields rotate wheat-to-barley or wheat-to-wheat, indicating poor soil management. The region has 25% of cropland classified as marginal wheat croplands (163,503 ± 33,796 ha) producing <2500 kg/ha. Wheat organic agriculture has decreased yield gaps in marginal croplands compared to more productive areas. By farming organic wheat in marginal croplands, the best croplands might be used for more productive farming. • The studied region in Spain has 25% of cropland classified as marginal wheat croplands. • A fuzzy interference system has proven effective to decipher wheat suitability classes. • Sentinel-2 data allowed monitoring the uncommon crop rotation practices in the region. • Certified organic management yielded 50% less than conventional in similar settings. • Wheat organic agriculture has decreased yield gaps in marginal croplands. [ABSTRACT FROM AUTHOR]

Background: Limited data exist on human zinc absorption from wheat biofortified via foliar (FBW) or root (hydroponically fortified wheat, HBW) zinc application. Stable isotope labels added at point of consumption (extrinsic labeling) might not reflect absorption from native zinc obtained by intrinsic labeling.Objectives: We measured fractional and total zinc absorption (FAZ, TAZ) in FBW and HBW wheat, compared with control wheat (CW) and fortified wheat (FW). The effect of labeling method was assessed in HBW (study 1), and the effect of milling extraction rate (EXR, 80% and 100%) in FBW (studies 2 and 3).Methods: Generally healthy adults (n = 71, age: 18-45 y, body mass index: 18.5-25 kg/m2) were allocated to 1 of the studies, in which they served as their own controls. In study 1, men and women consumed wheat porridges colabeled intrinsically and extrinsically with 67Zn and 70Zn. In studies 2 and 3, women consumed wheat flatbreads (chapatis) labeled extrinsically. Zinc absorption was measured with the oral to intravenous tracer ratio method with a 4-wk wash-out period between meals. Data were analyzed with linear mixed models.Results: In study 1 there were no differences in zinc absorption from extrinsic versus intrinsic labels in either FW or HBW. Similarly, FAZ and TAZ from FW and HBW did not differ. TAZ was 70-76% higher in FW and HBW compared with CW (P < 0.01). In studies 2 and 3, TAZ from FW and FBW did not differ but was 20-48% higher compared with CW (P < 0.001). Extraction rate had no effect on TAZ.Conclusions: Colabeling demonstrates that extrinsic zinc isotopic labels can be used to accurately quantify zinc absorption from wheat in humans. Biofortification through foliar zinc application, root zinc application, or fortification provides higher TAZ compared with unfortified wheat. In biofortified wheat, extraction rate (100-80%) has a limited impact on total zinc absorption. These studies were registered on clinicaltrials.gov (NCT01775319). [ABSTRACT FROM AUTHOR]

Responses of wheat yield in different regions to separate and joint changes in mean temperature (T mean), precipitation (Prec), and solar radiation (Rad) are the foundation for sustainable development under climate change; however, these remain uncertain. Here, we quantified the response of wheat yield variation to changes in T mean , Prec, and Rad by building multiple regression models, de-trending analysis, and MK trend tests. Multi-site meteorological data was obtained from different wheat growing areas and corresponding wheat yield data from provinces. Our findings showed that the effects of different climatic elements on wheat yield are spatially heterogeneous by region, and the combined effects of climatic elements are higher than those of single elements. Climate change caused an increase of 1% and 4.52% in winter and winter-spring wheat areas, respectively, and a loss of 3.72% in spring wheat areas. In addition, the effect of single meteorological factors on wheat yield varied depending on their rate of change. Rad and Prec are the main drivers of wheat yield variability; in winter, spring and winter-spring wheat areas, Rad significantly impacted wheat yield by 0.89%, −1.18%, and 2.46%, respectively, and Prec caused changes of −0.18%, −1.54%, and 1.33%, respectively. Considering normal and extreme climate fluctuations, we found that the contribution of climate change to wheat yield changes in different wheat-growing regions reached 26.1%, 18.6%, and 16.7%, respectively. Our results indicate that the overall climatic conditions in the spring wheat region are moving in an unfavourable direction, while Prec variability has become the main limiting factor for wheat production in the current winter and winter-spring wheat regions. These comprehensive and detailed findings provide a reference for the development of more accurate adaptation strategies and management measures in different regions. Going forward, the results of this study can be better applied to the development of region-specific climate-smart agricultural practices and extended to other crops and regions to better understand the broader impacts of climate change on global food security. [Display omitted] • Spatial differences in crop yield responses to different climate drivers at large scales. • We quantified the effects of climate variation on wheat yield variability. • Uniquely illustrates spatial patterns in the relationship between climate variability and wheat yield variability. • Uncovering the main drivers of climate change impact on wheat yield in China. • Light-efficient varieties and effective irrigation may improve China's wheat yield. [ABSTRACT FROM AUTHOR]

Droughts and water scarcity represent crucial issues for the agricultural sector, whose water demand is expected to increase in the next years to ensure food production for a growing population without expanding harvested areas. A proper assessment of crop water needs during the various plant growth periods provides useful information to effectively allocate water resources, highlighting the most drought-sensitive phenological stages. Winter wheat is the most widely grown cereal in the world. In Italy, it is mainly cultivated in the Po Plain, where the most severe drought of the last seventy years occurred in the summer of 2022. This work develops winter wheat drought vulnerability curves tailored to the Po Plain. The curves establish a relationship between water deficit and yield losses during various crop growth stages (establishment, vegetative, flowering and yield formation) and combinations of consecutive phases. Prediction bounds have been associated to vulnerability curves to assess the range of variation of the wheat response to water deficit. The effect of soil texture on wheat tolerance to droughts leading to water stress was investigated too. Results show that the flowering phase and the combinations including this growth stage are more sensitive to water stress with respect to the other considered phenological phases. In addition, winter wheat cultivated over clay loam soil is less sensitive to water deficit in comparison to the same crop grown on the other soil types considered in the study. The developed curves could be used in the framework of parametric insurance programs to pay indemnities based on the crop phenological stage and the soil texture. Moreover, the curves might be useful for land reclamation consortia, who could exploit them to schedule water delivery to farmers during drought or could be embedded into a drought early warning system to provide forecasts of crop yield losses in relation to the weather conditions. • Implementation of stage-specific drought vulnerability curves for winter wheat. • Design of wheat drought vulnerability curves for combinations of growth stages. • Derivation of 95% prediction bounds for the developed curves. • Flowering is the most sensitive stage to water stress. • Winter wheat grown on a clay loam soil tolerates better water stress. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management. [Display omitted] • Soil moisture was estimated in rain-fed and irrigated wheat fields using Sentinel-1 data. • Three change detection methods were evaluated. • Different methodological alternatives were proposed for applying change detection methods at the field scale. • The results of two methods improved when correcting for the vegetation influence on backscatter. • Soil moisture was generally overestimated for dry conditions. [ABSTRACT FROM AUTHOR]

Wheat is a strategic crop for Iran with respect to food security and environmental impact. In the northeastern Golestan Province irrigated and rainfed wheat production systems co-exist. In the light of climate change the comparative assessment of the two systems regarding productivity and sustainability is of urgent importance. This paper aims to contribute to a more informed debate on the viability of irrigation in marginal crop production regions. For this purpose, the present study investigates productivity and environmental impact of 540 wheat producing farm households (259 irrigated; 281 rainfed) in Golestan by partial life cycle assessment (LCA) methodology using greenhouse gas (GHG) emissions and product carbon footprint (PCF) as environmental sustainability indicators. The study finds huge heterogeneity among wheat producers with regard to yield, GHG emissions and PCF. Comparing irrigated and rainfed production showed that irrigation in average leads to 22% higher yields. However, the environmental impact of irrigated production is disproportionally high leading to 110% higher GHG emissions and 62% higher PCF, which highlights the trade-off between productivity and environmental sustainability. The major contributors to total GHG emissions of wheat production are energy for irrigation (only in irrigated production), N 2 O emissions related to fertilization and residue handling, diesel for machinery, and emissions related to fertilizer production and transport. Increased GHG emissions and PCF in irrigated compared to rainfed production are not only attributed to the energy required for irrigation, but also to increased fertilizer and other production inputs. Splitting the farmers into high, medium and low yield groups indicates the potential for increasing yields through increased input intensity without negative effects on PCF. The study's results demonstrate the potential of better balancing the trade-offs between productivity and sustainability of wheat production in marginal wheat production regions of the world. [ABSTRACT FROM AUTHOR]

Drought often causes tremendous agricultural impacts and economic losses, but drought impacts are difficult to assess and quantify. Improving drought monitoring and early warning systems by linking indicators to impacts can reduce social vulnerability. However, it is still challenging to establish quantitative links between drought indicators and impacts during drought stress. In this study, using experimental observation records during drought in different climatic zones in China, we established the quantitative links between the drought indicator based on soil moisture and the affected biological parameters of wheat (Triticum aestivum L.), determined the thresholds of the onset and severity of the impact of drought on wheat, and found that the onset thresholds had a convergence across climate zones and the severity thresholds were different in different climate zones. This study may provide a new understanding for evaluating drought stress or severity and provide an important reference to better understand the hazard itself and carry out impact-based drought monitoring and early warning. • The onset and severity thresholds of drought impacts on wheat (Triticum aestivum L.) were determined. • The onset threshold of drought impact on wheat converges to 50% in different climatic zones. • The severity thresholds of drought were different in different climate zones. [ABSTRACT FROM AUTHOR]

Precision farming has become a hot research topic in recent years due to the advancement of sensing technologies, increased computer performance, and advanced deep learning algorithms. As a result, several outstanding studies on deep learning applications to high-throughput phenotyping of wheat, one of the most demanding cereal crops on the planet, have been published. This paper aims to conduct a survey of publications that have used deep learning techniques to address various challenges in wheat production. To accomplish this, we propose an ontology-based knowledge management system that is specifically designed to highlight the publications' objectives, preprocessing algorithms, deep learning models, frameworks, datasets, and results. The presented ontology is intended to serve as a robust tool for future research in wheat high-throughput phenotyping. Additionally, we compare the performance of deep learning algorithms to that of long-established methods in this field. Compared to traditional machine learning techniques, this study demonstrates that deep learning algorithms provide a more robust, accurate, and cost-effective way of measuring wheat traits. • In recent years, soft computing has become a trending research field in agriculture. • Many studies have used deep learning in high-throughput wheat phenotyping. • Wheat precision farming can benefit from ontology-based knowledge management. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important cereal crops for human consumption. Understanding the eco-efficiency of wheat production is helpful in achieving sustainable agriculture. In this study, the eco-efficiency of wheat production in Japan at a regional scale was measured using a combined methodology of life cycle assessment and data envelopment analysis. Wheat production in paddy fields with inferior drainage performance was compared with production in upland fields with good growing conditions. The eco-efficiency indicator was defined as wheat yield per single environmental impact index, including global warming as a global environmental category and aquatic eutrophication as a regional one. The results indicate that eco-efficient wheat production can occur in both good and poor growing conditions, with a wheat yield obtained by an adequate application of nitrogen fertilizer. The mitigation of aquatic eutrophication caused by the excessive use of nitrogen fertilizer is an important factor in improving the eco-efficiency of wheat production. [ABSTRACT FROM AUTHOR]

• Crop production at various scales is constrained by climatic and non-climatic variables. • In Morocco wheat stands out as the most studied crop. • AquaCrop process-based crop model and regression-based models are the most frequently calibrated crop models. • Precipitation, temperature, soil properties, irrigation, and fertilization are frequently calibrated indicators. • Empirical models aptly integrate climatic and socioeconomic variables. • Process-based models focus on mostly climatic and biophysical variables. Agriculture is an important sector of the Moroccan economy, employing a huge portion of the Moroccan population and contributing about 14 - 20% to the country's GDP. Unfortunately, agricultural production in Morocco is impacted by climatic, non-climatic, biophysical, and non-biophysical stressors. Researchers have employed various crop models to understand how different crops respond to different environmental conditions such as temperature, precipitation, soil properties, fertilization, and irrigation. Unfortunately, there are no studies that provide a summary and a holistic perspective of the most frequently used models and their calibration inputs in Morocco. This work, therefore, seeks to fill these knowledge gaps by providing a summary of the most calibrated crop models, their calibration input data, the most frequently studied crops, how the studies are published (peer-review or grey literature), and the affiliations of the lead authors. This is achieved through a systematic review of the primary peer review and grey literature. A total of 68 relevant peer review and grey literature papers were considered. The results show that most of the authors are affiliated with Moroccan universities/organizations while wheat is the most studied crop. In addition, the AQUACROP and the regression-based models are the most used crop models. Additionally, most of the models are calibrated in order of importance with variables such as temperature, precipitation, soil properties, irrigation, and fertilizers. On the other hand, there is an observed increase in the use of non-climatic indicators such as poverty, farm income, and literacy levels to fit empirical models. It is still unclear how process-based models will integrate socio-economic indicators. This work has implications for future research as it provides a holistic picture of the key models that are currently used and their calibration. This information can be used by other projects to select methods to use, and crops to study based on the available data when working on crop models in Morocco, and North Africa. These results underscore the leading role in research funding offered by the government of Morocco and other organizations such as UM6P and OCP Africa in research valorization in Morocco and Africa. [ABSTRACT FROM AUTHOR]

Agriculture production contributes to global warming directly via the release of carbon dioxide (CO 2 ), methane and nitrous oxide emissions, and indirectly through the consumption of inputs such as fertilizer, fuel and herbicides. We investigated if including a grain legume ( Lupinus angustifolius ) in a cropping rotation, and/or applying agricultural lime to increase the pH of an acidic soil, decreased greenhouse gas (GHG) emissions from wheat production in a semi-arid environment by conducting a streamlined life cycle assessment analysis that utilized in situ GHG emission measurements, rather than international default values. We also assessed the economic viability of each GHG mitigation strategy. Incorporating a grain legume in a two year cropping rotation decreased GHG emissions from wheat production by 56% on a per hectare basis, and 35% on a per tonne of wheat basis, primarily by lowering nitrogen fertilizer inputs. However, a large incentive ($93 per tonne of carbon dioxide equivalents reduced) was required for the inclusion of grain legumes to be financially attractive. Applying lime was profitable but increased GHG emissions by varying amounts depending upon whether the lime was assumed to dissolve over one, five or 10 years. We recommend further investigating the impact of liming on both CO 2 and non-CO 2 emissions to accurately account for its effect on GHG emissions from agricultural production. [ABSTRACT FROM AUTHOR]

Plans outlined in the conference of parties (COP) 26 included the restoration of degraded lands as one of the targets for achieving long-term food sustainability under climate change. The experiment aimed to assess carbon dioxide (CO 2) sequestration and improvement in soil physicochemical properties of agriculture ecosystem models. The results of the experiment shows that the bulk density (ρb) and particle density (ρd) were significantly influenced at both depths 0–10 and 10–20 cm in developed ecosystems. The lowest (1.36 g cc−1) and highest (1.57 g cc−1) values of ρb were recorded at the depth of 0–10 cm in forest land (FL) and mono-cropping rice (Oryza sativa) cultivation land (MCRCL), respectively. The minimum values (kg ha−1) of N (161.27), P (14.87), and K (152.07) were recorded at the depth of 0–10 cm in mono-cropping wheat (Triticum aestivum) cultivated land (MCWCL), guava (Psidium guajava) + green gram (Vigna radiata) cultivation land (GGCL), and MCRCL, respectively. Moreover, the maximum N (207.60 kg ha−1) and P (19.27 kg ha−1) were recorded at the depth of 0–10 in FL system, and K (204.60 kg ha−1) in Karonda (Carissa carandas) cultivation land (KCL). The minimum soil N (158.93 kg ha−1) was recorded in pasture land (PL), while P (13.37 kg ha−1) and K (146.0 kg ha−1) was found in MCRCL system at the depth of 10–20 cm. While the highest soil organic carbon (SOC) stock was recorded in FL (18.40 Mg ha−1) and least in MCWCL system (6.57 Mg ha−1). The highest to the lowest value of CO 2 sequestration (Mg ha−1) was found in the FL system (115.06) followed by KCL (41.11), GGCL (38.93), MCWCL (22.10), MCRCL (17.65), PL (9.40), and seasonal pond area (SPA 0.87). Likewise, the highest to lowest value of total C credit (US$) was found in FL (342.03) after that KCL (122.2), GGCL (115.71), MCWCL (65.7), MCRCL (52.45), PL (27.94), and SPA (2.6), respectively. With the creation of agricultural ecosystem models on degraded land, this work gives a roadmap for repairing degraded land, enhance the terrestrial CO 2 sequestration, C-credit, and boosting ecological services, which may contribute to attaining long-term food sustainability. • At a depth of 0–20 cm, all physiochemical characteristics were altered. • NPK range was 207.60–19.27-18.10 kg ha−1 in agriculture models. • The FL has the highest rate of CO 2 sequestration (115.06 Mg ha−1). [ABSTRACT FROM AUTHOR]

Abstract: Understanding water and nutrient utilization in agricultural layered soil is very important for the improvement of agricultural management and protection of the environment. The objective of this paper is to determine the effect of layered soil profiles on crop yield and water and fertilizer nitrogen (N) utilization. Firstly a water and nitrogen management model (WNMM) was calibrated and validated under a wheat–maize cropping system in an alluvial plain using published data of two soil profiles (named A and B). The results showed that the model can be used to simulate the water movement and N transport, as well as crop growth in the study area. Then, another three very different soil profiles (named C, D and E) near to profiles A and B were studied. The profile C had a silt loam–clay profile; the profile D had a silty loam–clay–silt profile and the profile E had a clay–silty loam–silt profile. The soil hydraulic parameters of these profiles were obtained by pedo-transfer functions using measured soil properties. Given the same initial conditions and field management practices, the WNMM was then used to simulate water and Nitrogen balance and crop yield for the three profiles. Simulated results showed that the profiles (C, D and E) have similar water and fertilizer N use efficiencies (WUE, FNUE), while the crop yields of profiles C and D were higher than that of the profile E. Compared with profiles D and E, the profile C has least water drainage (127 mm) and total N loss (117 kg N ha−1). We concluded that layering of soil profiles has a strong effect on water and fertilizer N utilization and crop yield. Such an effect of layered property of soil profiles, therefore, needs to be taken into account in precision agriculture related research. [Copyright &y& Elsevier]

Abstract: Diverse vulnerabilities of Bangladesh''s agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario. [Copyright &y& Elsevier]

Abstract: Biofuels could become increasingly important for agriculture; however there is growing concern regarding the possible environmental drawbacks due to the risks of increased inputs during crop cultivation. These risks need to be evaluated in order to assess the best management practices. In this study, a life cycle assessment (LCA) was carried out: (i) to evaluate the environmental impacts of three cropping systems characterized by different external input levels (low S1, medium S2 and high S3) applied to sunflower and maize, both in rotation with wheat, in a Mediterranean region; (ii) to estimate the environmental benefits of the optimization of cropping systems for energy management. Output–input ratio, net energy balance, global warming potential (GWP), eutrophication potential (EP) and acidification potential (AP) were used as LCA impact categories. Data from cropping systems (external input and crop yields) were collected from a long-term experiment carried out in the coastal plain of Tuscany; data regarding fertilizers, machinery and pesticide production were taken from literature. The results obtained showed S1 with the highest output–input ratios and the lowest impact for the selected impact categories. The other cropping systems S2 and S3 showed limited differences between them for all the impact categories evaluated. Fertilizer use and application, irrigation and machinery use caused most of the environmental impacts and energy consumption. The allocation procedure, showing residues as co-products, had a strong influence on the overall efficiency of agricultural systems. [Copyright &y& Elsevier]

Abstract: This study was carried out in Gezira scheme (season 2007/08) to measure the farmer’s technical efficiency of producing wheat and to determine the main socio-economic factors affecting farmer’s technical efficiency of wheat production. The stochastic production frontier model was employed to achieve the study objectives. Primary data was collected from a random sample of 60 farmers in the Gezira scheme by mean of a structured questionnaire. The primary data was supplemented by secondary data collected from different relevant sources. The study results showed that the mean technical efficiency of wheat production is 63% which means that wheat production could have been increased by 37% at the same level of inputs, had resources efficiently utilized. The main socio-economic factors determining the farmer’s technical efficiency appeared to be: the timing of the different agricultural operations, irrigation and land ownership. To improve wheat production technical efficiency, the study recommended usage of wheat improved varieties and application of the different agricultural operations, particularly land preparation and irrigation, at the optimum timing. [Copyright &y& Elsevier]

Abstract: This paper based on the measured of light intensity and wheat production within the shelterbelt, with surfer graphics software, correlation analysis and regression analysis of classical statistical methods to analyze relation between wheat yield and light intensity within agro-forestry system. The results showed that the order of average light intensity was: south>west>east>north of the shelterbelt. Wheat yields can be divided into four regions: greater than 810g/m2 for the high yield areas; between 740.51 and 809.35g/m2 for the level-producing areas; 600-740.51g/m2 were cut areas; less than 600g/m2 were serious cut areas. Judged by wheat yield divided regions above, wheat yield reduction was not serious, but relatively large distributed of area on the east side of shelterbelt; seriously cut areas mainly distribute in the north of shelterbelt; east and south of shelterbelt almost did not have serious cut areas; The size of correlation between light intensity and yield per unit area was north of shelterbelt (0.850)>south of shelterbelt (0.564)>west of shelterbelt (0.554)>east of shelterbelt (0.474). The relationship between light and yield in north of shelterbelt was consistent with Compound model, the south of shelterbelt is consistent with Exponential model; the scale of shading was: north of shelterbelt was about 0-0.7H, south of shelterbelt was about 0-0.3H, east of shelterbelt was about 0-0.3H, west of shelterbelt was about 0-0.5H. [Copyright &y& Elsevier]

Abstract: The distribution of natural radionuclides was studied in winter wheat plants collected from three sites in Belgium during 2004–2007. Activity concentrations of 40K, 226Ra, 228Ra and 228Th in organically and conventionally grown wheat, and in the corresponding soil samples, were determined using ultra low-level gamma-ray spectrometry. The observed soil-to-wheat concentration ratios were calculated for the different parts of the wheat plant (root, stem and grain) in the two agricultural systems (organic and conventional). There were large variations in radionuclide activity concentrations between the sites and fields, but no significant difference between conventionally and organically grown wheat plants was observed. [ABSTRACT FROM AUTHOR]

Abstract: Soil organic matter (SOM) is the main energy resource of the great diversity of soil invertebrates but knowledge on the contribution of SOM pools of different ages to soil animal nutrition is sparse. In the present study natural stable isotope ratios were used to investigate the incorporation of decade old wheat-borne carbon into soil invertebrates of different trophic levels of a maize field 27 years after the replacement of wheat. For comparison a nearby continuous wheat field was investigated. Although the soil food web of the wheat and the maize field consisted of similar invertebrate taxa, most taxa were significantly more abundant in the wheat than in the maize field confirming that C4 plants are generally of poor food quality to soil animals due to low nutrient and high fibre contents. However, different cultivation strategies may also have contributed to differences in the abundance of invertebrates between both study sites. The mean incorporation of C3 carbon in soil animals in the maize field was 60.6±27.8%, but significantly differed between invertebrate species. Unfortunately, the field size did not entirely exclude incorporation of C3 carbon into the tissue of large and mobile invertebrates by feeding on C3 resources outside of the maize field. However, less mobile species such as endogeic earthworms incorporated high amounts of C3 carbon presumably due to the mobilisation of old carbon pools enclosed in soil aggregates and inaccessible to other soil invertebrates during gut passage. Furthermore, small and less mobile invertebrate species, such as most hemi- and epiedaphic Collembolans, also incorporated high amounts of C3 carbon likely by feeding on saprophytic fungi and microorganisms. In contrast, euedaphic Collembola incorporated only small amounts of C3 carbon suggesting preferentially feeding on maize resources. Overall, our data suggest that decade old carbon resources form an important component of the soil animal food web, but that the exploitation of old carbon resources by animal species varies with their distribution within the soil matrix, trophic position and exploitation of algae, and as an artefact to the small size of the study site, animal mobility. [Copyright &y& Elsevier]

Abstract: A streamlined hybrid life cycle assessment is conducted to compare the global warming potential (GWP) and primary energy use of conventional and organic wheat production and delivery in the US. Impact differences from agricultural inputs, grain farming, and transport processes are estimated. The GWP of a 1kg loaf of organic wheat bread is about 30g CO2-eq less than the conventional loaf. When organic wheat is shipped 420km farther to market, organic and conventional wheat systems have similar impacts. These results can change dramatically depending on soil carbon accumulation and nitrous oxide emissions from the two systems. Key parameters and their variability are discussed to provide producers, wholesale and retail consumers, and policymakers metrics to align their decisions with low-carbon objectives. [Copyright &y& Elsevier]

Abstract: Life cycle assessment methodology was used to compare conventional wheat production with a scenario where source-separated human urine replaced mineral fertilisers. A change-orientated perspective was used, including differences in capital goods between the scenarios. An optimal fertilising strategy regarding application time, technique and substitution of mineral fertiliser was demonstrated to be important for energy use, global warming and acidification. For reducing the energy use, a well designed collection system for urine also proved important, while recovery of the urine was essential for reducing eutrophication. Applying an agricultural perspective when evaluating the system highlighted potential conflicts regarding nutrient utilisation. [Copyright &y& Elsevier]

Abstract: Drought is a world-spread problem seriously influencing crop production and quality, the loss of which is the total for other natural disasters, with increasing global climate change making the situation more serious. Wheat is the staple food for more than 35% of world population, so wheat anti-drought physiology study is of importance to wheat production and biological breeding for the sake of coping with abiotic and biotic conditions. Much research is involved in this hot topic, but the pace of progress is not so large because of drought resistance being a multiple-gene-control quantitative character and wheat genome being larger (16,000Mb). On the other hand, stress adaptive mechanisms are quite different, with stress degree, different growth and developmental stages, time course, materials and experimental plots, thus increasing the complexity of the issue in question. Additionally, a little study is related to the whole life circle of wheat, which cannot provide a comprehensive understanding of its anti-drought machinery. We selected 10 kinds of wheat genotypes as materials, which have potential to be applied in practice, and measured change of relative physiological indices through wheat whole growing developmental circle (i.e. seedling, tillage and maturing). Here, we reported the dynamic anti-oxidative results of whole stage (i.e. seedling, tillage and maturing) in terms of activities of POD, SOD, CAT of 10 wheat genotypes as follows: (1) 10 wheat genotypes can be grouped into three kinds (A, B and C, respectively) according to their changing trend of the measured indices; (2) A group performed better resistance drought under the condition of treatment level 1, whose activities of anti-oxidative enzymes (POD, SOD, CAT) were higher; (3) B group exhibited stronger anti-drought under treatment level 2, whose activities of anti-oxidative enzymes were higher; (4) C group expressed anti-drought to some extent under treatment level 3, whose activities of anti-oxidative enzymes were stronger, MDA lower; (5) these results demonstrated that different wheat genotypes have different physiological mechanisms to adapt themselves to changing drought stress, whose molecular basis is discrete gene expression profiling (transcriptom); (6) our results also showed that the concept and method accepted and adopted by most researchers – that 75% FC is a proper supply for higher plants – was doubted because this level could not reflect the true suitable level of different wheat genotypes; (7) our research can provide insights into physiological mechanisms of crop anti-drought and direct practical materials for wheat anti-drought breeding; (8) POD, SOD and CAT activities of different wheat genotypes had quite different changing trend at different stages and under different soil water stress conditions, which was linked with their origin of cultivation and individual soil water stress threshold; (9) our primary results also firstly displayed that the changing trend for wheat adapting to environmental stress during life circle was an S-shaped curve, which is, by chance, consistent with Plant Growth Grand Periodicity Curve. [Copyright &y& Elsevier]

Six open-top chamber experiments with field-grown wheat Triticum aestivum L. (five with spring wheat and one with winter wheat) were combined to test which of the two ozone exposure indices, AOT40 and CFO3, that provided the most consistent relationship between relative yield loss and ozone exposure. AOT40 is the accumulated exposure over a threshold ozone concentration of 40 nl l-1, while CFO3 is the cumulative flux of ozone (uptake) to the flag leaves. The ozone uptake of the flag leaves was estimated using a stomatal conductance model, sensitive to phenology, light, vapour pressure deficit (VPD) and temperature in combination with measurements of the boundary layer conductance in the open-top chambers. Both indices were calculated for the grain-filling period, defined as the time from anthesis until 2 weeks before harvest. The duration of the grain-filling period was shown to be closely related to the rate of accumulation of thermal time above a base temperature of 0 deg. C. The CFO3 index provided a much more consistent pattern in terms ofozone effects compared to the AOT40 index. This was especially the case for spring wheat, for which a linear regression between relative yield and CFO3 using all five data sets is presented. According to the stomatal conductance model, VPD limited daytime stomatalconductance in warm and dry years, while temperature was the most important limiting factor during daytime in cool and humid years. The effect of light was mainly to delimit the time period of the day during which substantial uptake of ozone took place. It is concluded that,compared to the AOT40 index, the more mechanistically relevant flux-based index CFO3 will estimate larger yield loss in the relatively humid parts of western and northern Europe, while smaller yield loss will be estimated for the dry summer climates in south and central Europe. The use of an ozone flux threshold, similar to the cut-of [ABSTRACT FROM AUTHOR]

CERES-Wheat, a dynamic process crop growth model, is specified and validated for seven sites in the major wheat-growing regions of Spain.Variables explaining a significant proportion of simulated yield variance are crop water (sum of precipitation and irrigation) and temperature during the growing season. A multiple linear regression model is developed to represent simulated yield response to these variables.Seven agro-climatic regions are defined based on K-mean cluster analysis of temperature and precipitation data from 329 meteorological stations and provincial crop yield data. The yield functions derived from the validated crop model were then used with the gridded agro-climatic database to conduct a spatial analysis of climate change impactson national wheat production. Climate change scenarios with and without sulfate aerosols developed from the Hadley Centre (HCGG and HCGS)and Canadian Climate Centre (CCGG and CCGC) are tested. [ABSTRACT FROM AUTHOR]

Soil emissions of nitrous oxide (N2O) were measured usingstatic field chambers, which were installed in a wheat field. The treatments were: open-top chambers with ambient CO2 concentrations (OTC350), open-top chambers with 700 ppm CO2 (OTC700) and ambient air plots without open-top chambers (AA). Measurementsof N2O emissions were made weekly starting at anthesis. The measurements continued for ten weeks, until two weeks after the harvest of the mature crop. During the first eight weeks the N2O emissions were higher in the OTC350 treatment compared to OTC700. At the last two measurements, after the plants were harvested, the N2O emissions of the chamber treatments were similar to each other and higher than during the preceding period. The accumulation of grain protein per unit area was higher in OTC700 compared with OTC350. These results suggest that a competition for soil nitrogen exists between plants and the microbial community. The AA plots emitted less N2O during the green canopy period compared with the chamber treatments. After harvest, the emissions from AA increased upto the same magnitude as the chamber treatments. The lower emissionsof the ambient air plots during the pre-harvest period can be explained partly by lower ambient temperatures and drier soil. [ABSTRACT FROM AUTHOR]