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To feed a rapidly growing human population in an era of climate change and increasingly uncertain weather—such as drought and flood—humanity will need crops with increased yields, greater resiliency to extreme weather, and more resistance to disease. Appropriately enough in the Century of Biology, that means turning to genetic tools such as CRISPR. [ABSTRACT FROM AUTHOR]

Dmitry Kurouski, an associate professor of biomedical engineering in the department of Biochemistry and Biophysics at Texas A&M University in College Station, Texas, USA, recently spoke to Spectroscopy about Raman spectroscopy's role in determining crop yield of key food items as the world population continues to increase. His paper on the subject, 'Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming,' co-authored by student Charles Farber, appeared in the journal Frontiers in Plant Science in April 2022 (1). The following is transcribed from an online video interview between Spectroscopy and Kurouski in January 2024 with the assistance of artificial intelligence (AI) software, and has been lightly edited for length and clarity., Please briefly summarize the problem you were examining in this study and its implications in real-world applications. At its heart, the concern here is the issue of crop yield, as [...]

BACKGROUND: Poly‐γ‐glutamic acid (γ‐PGA) is employed extensively in agriculture to enhance soil water retention; however, the underlying mechanism by which γ‐PGA improves soil structure and soybean productivity in arid regions remains poorly understood. A micro‐scale field experiment was conducted in the arid region of northwest China, employing five concentrations of γ‐PGA to investigate its impacts on soybean yield, photosynthesis, and water‐use efficiency, as well as soil aggregates and water distribution. The five levels of γ‐PGA were 0 (CK), 10 (P1), 20 (P2), 40 (P3), and 80 kg ha−1 (P4). RESULTS: The results demonstrated that the application of γ‐PGA significantly improved soybean yield, photosynthesis, and chlorophyll content. It resulted in a decrease in soil aggregate content with a maximum diameter of less than 0.053 mm and an increase in the stability of soil aggregates in the uppermost layer of the soil (0–30 cm). The application of γ‐PGA significantly increased soil water content, particularly in the uppermost layer of the soil, and effectively reduced water consumption and improving water use efficiency in soybeans. Overall, the P3 treatment exhibited the most pronounced improvement of soybean yield, photosynthesis, water‐use efficiency, as well as distribution of soil aggregates and water. The correlation matrix heatmap also revealed a strong correlation between improvement of soybean yield or photosynthesis at various γ‐PGA application levels and the enhancement of soil stability or soil water content. CONCLUSION: The multivariate regression analysis revealed that an optimal application level of 46 kg ha−1γ‐PGA could enhance effectively both yield and water use efficiency of soybean in the arid region of northwest China. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Low recovery of conventional fertilizers remains a significant bottleneck for maize production globally. In particular, with phosphate fertilization, zinc (Zn) is prone to precipitation in soil, reducing recovery of both phosphorus (P) and Zn by maize. RESULTS: The present study was designed to investigate the synergistic effect of zinc oxide (ZnO) nanoparticles (NPs) and P on maize crop growth, yield, and nutrient uptake under ZnO seed coating and foliar application in a randomized complete block design. However, plants were subjected to two ZnO NPs levels (0.5 and 12 kg ha−1) amended with two P levels (45 and 90 kg ha−1). ZnO NPs, especially in the form of foliar application, with a P dose of 90 Kg ha−1 significantly (P < 0.05) improved maize crop growth, yield, and nutrient uptake compared with control. In comparison with the control group, plants grown in these conditions absorbed higher levels of Zn and P. Zn uptake rose to 16.34 g ha−1, 137.88 g ha−1, and 166.89 g ha−1 in roots, grains, and stover respectively, and P uptake increased to 0.80 mg kg−1, 10.066 mg kg−1, and 12.17 mg kg−1 respectively. Additionally, seed emergence rate, plant height, and cob length increased by up to 2%, 1177 cm2, and 3.3 cm respectively compared with control. Furthermore, Zn use efficiency was increased up to 38.55% in ZnO NPs foliar application. CONCLUSIONS: Application of ZnO NPs at 0.5 kg ha−1 in the form of foliar application with 90 kg ha−1 P dose produced a more pronounced increment in the parameters studied than ZnO NPs seed coating did. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The quality of soil plays a crucial role in sustainable agriculture. To better understand the changes in soil quality across different agroecosystems, long‐term experiments are considered as the most effective approach. Moreover in spite of their significance, there is paucity of information pertaining to long‐term impact of fertilizer and manure application on soil quality in Vertisols of Central India. Thus, the present study aimed to assess the soil quality in an ongoing 46‐year long‐term fertilizer experiment conducted in central India, where a soybean‐wheat cropping system (SWCY) was implemented on a Vertisol. The experiment involved the application of inorganic and integrated nutrient treatments, including farmyard manure (FYM). By analyzing various biological and physiochemical properties of the soil, a soil quality index (SQI) was developed. The SQI encompassed treatments with both optimal and suboptimal nutrient dosages, revealing the range of soil quality variations resulting from different nutrient applications over the long term. The study found a significant correlation between the SQI values and the grain yields of soybean (R2 = 0.85**) and wheat (R2 = 0.85**). The main indicators identified for assessing soil quality were large macro‐aggregates, dehydrogenase activity, microbial biomass nitrogen and carbon, and available nitrogen. Overall, the findings highlighted the importance of soil biological and physiochemical properties in determining soil quality, emphasizing the significance of integrating both inorganic and organic sources of nutrients for improving soil quality and crop productivity of soybean and wheat sequence in Vertisols of Central India. [ABSTRACT FROM AUTHOR]

Agriculture is a highly important sector of the Indian economy with half of the individuals relied on the subsistence of food crop production. Since agriculture is an emerging field in the economic world, crop identification, crop prediction, and classification, it reached huge attention. Particularly, crop yield prediction attracts scholars to develop a model of it to help the country in an economical way. Due to the vulnerability of the environment and soil conditions including soil quality, humidity, rainfall, and temperature as well. Farmers are in the position to make the decision of crop cultivation, monitoring its growth and harvesting time. Yet, the fluctuation of the climatic state makes it critical for farmers to predict crop yields. Also, the less moisture in the soil misleads the prediction results. These all come to the end of determining the relationship between food security and import–export strategy. Hence, the development of novel prediction systems is necessitated in agriculture industries. To conquer such shortcomings, a hybridized deep learning model is developed for crop yield prediction. Firstly, the required raw data is collected from benchmark datasets and utilized for subsequent sections. Secondly, the noteworthy features are extracted by using the autoencoder, statistical feature, and Principle Component Analysis (PCA). Thirdly, these resultant features are offered to acquire the optimal weighted features, where weight is optimized by proposing the novel algorithm as Fitness-derived Adaptive Harris Hawks Optimization (FA-HHO). Finally, the obtained weighted features are subjected to Hybrid Deep Learning (HDL), which is constructed with the 1-Dimensional Convolutional Neural Network (1-DCNN) with a Long Short-Term Memory (LSTM) layer and Deep Temporal Convolutional Neural Network (DTCNN). Further attaining the optimal values, the hyperparameters are tuned optimally by the FA-HHO approach. The final prediction rate is estimated by taking the average value. The performance of the prediction model is evaluated using diversification of parameters and compared against baseline approaches. Hence, the findings reveal that it contains the potential to predict the crop yield to help the farmers to increase productivity. [ABSTRACT FROM AUTHOR]

One of the most damaging obstacles to crop production is weeds; weeds pose a serious risk to agricultural output. Due to the homogenous morphological properties of weeds, farmers are unable to identify and classify the weed leaves.This study can aid farmers in identifying, categorizing, and quantifying the true extent of crop yield reduction. Computer vision is a sophisticated technique widely used for weed and crop leaf identification and detection in the agricultural field. This work has used three different datasets, such as 'Deep Weed', 'Crop Weed Filed Image Dataset (CWFID), and Multi-view Image Dataset for Weed Detection in Wheat Field (MMIDDWF), and collected 5090 images for training the model. This work uses segmentation techniques for vegetation and semantics for weed object detection. Furthermore, the masked image is distributed as small tiles; often the patches are square tiles, as in 25 × 25 (px), 50 × 50 (px), and 100 × 100 (px). This work has proposed a Deep Learning segmentation model named 'Pyramid Scene Parsing Network-USegNet' (PSPUSegNet) for data classification and compared the accuracy of the data from existing segmentation models such as UNet, SegNet, and USegNet. The suggested model, PSPUSegNet, obtained 96.98% precision, 97.98% recall, and 98.96% data accuracy in the Deep Weed dataset. The proposed model has self-supervised in term of deep learning mechanism.Our findings demonstrate that the deep weed dataset has achieved greater data accuracy compared to the CWFID and MMIDDWF datasets. The findings support the effectiveness of the suggested approach for weed species recognition. [ABSTRACT FROM AUTHOR]

SUMMARY Seeds are initiated from the carpel margin meristem (CMM) and high seed yield is top one of breeding objectives for many crops. β‐1,3‐glucanases play various roles in plant growth and developmental processes; however, whether it participates in CMM development and seed formation remains largely unknown. Here, we identified a β‐1,3‐glucanase gene (GLU19) as a determinant of CMM callose deposition and seed yield in cotton. GLU19 was differentially expressed in carpel tissues between Gossypium barbadense (Gb) and Gossypium hirsutum (Gh). Based on resequencing data, one interspecies‐specific InDel in the promoter of GLU19 was further detected. The InDel was involved in the binding site of the CRABS CLAW (CRC) transcription factor, a regulator of carpel development. We found that the CRC binding affinity to the GLU19 promoter of G. barbadense was higher than that of G. hirsutum. Since G. barbadense yields fewer seeds than G. hirsutum, we speculated that stronger CRC binding to the GLU19 promoter activated higher expression of GLU19 which in turn suppressed seed production. Consistent with this hypothesis was that the overexpression of GhGLU19 caused reduced seed number, boll weight and less callose formation in CMM. Conversely, GhGLU19‐knockdown (GhGLU19‐KD) cotton led to the opposite phenotypes. By crossing GhGLU19‐KD lines with several G. hirsutum and G. barbadense cotton accessions, all F1 and F2 plants carrying GhGLU19‐KD transgenic loci exhibited higher seed yield than control plants without the locus. The increased seed effect was also found in the down‐regulation of Arabidopsis orthologs lines, indicating that this engineering strategy may improve the seed yield in other crops. [ABSTRACT FROM AUTHOR]

Soybean is one of the most economically important crops worldwide. However, soybean yield can be substantially decreased by many diseases. Soybean genotypes could have different reactions to pathogen infection. As a first step toward investigating the biochemical basis of soybean resistance and susceptibility to disease, phytochemicals in the seeds of 52 soybean genotypes previously reported to have different reactions to diseases of soybean rust (SBR), Phomopsis seed decay (PSD), and purple seed stain (PSS) were analyzed. Using GC-MS, a total of 46 compounds were tentatively identified which included 11 chemical groups. Among those, the major group was esters, followed by carboxylic acid, ketone, and sugar moieties. Compounds having reported antioxidant, anti-microbial, and anti-inflammatory activities were also identified. UHPLC-DAD/MS analysis indicated that there were five major isoflavone components presented in the samples, including daidzin, glycitin, genistin, malonyldaidzin, and malonylglycitin. Isoflavones have been reported to play an important role in defense from plant pathogens. Although there was variance in the isoflavone content among soybean genotypes, those with the SBR resistance Rpp6 gene (PI 567102B, PI 567104B, PI 567129) consistently exhibited the highest concentrations of daidzin, glycitin, genistin, and malonyldaidzin. The SBR resistant genotype, PI 230970 (Rpp2) had the greatest amount of genistin. The SBR resistant genotype, PI 200456 (Rpp5) resistant genotype uniquely contained glycitein, a compound that was absent in the other 51 genotypes examined. A PSD-resistant genotype PI 424324B had nearly four times the amount of stigmasterol as PI 556625, which was susceptible to SBR, PSD, and PSS in our previous tests. Results of this study provide useful information for further investigation of the biochemical basis of soybean resistance to diseases. The results may also aid in selection of soybean lines for breeding for resistance to soybean rust and other diseases. [ABSTRACT FROM AUTHOR]

Summary: Intercropping leads to different plant roots directly influencing belowground processes and has gained interest for its promotion of increased crop yields and resource utilization. However, the precise mechanisms through which the interactions between rhizosphere metabolites and the microbiome contribute to plant production remain ambiguous, thus impeding the understanding of the yield‐enhancing advantages of intercropping.This study conducted field experiments (initiated in 2013) and pot experiments, coupled with multi‐omics analysis, to investigate plant–metabolite–microbiome interactions in the rhizosphere of maize.Field‐based data revealed significant differences in metabolite and microbiome profiles between the rhizosphere soils of maize monoculture and intercropping. In particular, intercropping soils exhibited higher microbial diversity and metabolite chemodiversity. The chemodiversity and composition of rhizosphere metabolites were significantly related to the diversity, community composition, and network complexity of soil microbiomes, and this relationship further impacted plant nutrient uptake. Pot‐based findings demonstrated that the exogenous application of a metabolic mixture comprising key components enriched by intercropping (soyasapogenol B, 6‐hydroxynicotinic acid, lycorine, shikimic acid, and phosphocreatine) significantly enhanced root activity, nutrient content, and biomass of maize in natural soil, but not in sterilized soil.Overall, this study emphasized the significance of rhizosphere metabolite–microbe interactions in enhancing yields in intercropping systems. It can provide new insights into rhizosphere controls within intensive agroecosystems, aiming to enhance crop production and ecosystem services. [ABSTRACT FROM AUTHOR]

Zinc (Zn) is an essential micronutrient that is found in almost all classes of enzymes. Its deficiency is a well-documented problem among cereal crops that causes decreased crop yields and nutritional quality. Therefore, an experiment was implemented in a randomized block design which included the treatments of bacterial inoculation consisting of T1 (Control), T2 (100% RDF;100:40:20), T3(100% RDF + 25 kg ZnSO4.7 H2O), T4(100%RDF+ ZnSB1– Bacillus sp. strain BAU_A2 @750 ml ha−1), T5 (100%RDF+ ZnSB2– Priestia megaterium strain BAU_A3 @750 ml ha−1), T6 (100%RDF+ ZnSB3-Bacillus subtilis strain BAU_A5@750 ml ha−1). The mineral NPK was applied as per treatments. The results indicated the maximum values of water soluble exchangeable Zn and organically bounded Zn in treatments consisting ZnSB1, ZnSB2 and ZnSB3, respectively. The WS+EXCH-Zn was significantly increased by 68.57%, 28.26%, 18%, 98.14%, 41.30%, 30.00% and 128.12%, 55.55%, 37.25%, 43%, 62.22% and 43.13% in first year (2018–19) and second year (2019–2020) under the soil treated with ZnSB1-Peribacillus simplex strain BAU_A1, ZnSB2-Bacillus sp. strain BAU__A2 and ZnSB3-Bacillus subtilis strain BAU_A5 along with 100%RDF over control, application of recommended dose of fertilizer with and without application of 25 kg ZnSO4 ha−1, respectively. However, the maximum decrement in Mn-oxide-bounded zinc (1.12 mg kg−1 soil) was observed with T6 which was significantly higher by 158%, 194.4%, 185.71% over control, 100% RDF with and without application of 25 kg ZnSO4 ha−1, which showed the solubilization of zinc with the activity of ZnSB in soil. The study may be beneficial to mitigate the zinc deficiency in soil and malnutrition in children. [ABSTRACT FROM AUTHOR]

Heavy and costly use of phosphorus (P) fertiliser is often needed to achieve high crop yields, but only a small amount of applied P fertiliser is available to most crop plants. Hakea prostrata (Proteaceae) is endemic to the P‐impoverished landscape of southwest Australia and has several P‐saving traits. We identified 16 members of the Phosphate Transporter 1 (PHT1) gene family (HpPHT1;1‐HpPHT1;12d) in a long‐read genome assembly of H. prostrata. Based on phylogenetics, sequence structure and expression patterns, we classified HpPHT1;1 as potentially involved in Pi uptake from soil and HpPHT1;8 and HpPHT1;9 as potentially involved in Pi uptake and root‐to‐shoot translocation. Three genes, HpPHT1;4, HpPHT1;6 and HpPHT1;8, lacked regulatory PHR1‐binding sites (P1BS) in the promoter regions. Available expression data for HpPHT1;6 and HpPHT1;8 indicated they are not responsive to changes in P supply, potentially contributing to the high P sensitivity of H. prostrata. We also discovered a Proteaceae‐specific clade of closely‐spaced PHT1 genes that lacked conserved genetic architecture among genera, indicating an evolutionary hot spot within the genome. Overall, the genome assembly of H. prostrata provides a much‐needed foundation for understanding the genetic mechanisms of novel adaptations to low P soils in southwest Australian plants. [ABSTRACT FROM AUTHOR]

Molybdenum (Mo) is an essential micronutrient required for plant growth but is prone to leaching from neutral and alkaline soils. The use of slow-release Mo sources could potentially reduce leaching losses from soils and increase crop yields. In this study, we assessed mechanochemistry as a green method to produce slow-release Mo sources. Molybdenum compounds (MoO3 or (NH4)6Mo7O24·4H2O) were mechanochemically (MC) treated with ZnO to synthesize compounds with a Mo content of 1–36%. Reduced Mo solubility after MC treatment, compared to the initial Mo source, was obtained with the MoO3 source and these composites were used for co-compaction with macronutrient fertilizers. Macronutrient pellets with 0.2% Mo were compacted using the 4% Mo and 36% Mo (characterized as ZnMoO4) compounds. A column dissolution test showed that the 4% Mo compound in a macronutrient carrier (DAP and MAP) only released around 40% of the total Mo compared to 80% for a non-MC treated control over 72 h. Column leaching using two soils revealed that the release behavior of Mo was strongly related to the pH of the leachate, which was affected by both the soil pH and the macronutrient carrier. More Mo was released when the MC-treated compound was co-compacted with diammonium phosphate (DAP) compared to monoammonium phosphate (MAP). The MC-treated compound with 4% Mo showed significantly less leaching than the control without ball milling when co-compacted with both MAP and DAP. In a pot trial with simulated leaching, the uptake of Mo was greater for the MC-treated 4% Mo compound co-compacted into DAP than for the other Mo sources. Overall, our results indicate that MC-treated MoO3–ZnO could be used as a slow-release Mo source in high-rainfall areas. [ABSTRACT FROM AUTHOR]

Introduction: Phosphorus nutrition and hormone concentration both affect crop yield formation. Ascertaining the interaction of phosphorus and GA3 has a synergistic effect on the grain yield and phosphorus utilization efficiency of oilseed flax in dryland. It is extremely important for improving grain yield and phosphorus utilization efficiency. Methods: A field experiment was conducted in 2019 and 2020 at the Dingxi Oil Crops Test Station to investigated the effects of phosphorus, gibberellin (GA3), and their interaction on the grain yield and phosphorus-utilization efficiency of oilseed flax plants. Phosphorus fertilizerwas applied at three levels (0, 67.5, 135 kg P2O5·ha-1) and GA3 was also sprayed at three concentrations (0, 15, and 30 mg·L-1). Results: The results showed that application of 67.5 kg P2O5·ha-1 reduced leaves acid phosphatase (ACPase) activity, but increased phosphorus accumulation throughout the growth period, the 1000-kernel weight (TKW), and the number of grains per capsule. Spraying GA3 significantly increased the leaves ACPase activity, phosphorus accumulation after anthesis and its contribution to grain, phosphorus-utilization efficiency, the number of capsules per plant, and TKW. The phosphorus accumulation at the anthesis, kernel, and maturity stages under the treatment of fertilizing 67.5 kg P2O5·ha-1 and spraying 30 mg·L-1 GA3 were increased by 56.06%, 73.51%, and 62.17%, respectively, compared with the control (no phosphorus, no GA3). And the phosphorus accumulation after anthesis and its contribution to grain also increased. 67.5 kg P2O5·ha-1 combined with 30 mg·L-1 GA3 and 135 kg P2O5·ha-1 combined with 15 mg·L-1 GA3 both significantly increased grain yield of oilseed flax, reaching 1696 kg·ha-1 and 1716 kg·ha-1 across two years, respectively. And there was no significant difference between them. However, the former treatment significant increased the apparent utilization rate, agronomic utilization rate, and partial productivity of phosphorus. The interaction between phosphorus and GA3 was significant for grain yield. Conclusion: Therefore, the application of 67.5 kg P2O5·ha-1 in combination with 30 mg·L-1 GA3 is an effective fertilization approach for enhancing oilseed flax growth and grain yield in the experiment region and other similar areas. [ABSTRACT FROM AUTHOR]

Introduction: Environmental conditions play a prime role in the growth and development of plant species, exerting a significant influence on their reproductive capacity. Soybean is sensitive to high temperatures during flowering and seed developmental stages. Little is known about the combined environmental effect of temperature and CO2 on seed yield and quality and its future generation. Methods: A study was conducted to examine the effect of temperature (22/14°C (low), 30/22°C (optimum), and 38/30°C (high)), and CO2 (420 ppm (ambient; aCO2) and 720 ppm (elevated; eCO2)) on seed yield, quality, and transgenerational seedling vigor traits of soybean cultivars (DS25-1 and DS31-243) using Soil-Plant-Atmospheric-Research facility. Results: A significant temperature effect was recorded among yield and quality attributes. At high-temperature, the 100-seed weights of DS25-1 and DS31-243 declined by 40% and 24%, respectively, over the optimum temperature at aCO2. The harvest index of varieties reduced by 70% when exposed to high temperature under both aCO2 and eCO2, compared to the optimum temperature at aCO2. The seed oil (- 2%) and protein (8%) content altered when developed under high temperature under aCO2. Maximum sucrose (7.5%) and stachyose (3.8%) accumulation in seeds were observed when developed under low temperatures and eCO2. When the growing temperature increased from optimum to high, the seed oleic acids increased (63%), while linoleic and linolenic acids decreased (- 28% and - 43%, respectively). Significant temperature and CO2 effects were observed in progenies with the highest maximum seedling emergence (80%), lesser time to 50% emergence (5.5 days), and higher seedling vigor from parents grown at low-temperature treatment under eCO2. Discussion: Exposure of plants to 38/30°C was detrimental to soybean seed yield, and eCO2 levels did not compensate for this yield loss. The high temperature during seed developmental stages altered the chemical composition of the seed, leading to an increased content of monounsaturated fatty acids. The findings suggest that parental stress can significantly impact the development of offspring, indicating that epigenetic regulation or memory repose may be at play. [ABSTRACT FROM AUTHOR]

Rice-wheat and maize-wheat rotations are major cropping systems in the middle and lower reaches of Yangtze River in China, where high nitrogen (N) inputs and low N efficiency often exacerbate resource waste and environmental pollution. Due to the changes in factors such as soil properties and moisture content, the N fate and the N utilization characteristics of wheat in different rotations are significantly different. Efficient N management strategies are thus urgently required for promoting maximum wheat yield in different rotation systems while reducing N loss. A 2-year field experiment using isotopic (15N) tracer technique was conducted to evaluate the fate of 15N-labeled urea in wheat fields and the distribution characteristics of N derived from different sources. The wheat yield and N use efficiency under various N rates (180 and 240 kg ha-1, abbreviated as N180 and N240) and preceding crops (rice and maize, abbreviated as R-wheat and M-wheat) were also investigated. The results showed that N240 increased N uptake and grain yield by only 8.77-14.97% and 2.51-4.49% compared with N 180, but decreased N agronomic efficiency (NAE) and N physiological efficiency (NPE) by 14.78-18.79% and 14.06-31.35%. N240 also decreased N recovery in plants by 2.8% on average compared with N180, and increased N residue in soil and N loss to the environment. Compared with that of basal N, the higher proportion of topdressing N was absorbed by wheat rather than lost to the environment. In addition, the accumulation of topdressing N in grain was much higher than that of basal N. Compared with that in R-wheat treatment, plants in M-wheat treatment trended to absorb more 15N and reduce unaccounted N loss, resulting in higher yield potential. Moreover, the M-wheat treatment increased N recovery in 0-20 cm soil but decreased 80-100 cm soil compared with R-wheat treatment, indicating a lower risk of N loss in deeper soil. Collectively, reducing N application rate and increasing the topdressing ratio is an effectiveway to balance sustainable crop yield for a secure food supply and environmental benefit, which ismore urgent in rice-wheat rotation. [ABSTRACT FROM AUTHOR]

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Water and nutrients are two main determinants of wheat yield, which are vital for maintaining high crop yields. In the present study, the effects of water and phosphate fertilization on wheat yield, photosynthetic parameters, water productivity and phosphate use efficiency were investigated. Five dryland wheat cultivars from the 1940s to the 2010s that are widely cultivated in Shaanxi Province, China, were used. Experiments were conducted from 2019 to 2022 using two irrigation levels (normal rainfall and no precipitation after the reviving stage) and two phosphorus application levels (0 and 100 kg ha−1).Compared with old cultivars (‘Mazha’), the grain yield of modern cultivars (‘Changhan 58’) was 89.24% higher and was closely correlated with chlorophyll index, leaf area index, photosynthetic rate and tillers. With the replacement of cultivars, the phosphorus content, water potential and phosphatase activity of wheat leaves increased. Considering water–phosphorus interactions, the water use efficiency and phosphorus use efficiency of wheat showed a significant positive correlation.Our findings indicate that modern wheat cultivars are more responsive to phosphorus. Further analysis revealed that modern varieties have evolved two phosphorus absorption strategies in response to phosphorus deficiency – namely, the formation of a phosphorus supply source, which may result in larger numbers of green organs; and an increase in phosphorus sinks, which tended to activation and transport of plant phosphorus. Our results may thus contribute to water conservation, increased yields and the development of strategies for efficient phosphorus fertilization. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background Results Conclusion Maize, wheat, rice and soybean production are intimately linked to food security. Identifying the key factors affecting crop yields and determining the countries where increased irrigation and nitrogen application most effectively enhance yields are essential steps towards achieving sustainable development goals and ensuring food security. Identifying these areas is crucially dependent on yield gaps. However, the lack of comparability between different regions in current regional‐scale yield gap studies stems from varied methodologies. Moreover, global yield gap research, relying on statistical models and regression methods, tends to neglect the crop growth process. In this study, we used a random forest model, based on statistical and meteorological data, to pinpoint the key factors influencing crop yields. Subsequently, using unified yield data from the Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP), derived from crop models simulations, we applied the yield gap method to calculate the potential yield increase for four crops across countries, under conditions of full irrigation and nitrogen application.Our research finds that nitrogen application is the main factor affecting yields globally, while irrigation plays a crucial role in the major producing countries. The countries with high potential for yield increases are located at the border between Africa and Eurasia. The global average yield of the four major crops increased 13.7–29.8% under full irrigation, 2.9–39.1% under full nitrogen application and 29.4–97.8% under both conditions.This study provides crucial insights into global crop yield changes and their determinants, which are highly important for global sustainable agriculture and food security efforts. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Sowing date and soil fertility are very important factors in the overwintering and production performance of alfalfa (Medicago sativa L.), yet there's a knowledge gap in knowledge on how late-seeded alfalfa responds to phosphorus (P) fertilization. A field study was conducted in Inner Mongolia from 2020 to 2022 using a split-plot design. The main plots consisted of five sowing dates (31 July, 8, 16, and 24 August, and 1 September), while the subplots involved five P application rates (0, 40, 70, 100, and 130 kg P2O5 ha−1). Throughout the growing seasons, the overwintering rate, root traits, forage yield, and yield components were measured. The results revealed a consistent decrease in overwintering ability and productivity with the delayed sowing. This reduction in overwintering rate was mainly due to diminished root traits, while the decrease in forage yield was largely associated with a reduction in plants per square meter. However, P fertilizer application to late-seeded alfalfa demonstrated potential in enhancing the diameter of both the crown and taproot, thus strengthening the root system and improving the overwintering rate, the rate of increase ranges from 11.6 to 49%. This adjustment could also improve the shoots per square meter and mass per shoot, increasing by 9.4–31.3% and 15.0–27.1% respectively in 2 years, which can offset the decline in forage yield caused by late sowing and might even increase the forage yield. Regression and path analysis indicated that alfalfa forage yield is primarily affected by mass per shoot rather than shoots per square meter. This study recommended that the sowing of alfalfa in similar regions of Inner Mongolia should not be later than mid-August. Moreover, applying P fertilizer (P2O5) at 70.6–85.9 kg ha−1 can enhance the forage yield and persistence of late-seeded alfalfa. Therefore, appropriate late sowing combined with the application of P fertilizer can be used as an efficient cultivation strategy for alfalfa cultivation after a short-season crop harvest in arid and cold regions. [ABSTRACT FROM AUTHOR]

Ozone (O3) is one of the most harmful and widespread air pollutants, affecting crop yield and plant health worldwide. There is evidence that O3 reduces the major limiting factor of photosynthesis, namely CO2 mesophyll conductance (gm), but there is little quantitative information of O3‐caused changes in key leaf anatomical traits and their impact on gm. We exposed two O3‐responsive clones of the economically important tree species Populus × canadensis Moench to 120 ppb O3 for 21 days. An anatomical diffusion model within the leaf was used to analyse the entire CO2 diffusion pathway from substomatal cavities to carboxylation sites and determine the importance of each structural and subcellular component as a limiting factor. gm decreased substantially under O3 and was found to be the most important limitation of photosynthesis. This decrease was mostly driven by an increased cell wall thickness and length of subcellular diffusion pathway caused by altered interchloroplast spacing and chloroplast positioning. By contrast, the prominent leaf integrative trait leaf dry mass per area was neither affected nor related to gm under O3. The observed relationship between gm and anatomy, however, was clone‐dependent, suggesting that mechanisms regulating gm may differ considerably between closely related plant lines. Our results confirm the need for further studies on factors constraining gm under stress conditions. [ABSTRACT FROM AUTHOR]

Phosphorous (P) is a limiting macronutrient for crop growth. Its deficiency prevents plant development leading to an extensive use of phosphatic fertilizers globally. Bio-organic phosphate (BOP) fertilizer provides a sustainable approach to optimize nutrient availability, enhance crop yield, and mitigate the negative impacts of chemical fertilizers on the environment. Therefore, the present study integrates the application of heat-tolerant phosphate-solubilizing bacteria, rock phosphate, and organic materials for the development of BOP. For this purpose, potential heat-tolerant phosphate-solubilizing bacteria (PSB) were isolated from major wheat-growing areas of southern Punjab. Five isolates were the efficient phosphate solubilizers based on in vitro phosphate-solubilizing activity (291-454 μg ml-1 and 278-421 μg ml-1) with a concomitant decrease in pH (up to 4.5) at 45°C and 50°C, respectively. These PSB were used for the development of potential consortia that are compatible and showed high P solubilization. In planta evaluation of these PSB consortia in a pot experiment under net house conditions showed that consortium-2 had a favorable impact on growth parameter with enhanced grain yield (9.63 g plant-1) and soil available P (10 μg g-1) as compared with 80% uninoculated control. The microcosm study was conducted to evaluate PSB consortium-2 integrated with carrier material (plant material and filter mud) and rock phosphate as BOP increased total phosphorous (14%) as compared with uninoculated controls. Plant-based BOP showed higher viable count (3.5 × 108\u00B0CFU) as compared with filter mud-based BOP. Furthermore, the effect of BOP on wheat growth parameters revealed that BOP showed a promising influence on grain yield (4.5 g plant-1) and soil available P (10.7 μg g-1) as compared with uninoculated 80 and 100% controls. Principle component analysis (PCA) further validates a positive correlation between BOP with grain weight and plant height and soil available P as compared with both 80 and 100% controls. For the first time, this study reports the combined application of bio-organic phosphate fertilizer and heattolerant PSB, which offers an eco-friendly option to harvest better wheat yield with low fertilizer input. [ABSTRACT FROM AUTHOR]

In modern agriculture, Controlled environment agriculture (CEA) stands out as a contemporary production mode that leverages precise control over environmental conditions such as nutrient, temperature, light, and other factors to achieve efficient and high-quality agricultural production. Numerous studies have demonstrated the efficacy of manipulating these environmental factors in the short period before harvest to enhance crop yield and quality in CEA. This comprehensive review aims to provide insight into various pre-harvest practices employed in CEA, including nutrient deprivation, nutrient supply, manipulation of the light environment, and the application of exogenous hormones, with the objective of improving yield and quality in horticultural crops. Additionally, we propose an intelligent pre-harvest management system to cultivate high-quality horticultural crops. This system integrates sensor technology, data analysis, and intelligent control, enabling the customization of specific pre-harvest strategies based on producers' requirements. The envisioned pre-harvest intelligent system holds the potential to enhance crop quality, increase yield, reduce resource wastage, and offer innovative ideas and technical support for the sustainable development of CEA. [ABSTRACT FROM AUTHOR]

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950–2020 and 1850–2020), ERA5 reanalysis data (1950–2020), and climate model simulations (period 1970–2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies. [ABSTRACT FROM AUTHOR]

Cold stress has severe negative consequences for plant growth and crop yield. Here, we report that an Arabidopsis thaliana mutant that lacks the HPE1 gene, which encodes an RNA‐binding protein, maintains higher photosynthetic activity under cold stress, together with higher accumulation of thylakoid proteins. We showed that HPE1 interacts with MORF2 and MORF9 and thereby mediates RNA editing in chloroplasts. Loss of HPE1 function increased the editing efficiency at four RNA editing sites, rpoC‐488, ndhB‐149, ndhB‐746 and matK‐706, under cold stress and altered the expression of nuclear photosynthesis‐related genes and cold‐responsive genes. We propose that HPE1‐mediated RNA editing acts as a trigger for retrograde signaling that affects photosynthesis under cold stress. [ABSTRACT FROM AUTHOR]

The physical properties of nano-fertilizers (NFs) are important in determining their performance, efficacy, and environmental interactions. Nano-fertilizers, due to their small size and high surface area-to-volume ratio, enhance plant metabolic reactions, resulting in higher crop yields. The properties of nano-fertilizers depend on the synthesis methods used. The nanoparticle's nutrient use efficiency (NUE) varies among plant species. This review aims to analyze the relationship between the physical properties of NF and their influence on crop performance and nutrient uptake efficiency. The review focuses on the physical properties of NFs, specifically their size, shape, crystallinity, and agglomeration. This review found that smaller particle-sized nanoparticles exhibit higher nutrient use efficiency than larger particles. Nano-fertilizer-coated additives gradually release nutrients, reducing the need for frequent application and addressing limitations associated with chemical fertilizer utilization. The shapes of nano-fertilizers have varying effects on the overall performance of plants. The crystalline structure of nanoparticles promotes a slow release of nutrients. Amorphous nano-fertilizers improve the NUE and, ultimately, crop yield. Agglomeration results in nanoparticles losing their nanoscale size, accumulating on the outer surface, and becoming unavailable to plants. Understanding the physical properties of nano-fertilizers is crucial for optimizing their performance in agricultural applications. [ABSTRACT FROM AUTHOR]

Legume-grass mixtures are often used to increase forage yield and soil fertility in managed grasslands, but it remains unclear whether these benefits could be further improved by utilizing optimal legume-grass seeding ratios and phosphorus (P) fertilization. Here, we conducted a 5-year field experiment across 3 sites to investigate the effects of legume-grass seeding ratio and P fertilization on forage yield and soil quality. This experiment included mixtures comprised of two legume species and two grass species at five legume-grass ratios (3:7, 4:6, 5:5, 6:4, and 7:3), and monoculture of each species. P fertilizer was applied at the rate of 0, 9, 18, or 27 kg P ha-1 year-1. Plant diversity effects, e.g., complementarity and selection effects, were assessed by comparing yield of mixtures with monocultures. Our results show that a legume-grass mixture with a seeding ratio of 3:7 under moderate P fertilization resulted in the highest forage yield among all monocultures and mixtures, for each of the three sites, with persistent and consistent transgressive overyielding. Notably, greater soil organic matter, total nitrogen, enzymatic activity, and microbial biomass were observed with the legume-grass ratio of 3:7, compared to monocultures or other seeding ratios. Structural equation modeling indicated that the legume-grass ratio of 3:7 achieved highest yield through directly improving complementarity effect, and indirectly promoting selection effects because of increased fungal biomass. P fertilization directly enhanced soil nutrient and enzymatic activities, and further improved complementarity effect, resulting in high forage yield and soil quality. These results indicate that forage diversification practices with low legume-grass seeding ratios and moderate P fertilization can simultaneously benefit forage production and soil quality in managed grasslands. Overall, our study suggests that low legume seeding proportion in legume-grass mixtures combined with moderate nutrient management is a useful strategy for sustainable and highly productive managed grasslands. [ABSTRACT FROM AUTHOR]

Aims: Enhancing crop yields is a contemporary challenge of the modern world, and selenium (Se) has been shown to improve plant health against various abiotic stressors. This study aimed to evaluate the influence of Se on the biosynthesis of photosynthetic pigments, nitrogen metabolism, antioxidant metabolism, flavonoid synthesis and nodulation of cowpea plants. Methods: We carried out a soil experiment with five Se application rates (0, 7.5, 15, 30, and 45 µg kg−1) and a hydroponics experiment with four Se concentrations (0, 5, 10, and 15 µmol L−1). Results: Both experiments revealed hormetic-like responses of cowpea plants to Se, at both molecular and whole-plant levels. Se dose-dependently increased the synthesis of chlorophylls, carotenoids and pheophytin as well as the efficiency of enzymatic antioxidant metabolism by decreasing MDA levels and increasing CAT and APX activity. Selenium also enhanced nitrate reductase activity, resulting in greater biosynthesis of amino acids and proteins in the leaves. However, the dose responses were not identical in the two experiments. Hormetic-like biphasic responses were uniquely revealed for daidzein, genistein, and kaempferol in the hydroponics experiment. Selenium also enhanced the number of nodules in roots, which generated increases in the production of ureides and greater production of shoot dry weight in both experiments. Low rates of Se stimulated the translocation of allantoic acid from nodules to leaves. Conclusions: This study adds new insights regarding Se effect on antioxidant and nitrogen metabolism, photosynthetic pigments and nodulation, which can increase cowpea yield. [ABSTRACT FROM AUTHOR]

Deep learning methods have been widely used in applications of yield estimation using remote sensing data. However, they still face some challenges in terms of accuracy due to their inability to fully utilize the spatiotemporal information of remote sensing data. In response to this problem, a winter wheat yield estimation method based on ensemble deep learning and Gaussian process (GP) was proposed. First, the long-short term memory (LSTM) network and convolutional neural network (CNN) were constructed to explore deep spatiotemporal features from Sentinel-1A time-series images. Then, a GP component was applied for fusion of intraimage deep spatiotemporal features and inter-sample spatial consistency features. Finally, the yield estimation results were obtained. The experimental results showed that the proposed method had higher accuracy than those compared models, with a coefficient of determination (${R^2}$ R 2 ) of 0.698, a root mean square error (RMSE) of 477.045 kg/ha and a mean absolute error (MAE) of 404.377 kg/ha, demonstrating the application potential of the proposed method in crop yield estimation applications. [ABSTRACT FROM AUTHOR]

SUMMARY: Gibberellins (GAs) play crucial roles in regulating plant architecture and grain yield of crops. In rice, the inactivation of endogenous bioactive GAs and their precursors by GA 2‐oxidases (GA2oxs) regulates stem elongation and reproductive development. However, the regulatory mechanisms of GA2ox gene expression, especially in rice reproductive organs, are unknown. The BEL1‐like homeodomain protein OsBLH4, a negative regulatory factor for the rice OsGA2ox1 gene, was identified in this study. Loss of OsBLH4 function results in decreased bioactive GA levels and pleiotropic phenotypes, including reduced plant height, decreased grain number per panicle, and delayed heading date, as also observed in OsGA2ox1‐overexpressing plants. Consistent with the mutant phenotype, OsBLH4 was predominantly expressed in shoots and young spikelets; its encoded protein was exclusively localized in the nucleus. Molecular analysis demonstrated that OsBLH4 directly bound to the promoter region of OsGA2ox1 to repress its expression. Genetic assays revealed that OsBLH4 acts upstream of OsGA2ox1 to control rice plant height, grain number, and heading date. Taken together, these results indicate a crucial role for OsBLH4 in regulating rice plant architecture and yield potential via regulation of bioactive GA levels, and provide a potential strategy for genetic improvements of rice. Significance Statement: GA 2‐oxidases (GA2oxs) play crucial roles in regulating plant architecture and yield by inactivating endogenous bioactive GAs and their precursors, but the regulatory mechanisms of GA2ox gene expression remain unclear. This study identifies that OsBLH4, a member of the BEL1‐like homeodomain proteins, acts upstream of OsGA2ox1, modulates endogenous bioactive GA levels, and regulates stem elongation and reproductive development in rice. [ABSTRACT FROM AUTHOR]

SUMMARY: Enhancing the efficiency of photosynthesis represents a promising strategy to improve crop yields, with keeping the steady state of PSII being key to determining the photosynthetic performance. However, the mechanisms whereby the stability of PSII is maintained in oxygenic organisms remain to be explored. Here, we report that the Psb28 protein functions in regulating the homeostasis of PSII under different light conditions in Arabidopsis thaliana. The psb28 mutant is much smaller than the wild‐type plants under normal growth light, which is due to its significantly reduced PSII activity. Similar defects were seen under low light and became more pronounced under photoinhibitory light. Notably, the amounts of PSII core complexes and core subunits are specifically decreased in psb28, whereas the abundance of other representative components of photosynthetic complexes remains largely unaltered. Although the PSII activity of psb28 was severely reduced when subjected to high light, its recovery from photoinactivation was not affected. By contrast, the degradation of PSII core protein subunits is dramatically accelerated in the presence of lincomycin. These results indicate that psb28 is defective in the photoprotection of PSII, which is consistent with the observation that the overall NPQ is much lower in psb28 compared to the wild type. Moreover, the Psb28 protein is associated with PSII core complexes and interacts mainly with the CP47 subunit of PSII core. Taken together, these findings reveal an important role for Psb28 in the protection and stabilization of PSII core in response to changes in light environments. Significance Statement: The small protein Psb28 plays a crucial role in the protection and stabilization of PSII reaction center via direct interaction with the CP47 subunit of PSII core complexes in the model plant Arabidopsis thaliana, providing novel insights into the maintenance of PSII function for boosting photosynthetic capacity in response to changing light environments. [ABSTRACT FROM AUTHOR]

• Drought stress severely affects plant growth and development. • The use of glycine betaine encapsulated chitosan nanoparticles is an efficient strategy to alleviate drought stress. • Chitosan and glycine betaine encapsulated chitosan nanoparticles increased the physiological, morphological and molecular attributes of maize plant. • Glycine betaine encapsulated chitosan nanoparticles were more effective under drought stress as compared to alone nanoparticles. Drought is one of the main abiotic stresses that affect the growth and development of maize crop, thus causing food crises across the world. Nanoparticles are considered an effective tool for improving crop yield by upgrading the plant tolerance mechanism under abiotic stress. In this study, we investigated the role of foliar application of glycine betaine-encapsulated chitosan loaded nanoparticles (LNPs) and unloaded chitosan nanoparticles (NPs) in improving morphological, physiological, biochemical and molecular attributes in two maize varieties, HNG and CZP, grown under drought stress. Petri plates experiment was performed to screen different levels of drought (5 % and 20 %) and nanoparticle concentrations (10, 20 and 40 μg NPs). In the pot experiment, 20 % drought stress along with 20 μg LNPs and 20 μg NPs were used to examine the morphological, physiological and biochemical attributes of maize plants. Drought stress led to a significant reduction in fresh and dry weight, relative water content, root and shoot lengths, photosynthetic pigments, and antioxidant enzyme activities, including catalase, superoxide dismutase, ascorbate peroxidase, and proline content compared to control plants. However, foliar application of LNPs resulted in increased photosynthetic pigments, enhanced antioxidant enzyme activity, and elevated proline content under both control and drought-stressed conditions. Conversely, malondialdehyde (MDA) content decreased significantly with the application of LNPs and NPs alone under stress conditions. Moreover, nanoparticles also enhanced the relative expression levels of antioxidant genes, ZmAPX, ZmCAT , and ZmSOD. These findings highlight the potential of LNPs as an ecofriendly which helps in ameliorating drought stress in maize, and other plants. [ABSTRACT FROM AUTHOR]

Summary Societal Impact Statement Summary Climate change continues to intensify the challenges of food production as agricultural systems face more variable and extreme weather. Coupled with increasing human population, growers must balance increasing crop yields with nutrient content to prevent global malnutrition. Photosynthetic diversity may permit some crops to tolerate climate change and elevated CO2 whilst maintaining both crop yield quantity and quality. This review examines how photosynthetic diversity interacts with crop production and nutritional stability under elevated CO2 and climate change, and highlights opportunities for photosynthetic diversity to inspire agricultural solutions.Innovative agricultural solutions are desperately needed to achieve food security for a growing human population amidst the imminent pressures of climate change that threaten more variable and extreme weather, placing additional pressures on already precarious agricultural systems. Not only are crop yields at risk under climate change but rising global atmospheric CO2 concentrations are concurrently driving a carbon dilution effect that threatens to reduce the nutritional quality of our crops to further global malnutrition. Plants using different photosynthetic metabolisms, however, experience these negative impacts to yield and nutrition to different degrees. Thus, photosynthetic diversity may offer solutions to combat malnutrition under climate change and elevated CO2 concentrations, whether that be through targeting existing resilient species for agricultural programmes or applying agricultural biotechnology to engineer photosynthetic diversity into existing crops. Here, we discuss how each major photosynthetic type is predicted to fare under elevated CO2 concentrations and climate change and explore agricultural opportunities to maintain both yield and nutrient stability. [ABSTRACT FROM AUTHOR]

Roots and associated soil biota are key to the energy balance of plants since they consume substantial amounts of photosynthates and in turn provide resources that fuel photosynthesis. Due to spatiotemporal soil heterogeneity, single root systems are repeatedly exposed to soil conditions that either foster or limit plant growth. Knowledge concerning the root responses to repeated cycles of edaphic stress remain scarce but is indispensable to understand root foraging behaviour in soil. Quantification of the acclimative value of root responses to soil heterogeneity requires distinction between plastic (i.e., persistent) and elastic (i.e., reversible) root responses. We introduce energy balances as an approach to capture the effects of repeated edaphic stress cycles on crop growth and yield formation and to quantify the acclimative value of root responses to specific soil environments. Spatiotemporal soil heterogeneity and the resulting edaphic stress cycles can be decisive for crop growth. However, our understanding of the acclimative value of root responses to heterogeneous soil conditions remains limited. We outline a framework to evaluate the acclimative value of root responses that distinguishes between stress responses that are persistent and reversible upon stress release, termed 'plasticity' and 'elasticity', respectively. Using energy balances, we provide theoretical evidence that the advantage of plasticity over elasticity increases with the number of edaphic stress cycles and if responses lead to comparatively high energy gains. Our framework provides a conceptual basis for assessing the acclimative value of root responses to soil heterogeneity and can catalyse research on crop adaptations to heterogeneous belowground environments. [ABSTRACT FROM AUTHOR]

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model. [ABSTRACT FROM AUTHOR]

The objective was to evaluate the efficacy of pre-emergence herbicides mixture applied to the soil with and without dead cover crops (Sorghum bicolor) for the control of Amaranthus hybridus L. (smooth pigweed) and its selectivity in soybeans. This study was structured in split plot (2 × 6 + 2), where factor A plots (with and without dead cover) and factor B six herbicides mixture: flumioxazin + S-metolachlor (50.4 + 1,008 g a.i. ha−1), flumioxazin + imazethapyr (60 + 127.2 g a.i. ha−1), pyroxasulfone + sulfentrazone (137.6 + 160 g a.i. ha−1), diuron + sulfentrazone (400 + 200 g a.i. ha−1), metribuzin + S-metolachlor (326.4 + 1,344 g a.i. ha−1) and sulfentrazone + imazethapyr (200 + 100 g a.i. ha−1) and two untreated control plots. As for the results, the herbicides flumioxazin + S-metolachlor, flumioxazin + imazethapyr and pyroxasulfone + sulfentrazone showed excellent control (97–99%) and were not influenced by the plot with and without dead cover. They also showed higher yield soybeans (<2,244 kg ha−1). All herbicides were selective to the soybeans. Overall, pre-emergence herbicides and cover crops were efficient methods for the control of A. hybridus, which farmers should use to avoid losses in yield soybeans due to weed competition. [ABSTRACT FROM AUTHOR]

In the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality. [ABSTRACT FROM AUTHOR]

Soybean film mulching has shown promise in maintaining consistent and high yields in semi‐arid regions. However, the specific impacts of full‐film and semi‐film mulching on soybean growth, root nodule traits and grain yield are poorly understood. This 2‐year study (2021–2022) investigates the effects of full‐film and semi‐film mulching on soil moisture, soybean growth and yield. Our findings revealed that semi‐film mulching increased soybean yield by 18.12% compared to full‐film mulching, averaged across 2 years. Furthermore, the semi‐film treatment significantly enhanced biological nitrogen fixation ability, increasing root nodule numbers by 24.81%–33.43%, compared to full‐film mulching. This improvement also positively affected soybean quality, with crude protein content increasing by 5.89% in 2021 and 4.14% in 2022 compared to full‐filming. Moreover, semi‐film mulching helped maintain soil moisture and temperature during later soybean growth stages. There findings suggest that semi‐film mulching is a viable agricultural strategy for soybean cultivation in semi‐arid regions, improving soybean quality and efficiency while promoting environmental sustainability. [ABSTRACT FROM AUTHOR]

One of the dominant global development agendas for rural Africa in the past two decades has cast large-scale agro-industrial investments as a solution to achieve more efficient land use, higher crop yields, enhanced food security, and poverty reduction, among others. However, mounting evidence shows that this agenda has not fulfilled its promises: most land deals for agricultural production have not materialised as planned and their socio-economic development objectives often remain unreached. Despite the often severe impacts of non-operational projects, knowledge about why they fail to take place and operate remains fragmentary. Based on an extensive literature review of contemporary land deals in seven sub-Saharan countries, this paper sheds light on two 'productive gaps'. First, the article delves into the 'productive gap' of land deals themselves, identifying key drivers of non-operational land deals. The reviewed literature points to local opposition and financial difficulties as significant factors impacting agricultural operations. Local opposition, in turn, stems largely from flawed land acquisition processes and unfulfilled investors' promises. Second, this article offers a critical appraisal of the biases and oversights in the knowledge the land grab scholarship has 'produced'. [ABSTRACT FROM AUTHOR]

Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithms and covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations. [ABSTRACT FROM AUTHOR]