Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

BACKGROUND: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. RESULTS: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). CONCLUSION: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Sustaining rice productivity through weather-resilient agricultural practices.

BACKGROUND: Enhancing productivity and profitability and reducing climatic risk are the major challenges for sustaining rice production. Extreme weather can have significant and varied effects on crops, influencing agricultural productivity, crop yields and food security. RESULTS: In this study, a comparative evaluation of two crop management systems was performed involving farmers adopting a weather forecast-based advisory service (WFBAS) and usual farmers' practice (FP). WFBAS crop management followed the generated weather forecast-based advice whereas the control farmers (FP) did not receive any weather forecast-based advice, rather following their usual rice cultivation practices. The results of the experiments revealed that WFBAS farmers had a significant yield advantage over FP farmers. With the WFBAS technology, the farmers used inputs judiciously, utilized the benefit of favorable weather and minimized the risk resulting from extreme weather events. As a result, besides the yield enhancement, WFBAS provided a scope to protect the environment with the minimum residual effect of fertilizer and pesticides. It also reduced the pressure on groundwater by ensuring efficient water management. Finally, the farmers benefited from higher income through yield enhancement, reduction of the costs of production and reduction of risk. CONCLUSION: A successful and extensive implementation of WFBAS in the rice production system would assist Bangladesh in achieving Sustainable Development Goal 2.4, which focuses on rice productivity and profitability of farmers as well as long-term food security of the country. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A brief review on oryzacystatin: a potent phytocystatin for crop management.

Phytocystatins are a type of proteinase inhibitor which are extensively studied for their specific inhibitory action against cysteine protease enzymes (CP) of insects and pathogens. Oryzacystatins (OC), a phytocystatin from rice inhibits CP in a reversible manner with its conserved tripartite wedge. OCs have important role in plant innate defense mechanism through phytohormonal signalling pathways. OC are induced in response to both biotic and abiotic stress conditions and are used to develop transgenic plants exhibiting resistance against stress conditions. In this review, we focus on the structure and mechanism of action of oryzacystatins, their possible role in plant physiology, biotic and abiotic stress tolerance mechanism in plants and their potential application strategies for future crop management studies.

Accelerating technical change through ICT: Evidence from a video-mediated extension experiment in Ethiopia.

Despite enthusiasm around applications of information and communications technologies (ICTs) to smallholder agriculture in many lower-income countries, there are still many questions on the effectiveness of ICT-based approaches. This study assesses the impacts of video-mediated agricultural extension service provision on farmers' adoption of improved agricultural technologies and practices in Ethiopia using data from a two-year randomized experiment. Our results show that the video-mediated extension approach significantly increases uptake of recommended technologies and practices by improving extension access and farmer knowledge. Specifically, we find that video-mediated extension reaches a wider audience than the government's conventional extension approach and leads to higher levels of farmer understanding and uptake of the subject technologies in those locations randomly assigned to the program. While our results also point to greater extension access and greater knowledge among female spouses in locations where both male and female spouses were targeted by the program, we do not find clear evidence that a more inclusive approach translates into higher uptake of the subject technologies. Finally, we find that the video-mediated approach becomes less costly as the scale of operation increases.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models.

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

The effects of ants on pest control: a meta-analysis.

Environmental impacts of conventional agriculture have generated interest in sustainable agriculture. Biological pest control is a fundamental tool, and ants are key players providing ecological services, as well as some disservices. We have used a meta-analytical approach to investigate the contribution of ants to biological control, considering their effects on pest and natural enemy abundance, plant damage and crop yield. We also evaluated whether the effects of ants are modulated by traits of ants, pests and other natural enemies, as well as by field size, crop system and experiment duration. Overall (considering all meta-analyses), from 52 studies on 17 different crops, we found that ants decrease the abundance of non-honeydew-producing pests, decrease plant damage and increase crop yield (services). In addition, ants decrease the abundance of natural enemies, mainly the generalist ones, and increase honeydew-producing pest abundance (disservices). We show that the pest control and plant protection provided by ants are boosted in shaded crops compared to monocultures. Furthermore, ants increase crop yield in shaded crops, and this effect increases with time. Finally, we bring new insights such as the importance of shaded crops to ant services, providing a good tool for farmers and stakeholders considering sustainable farming practices.

Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley (Hordeum vulgare L.).

In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green-red, and blue-yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure.

Life Cycle Thinking for the environmental and financial assessment of rice management systems in the Senegal River Valley.

Rice is a staple food in Senegal, which however imports more than 70% of the rice consumed annually to meet its domestic demand. Despite governmental efforts to increase rice self-sufficiency, both rice supply and yields remain low. Senegalese farmers face challenges related to irrigation infrastructure and fertiliser access, besides those derived from climate change. This study applies Life Cycle Assessment (LCA) combined with financial Life Cycle Costing (LCC) to evaluate alternative scenarios for rice management in the Senegal River Valley and identify sustainability hotspots and potential improvements. Specifically, rice cultivation in Ross Béthio (Saint Louis, Senegal) is assessed based on the observed agricultural practices during the dry seasons of 2016 and 2017. Two scenarios capturing conventional (CONV) and intensive (INT) practices are compared to two reference scenarios (SAED scenarios) according to the recommendations of the official agricultural advisory service. The INT scenario generates the lowest impacts per kg of paddy rice in seven out of thirteen impact categories, including climate change, freshwater and marine eutrophication, ozone depletion and water scarcity. This is due to the higher yields (7.4 t ha-1) relative to CONV (4.8 t ha-1) and the two reference SAED scenarios (6.0 t ha-1). The two latter scenarios show the lowest values in the remaining categories, although they also generate slightly lower profits than INT (138 € t-1 vs. 149 € t-1) due to increased labour costs for additional fertilisation treatments. The results from both LCA and LCC underline the importance of increasing yields to decrease environmental impacts and production costs of rice when estimated per kg of product. Well-designed fertiliser application doses and timing and increased mechanisation can deliver further environmental benefits. Additional improvements (e.g. in irrigation, crop rotations, straw management) could be considered to promote the long-term sustainability and profitability of rice production in Senegal. LCA in combination with financial LCC is identified as a decision-support tool for evaluating the sustainability of alternative crop management practices. Life Cycle Thinking can still benefit from experiential learning based on information exchange between farmers, researchers and extension agents to contribute to a sustainable agriculture and ultimately to food security in Africa.

Environmental life cycle assessment of rice production in northern Italy: a case study from Vercelli.

Purpose: The study's objective is to assess the environmental performance of rice production in Northern Italy, in particular in Piedmont, the first Italian and European district for the rice-growing area, and thus identify the most critical hotspots and agricultural processes. In particular, as a case study, a farm located in Vercelli (VC) has been chosen. Subsequently, the study results were compared with other different cultivation practices to evaluate the most sustainable choice. Methods: The application of the LCA has been performed, highlighting the phases of rice production that have the most significant impact. Then, uncertainty and sensitivity analyses have been made to estimate the robustness of the results and assess the influence of changing some input variables on emission reduction. Finally, multivariate statistical, specifically a principal component analysis (PCA), was conducted to aid the interpretation of the output dataset of this case study. LCA, uncertainty analysis, and sensitivity analysis were performed with SimaPro 9.2.0, using ReCiPe 2016 Midpoint (H) methodology, and PCA with R software. Results and discussions: The hotspot with the highest environmental load is irrigation, which compared to the other phases impacts more in 15 out of 18 categories, including 12 with impacts greater than + 75%. This is because irrigation causes direct impacts, related to the methanogenesis in rice fields, but also indirect impacts related mainly to the production of the energy mix required to move the large masses of irrigation water. Therefore, different water management systems were compared and results show that the irrigation systems based on intermittent paddy submergence (DSI) could result in - 40% lower impacts, resulting to be the preferable technique over the other irrigation systems analyzed, including the traditional one used in this study. Conclusions: In order to reduce the environmental impacts related to the irrigation process, a water management system characterized by intermittent flooding of the paddy field (DSI) could be used as it reduces the environmental impacts the most (- 40%), while the least suitable system is one characterized by continuous flooding without drought periods, as it causes the highest impacts. Supplementary Information: The online version contains supplementary material available at 10.1007/s11367-022-02109-x.

Machine Learning in Agriculture: A Comprehensive Updated Review.

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

The optimization of wheat yield through adaptive crop management in a changing climate: evidence from China.

BACKGROUND: Adaptive crop management is critical to food security in a changing climate, but the respective contributions of climate change and crop management to yields remain unclear. Thus, we distinguished and quantified the respective contribution of climate change and crop management on wheat yield between 1981 and 2018 in China, using first-difference multivariate regression model. RESULTS: Wheat production in China has increased over the past four decades. Under the sole impact of climate change, wheat yield generally decreased (-5.45 to +1.09% decade-1 ). Crop management increased the wheat yield from 7.11 to 19.94% decade-1 . Sensitivities of wheat yield to climatic variables (average temperature, accumulated sunshine hours, accumulated precipitation) were spatially heterogeneous; notably, in spring-wheat planting areas, wheat yield was more susceptible to the negative impact of warming. In terms of relative contribution, the contribution of climate change to spring wheat yield was -24.08% to -5.41%, and the contribution to winter wheat was -4.98% to +34.69%. Crop management had a positive contribution to all wheat-growing areas (65.31-96.84%). CONCLUSION: Crop management had a greater effect on wheat yield than climate change did. Among the three climatic variables investigated, average temperature had the dominant effect on wheat yield change; the impact of precipitation was minimal but mostly negative. The results provide insight regarding the contribution of climate change and crop management to wheat yield; adaptation measures may be more effective in planting areas where crop management contributes more, which will help stakeholders optimize crop management and adaptation strategies. © 2020 Society of Chemical Industry.

Effects of climate change and crop management on changes in rice phenology in China from 1981 to 2010.

BACKGROUND: Crop phenology change is co-determined by climate change and adaptation strategies, such as crop management, but their combined and isolated impacts on rice phenology are still unclear. Quantifying the impacts and identifying the main contributors are critical to food security under climate change. Thus we distinguished and quantified the relative contribution of climate change and crop management to rice (Oryza sativa L.) phenological changes in China from 1981 to 2010, using a first-difference multivariate regression method. RESULTS: Rice phenology has changed over the past 30 years in China. The mean length of the phenological stage from emergence to transplanting was shortened, whereas the mean length of the stage from transplanting to heading, from heading to maturity, was prolonged. The relative contribution of crop management was greater than that of climate change for single and late rice, which took up over 90% of the total change in certain phenology stages. Among the climatic factors, temperature was the dominant contributor, which accounted for more than 50% of the change in rice phenology. The stage from transplanting to heading of early rice and late rice had strongly negative sensitivities to increasing temperature. CONCLUSIONS: Crop management has offset the adverse effects of climate change on single and early rice phenology in China over the past 30 years to some extent, while further adaptation measures such as adjusting sowing date, shifting rice varieties, applying nitrogen fertilizer and irrigation should be applied to late rice in southern China, especially in a warmer future. © 2021 Society of Chemical Industry.

Integrated systematic approach increase greenhouse tomato yield and reduce environmental losses.

High input - high output greenhouse vegetable systems are responsible for nutrient surpluses and environmental losses. Integrated strategies that improve soil, crop and nutrient management are needed to ensure more sustainable production systems. We conducted a two-year field experiment to evaluate the potential of integrated soil-crop system management (ISSM) practices to improve the productivity and environmental outcomes from an intensive greenhouse tomato production system in the Yangtze River Basin, China. Four treatments were tested: i) farmers' practice (FP); ii) soil remediation (SR), where lime nitrogen with compost addition was the only management strategy; iii) a treatment that combined soil remediation with optimized crop planting density (SRCO), which increased planting density for improving crop yield; and iv) integrated soil-crop system management (ISSM), as a systematic integrated approach, which included the combined optimization of soil remediation, crop optimization, and nutrient management. In the integrated soil-crop system management treatment, nutrient management was optimized through adoption of the most appropriate type (formula) of fertilizer for the crop, rate and application timing of synthetic fertilizer, and by substituting poultry manure with compost. Our results indicated that the fruit yield of the integrated soil-crop system management treatment was 104 t ha-1, 13.4%-37.3% higher than that of the other three treatments. The mean reactive nitrogen loss (81.1 kg N ha-1) and the greenhouse gas emissions (6495 kg CO2-eq ha-1) in the farmers' practice treatment were much higher than in the other three treatments (reactive nitrogen loss: 47.9-54.3 kg N ha-1; and greenhouse gas emissions: 4926-5468 kg CO2-eq ha-1, respectively). The mean nitrogen and carbon footprints of the integrated soil-crop system management treatment were significantly lower than those of other treatments, as a result of both the lower fertilizer nitrogen use and the greater yield. This study indicates that integrated soil-crop system management could produce greater yields and increase net profit with reduced nitrogen inputs, whilst reducing the environmental cost associated with conventional farmers' practice in plastic-greenhouse vegetable production systems.

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic.

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1-0.5 Mg C ha-1 y-1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5-1.5 Mg C ha-1 y-1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.

Co-production of knowledge and adaptation to water scarcity in developing countries.

Dwindling of freshwater resources is a harsh reality of the arid and semi-arid regions of the world and climate change is expected to deteriorate their situation through major reduction of freshwater supplies. Co-production of knowledge, through active negotiation of experts, government and local stakeholders has been used as a strategy to adapt to water scarcity. However, in many developing countries, co-production of knowledge is not common and adaptation efforts rarely reflects the plurality of involved knowledge sources and actors. Given the urgent need of transition towards water-efficient agricultural practices, the Iran's government applied the knowledge co-production approach and implemented an integrated participatory crop management (IPCM) project in the Bakian village, Fars province. The objectives of this study were to analyze the knowledge co-production process, identify the factors contributing to adoption of the co-produced knowledge and investigate the corresponding social, economic and environmental impacts. A mixed-method research was conducted comprising a case study on 19 informants selected using purposive sampling and a survey of 150 rice producers selected through systematic random sampling. The results indicated the relevance and pertinence of knowledge co-production in recognizing the real problems of the rice producers and suggesting some potential adaptive strategies. Though a wide range of natural, financial, technical, institutional and structural constraints restricted adoption of the proposed adaptive strategies, application of the co-produced knowledge significantly increased water productivity, ensured higher yields and farm-based sustainable livelihoods, and enhanced resilience of the farm households under water scarcity. Some recommendations and implications are offered to increase adaptation of farm families to water scarcity.

The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach.

Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain-fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fertility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation procedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub-humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2 . The study finds that the use of improved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results suggest that the application of fertilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is projected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.

Fusarium Crown Rot Whitehead Symptom as Influenced by Wheat Crop Management and Sampling Date.

Symptoms of Fusarium crown rot of wheat include premature death of inflorescens (whiteheads), lesions on subcrown internodes, and rotting of crown tissue and lower stem internodes. Each symptom type is influenced by a different set of environmental conditions. Whiteheads are the easiest symptom to quantify and are frequently reported in the Pacific Northwest U.S.A. The objective of this research was to examine factors associated with whitehead expression and relationships with wheat yield and test weight. Incidence of whiteheads differed for inoculations with different isolates of F. pseudograminearum and F. culmorum, and over years due to weather factors. Whiteheads became less as planting dates for winter wheat were delayed until after September, and incidence was increased with increasing nitrogen application rate. Dates of initial and greatest expression of whiteheads differed among cultivars, which was associated in part with the cultivar heading date. Whiteheads were not correlated with subcrown internode lesions or browning of crown tissue. Whiteheads were also not correlated with grain test weight. Whiteheads were sometimes negatively associated with grain yield, but that relationship was variable and could not be considered a reliable, recurrent, or accurate measure of crown rot severity. These results indicate the need for caution in reporting whiteheads as a sole indicator of cultivar susceptibility to Fusarium crown rot.

Effects of tillage, crop establishment and diversification on soil organic carbon, aggregation, aggregate associated carbon and productivity in cereal systems of semi-arid Northwest India.

Intensive tillage based management practices are threatening soil quality and systems sustainability in the rice-wheat belt of Northwest India. Furthermore, it is accentuated with puddling of soil, which disrupts soil aggregates. Conservation agriculture (CA) practices involving zero tillage, crop residue management and suitable crop rotation can serve as better alternative to conventional agriculture for maintaining soil quality. Soil organic carbon is an important determinant of soil quality, playing critical role in food production, mitigation and adaptation to climate change as well as performs many ecosystem functions. To understand the turnover of soil carbon in different forms (Total organic carbon-TOC; aggregate associated carbon-AAC; particulate organic carbon- POC), soil aggregation and crop productivity with different management practices, one conventional agriculture based scenario and three CA based crop management scenarios namely conventional rice-wheat system (Sc1), partial CA based rice-wheat-mungbean system (Sc2), full CA-based rice-wheat-mungbean system (Sc3) and maize-wheat-mungbean system (Sc4) were evaluated. TOC was increased by 71%, 68% and 25% after 4 years of the experiment and 75%, 80% and 38% after 6 years of the experiment in Sc4, Sc3 and Sc2, respectively, over Sc1 at 0-15 cm soil depth. After 4 years of the experiment, 38.5% and 5.0% and after 6 years 50.8% and 24.4% improvement in total water stable aggregates at 0-15 and 15-30 cm soil depth, respectively was observed in CA-based scenarios over Sc1. Higher aggregate indices were associated with Sc3 at 0-15 cm soil depth than others. Among the size classes of aggregates, highest aggregate associated C (8.94 g kg-1) was retained in the 1-0.5 mm size class under CA-based scenarios. After 6 years, higher POC was associated with Sc4 (116%). CA-based rice/maize system (Sc3 and Sc4) showed higher productivity than Sc1. Therefore, CA could be a potential management practice in rice-wheat cropping system of Northwest India to improve the soil carbon pools through maintaining soil aggregation and productivity.

Machine Learning in Agriculture: A Review.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

A look into a multifunctional toolbox: endophytic Bacillus species provide broad and underexploited benefits for plants.

One of the major challenges of agriculture currently is to obtain higher crop yield. Environmental conditions, cultivar quality, and plant diseases greatly affect plant productivity. On the other hand, several endophytic Bacillus species have emerged as a complementary, efficient, and safe alternative to current crop management practices. The ability of Bacillus species to form spores, which resist adverse conditions, is an advantage of the genus for use in formulations. Endophytic Bacillus species provide plants with a wide range of benefits, including protection against phytopathogenic microorganisms, insects, and nematodes, eliciting resistance, and promoting plant growth, without causing damage to the environment. Bacillus thuringiensis, B. subtilis, B. amyloliquefaciens, B. velezensis, B. cereus, B. pumilus, and B. licheniformis are the most studied Bacillus species for application in agriculture, although other species within the genus have also shown great potential. Due to the increasing number of whole-genome sequenced endophytic Bacillus spp. strains, various bioactive compounds have been predicted. These data reveal endophytic Bacillus species as an underexploited source of novel molecules of biotechnological interest. In this review, we discuss how endophytic Bacillus species are a valuable multifunctional toolbox to be integrated with crop management practices for achieving higher crop yield.