The Inter-Annual Variability of Rainfall Onset and Its Implication on Crop Planting in Selected East Africa Countries

The inter-annual variability of rainfall onset and crop replanting in East Africa (EA) was assessed using daily estimated rainfall data from climate hazard group infrared precipitation (CHIRPS Ver2.0) and monthly Sea Surface Temperature (SST) indices [Indian Ocean Dipole (IOD) and El-Ni?o Southern Oscillation (ENSO) at NINO3.4 region] from the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). The data covered a period of 40 years from1981 to 2020. The methods of cumulative of daily mean rainfall, percentage of onset date departure (PODD), Mann-Kendall (MK) trend test, student t-test, and correlation were applied in the analysis. The results showed that early onset with dry spell (WDS) consideration frequently occurs in Uganda between the first and second dekads of September, while late rainfall onset WDS occurs in the first and second dekads of December over central and Northern Kenya as well as in the Northeastern highlands, parts of the northern coast and unimodal regions in Tanzania. Rainfall onset with no dry spell (WnDS) portrayed an average of 10 days before the occurrence of true onset WDS, with maximum onset departure days (ODD) above 30 days across the Rift Valley area in Kenya and the Northeastern highlands in Tanzania. The high chance of minimum ODD is seen over entire Uganda and the area around Lake Victoria. However, few regions, such as Nakuru (Kenya) Gulu and Kibale (Uganda), and Gitega (Burundi), revealed a slight positive linear trend while others showed negative trend. Significant positive patterns for correlation between onset WDS and SST indices (IOD and NINO 3.4) were discovered in Northern and Northeastern Kenya, as well as areas along the Indian Ocean (over Tanzania’s Northern Coast). Inter-annual relationship between onset dates WDS and IOD (NINO3.4) indices exhibits a high correlation coefficient r = 0.23 (r = 0.48) in Uganda and r = 0.44 (r = 0.36) in Kenya. On the other hand, a negative correlation was revealed over Burundi and Tanzania (over a unimodal region). A high percentage of PODD was observed, ranging from 40% to 70% over the Rift Valley in Kenya and at the Northeastern highlands in Tanzania. However, a strong PODD above 70% was observed over Tanga and the Northern Pwani Region in Tanzania. These findings will help farmers to understand the appropriate time for crop planting, as well as help other socio-economic activities that strongly depend on rainfall.

Salt-Water Transport in Unsaturated Soils Under Crop Planting: Dynamics and Numerical Simulation

A laboratory salt-water dynamics experiment using unsaturated soils in packed silt loam and clay soil columns withdifferent soil texture profiles and groundwater levels under crops were conducted to study the changes of salt-waterdynamics induced by water uptake of crops and to propose the theoretical basis for the regulation and control of salt-water dynamics as well as to predict salinity levels. The HYDRUS 1D model was applied to simulate the one-dimensionalmovement of water and salt transport in the soil columns. The results showed that the salts mainly accumulated in theplow layer in the soil columns under crops. Soil water and salt both moved towards the plow layer due to soil waterabsorption by the crop root system. The salt contents in the column with lower groundwater were mostly greater thanthose with high groundwater. The water contents in the soil columns increased from top to the bottom due to plant rootwater uptake. The changes in groundwater level had little influence on water content of the root zone in the soil columnswith crop planting. Comparison between the simulated and the determined values showed that model simulation resultswere ideal, so it is practicable to do numerical simulation of soil salt and water transport by the HYDRUS 1D model.Furthermore, if the actual movement of salt and water in fields is to be described in detail, much work needs to be done.The most important thing is to refine the parameters and select precise boundary conditions.

Screening of Ratooning Rice Varieties Suitable for Ratooning Rice-Rape Cropping Planting Pattern in Ganfu Plain

[Objective] To screen ratooning rice varieties for the ratooning rice-rape cropping planting pattern in Ganfu Plain. [Method] The growth period, plant morphology, yield and its component of 11 varieties at the first and rebirth season were compared and analyzed. [Result] The ratooning rice varieties such as Heliangyou -1, Y Liangyou 6, Zhunliangyou 608 and Jingliangyouhuazhan were suitable for the production and application in Ganfu Plain. Heliangyou 1 and Zhunliangyou 608 showed the characteristics of early maturity, easy to achieve high yield and stable production. [Conclusion] It suggests that Heliangyou 1 and Zhunliangyou 608 should be selected as preferred varieties for the planting pattern of ratooning rice-rape cropping.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

Insights into the Roles of Melatonin in Alleviating Heavy Metal Toxicity in Crop Plants

Alleviating heavy metal pollution in farmland soil,and heavy metal toxicity in plants is the focus of global agricultural environmental research.Melatonin is a kind of indoleamine compound that wide exists in organisms;it is currently known as an endogenous free radical scavenger with the strongest antioxidant effect.As a new plant growth regulator and signaling molecule,melatonin plays an important role in plant resistance to biotic or abiotic stress.Recent studies indicate that melatonin can effectively alleviate heavy metal toxicity in crop plants,which provides a new strategy to minimize heavy metal pollution in crop plants.This study summarizes the research progress on the role of melatonin in alleviating heavy metal toxicity in crop plants and the related physiological and ecological mechanisms such as reducing the concentration of heavy metals in the rhizosphere,fixing and regionally isolating of heavy metals,maintaining the mineral element balance,enhancing the antioxidant defense system and interacting with hormonal signaling.Furthermore,future prospects for the mechanism of melatonin in regulating heavy metal toxicity,the pathway regulating synthesis and catabolism,and the interaction mechanism of melatonin signaling and other phytohormones are presented in this paper,with the goal of providing a theoretical basis for controlling heavy metal ion accumulation in crop plants grown in contaminated soil.

Phytoremediation of Cd Contaminated Soil through Certain Weed and Crop Species

Phytoremediation is a relatively new approach in remediating ecosystems contaminated by ecotoxic pollutants such as herbicides or heavy metals and especially cadmium （Cd）. Certain indicators of phytoremediation, as plant growth, tolerance to Cd, and uptake, transfer factor （TF） and percent removal of Cd, were studied for 11 crops and 8 weed species in soil with varying levels of Cd （0-240 mg Cd kg＂ soil） under controlled environment. Cadmium accumulated mainly in roots （51%-86%, depending on the species）, while a 14%-49% was transferred to shoots （except for four species） the concentration being positively related to Cd level in soil. Highest concentration in the above ground plant part was measured in sugarbeets （41-101 mg Cd kg-1 DW） followed by Bromus sterilis （75）, Eruca sativa （32-82）, Cichorium intibus （35-80）, and maize （60 mg Cd kgl）. Based on the results it is concluded that sugarbeets, maize, C. intibus, B. sterilis, E. sativa, Apium graveolens, and Vicia sativa seem to have a potential in remediating Cd contaminated soils.

From statistics to grids:A two-level model to simulate crop pattern dynamics

Crop planting patterns are an important component of agricultural land systems.These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.However,the extent of these changes and their possible impacts on the environment,terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.To fill this gap,this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.This method features a two-level model that combines a land-use simulation and a crop pattern simulation.The output of the first level is the spatial distribution of the cropland,which is then used as the input for the second level,which allocates crop censuses to individual gridded cells according to certain rules.The method was tested using data from 2000 to 2019 from Heilongjiang Province,China,and was validated using remote sensing images.The results show that this method has high accuracy for crop area spatialization.Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

The Resistance of Crops in Plant Protection

World crop production requires highly-productive varieties of agricultural crops,which are resistant to pest organisms.Such varieties are also of great importance for the Uzbekistan.Their

Improvement of light uniformity by lighting arrangement for standardized crop production

In a commercialized, fully artificial plant factory, artificial luminaire is arranged in a unified way using a general illumination theory, an actual measurement, or an empirical methodology. However, with these methods, lightings are implemented without considering specific optical characteristics of lighting or material characteristics of each component that constructs a cultivation system, resulting in an amount of light that becomes irregular. The amount of lighting is closely related with the growth and quality of crops, and the deviation between points where cultivated crops are located causes quality difference in the produced crops, thus impairing the economic feasibility of a plant factory. In this regard, a simulation to figure out an optimum lighting layout was performed. Arrangements based on the spectrum distribution of light source and reflector materials were implemented to ascertain the distance between lighting and height of lighting and gather information in the pre-treatment process to improve the uniformity of light in the plant cultivation system. Improvement of around 15% in light uniformity is achieved compared with the existing system after the simulation is carried out. This result would reduce the deviation in crop growth to make uniform quality crop production possible.

Reversible protein phosphorylation, a central signaling hub to regulate carbohydrate metabolic networks

Plants produce a range of carbohydrates to meet their growth and developmental needs. Protein reversible phosphorylation plays key roles in coordinating multiple metabolic pathways and integrating diverse internal and external cues. Understanding such regulatory metabolism will provide novel resources for breeding and crop management by modulating metabolic pathways for control of growth and stress response. In this review, we summarize the complex, multifaceted functions of protein phosphorylation and their connections to plant metabolism. We focus particularly on carbohydrate metabolic pathways that are controlled by key kinases and discuss how they are linked to downstream changes in physiology, important agronomic traits and crop quality.

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin,north-west China

To improve the accuracy of runoff forecasting,an uncertain multiple linear regression(UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming(ITSP) model is used for crop planting structure optimization(CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

Uncertain and multi-objective programming models for crop planting structure optimization

Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.

Spatial distribution of fossil fuel derived CO_(2) over India using radiocarbon measurements in crop plants

Examining the contribution of fossil fuel CO_(2) to the total CO_(2) changes in the atmosphere is of primary concern due to its alarming levels of fossil fuel emissions over the globe,specifically developing countries.Atmospheric radiocarbon represents an important observational constraint and utilized to trace fossil fuel derived CO_(2)(CO_(2ff))in the atmosphere.For the first time,we have presented a detailed analysis on the spatial distribution of fossil fuel derived CO_(2)(CO_(2ff))over India using radiocarbon(Δ14C)measurements during three-year period.Analysis shows that theΔ14C values are varying between 29.33‰ to-34.06‰ across India in the year 2017,where highest value belongs to a location from Gujarat while lowest value belongs to a location from Chhattisgarh.Based on the14C patterns,spatial distributions of CO_(2ff) mole fractions have been determined over India and the calculated values of CO_(2ff) mole fractions are varying between 4.85 ppm to 26.59 ppm across India.It is also noticed that the highest CO_(2ff) mole fraction is observed as 26.59 ppm from a site in Chhattisgarh.CO_(2ff) mole fraction values from four high altitude sites are found to be varied between 4.85 ppm to 14.87 ppm.Effect of sampling different crop plants from the same growing season and different crop plant organs(grains,leaves,stems)on theΔ14C and CO_(2ff) have been studied.Annual and intra seasonal variations in theΔ14C and CO_(2ff) mole fractions have also been analyzed from a rural location(Dholpur,Rajasthan).

Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed control

The continued emergence of herbicide-resistant weeds and the increasing labor costs are threatening the ability of growers to manage weeds and maintain profits.The smart farm with the advantage of non-invasive and high-efficiency operation plays an important role in increasing the sustainability of agricultural system as it can optimize crop inputs such as herbicides while preserving resources including soil and water.An automatic weed control system requires a sensing subsystem capable of detecting and distinguishing crop plants from weeds.The overlapping plants remain a challenge for successful detection of weeds.Crop plant signaling is a new robot-plant interaction technique that allows the visualization of exogenous fluorescent signals applied to crop plants for crop/weed identification.Based on all published articles in the leading edge of knowledge,a comprehensive review of the mushrooming crop plant signaling for discriminations of weeds and crops is highlighted.The discussion outlines the significant progress that has been made in developing new and more robust automated systems along with the current challenges and future prospects.This paper details the promise of crop plant signaling for accurate and automated plant recognitions in cropping systems.There is no doubt that this review is of great significance to scholars in related research field to study the solutions to real-time weed control.

Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.

The CRISPR/Cas revolution continues: From efficient gene editing for crop breeding to plant synthetic biology

Since the discovery that nucleases of the bacterial CRISPR(clustered regularly interspaced palindromic repeat)-associated(Cas) system can be used as easily programmable tools for genome engineering,their application massively transformed different areas of plant biology. In this review, we assess the current state of their use for crop breeding to incorporate attractive new agronomical traits into specific cultivars of various crop plants. This can be achieved by the use of Cas9/12 nucleases for double-strand break induction,resulting in mutations by non-homologous recombinatr e-tion. Strategies for performing such experiments à from Rthe design of guide RNA to the use of different transformation technologies à are evaluated. Furtherweive-more, we sum up recent developments regarding the use of nuclease-deficient Cas9/12 proteins, as DNAbinding moieties for targeting different kinds of enzyme activities to specific sites within the genome. Progress in base deamination, transcriptional induction and transcriptional repression, as well as in imaging in plants, is also discussed. As different Cas9/12 enzymes are at hand, the simultaneous application of various enzyme activities, to multiple genomic sites, is now in reach to redirect plant metabolism in a multifunctional manner and pave the way for a new level of plant synthetic biology.

Carbon footprint of crop production in Heilongjiang land reclamation area,China

In the context of global warming,agriculture,as the second-largest source of greenhouse gas emissions after industry,had attracted widespread attention from all walks of life to reduce agricultural emissions.The carbon footprint of the planting production system of the Heilongjiang Land Reclamation Area(HLRA),an important commodity grain base in China,was evaluated and analyzed in this paper.On this basis,this paper sought feasible strategies to reduce carbon emissions from two aspects:agronomic practices and cropping structure adjustment,which were particularly crucial to promote the low-carbon and sustainable development of agriculture in HLRA.Therefore,using the accounting methods in IPCC and Low Carbon Development and Guidelines for the Preparation of Provincial Greenhouse Gas Inventories compiled by the Chinese government,relevant data were collected from 2000 to 2017 in HLRA and accounted for the carbon emissions of the planting production system in four aspects:carbon emissions from agricultural inputs,N_(2)O emissions from managed soils,CH_(4) emissions from rice cultivation and straw burning emissions.Then carbon uptake consisted of seeds and straws.Finally,with farmers'incomes were set as the objective function and carbon emissions per unit of gross production value was set as the constraint,this paper simulated and optimized the cropping structure in HLRA.The results showed that there was a“stable-growing-declining”trend in the total carbon emissions and carbon uptake of the planting production system in HLRA,with total carbon emissions of 2.84×10^(10) kg and total carbon uptake of 7.49×10^(10) kg in 2017.In the past 18 years,carbon emissions per unit area and carbon emissions per unit of gross production had both shown a decreasing trend.To achieve further efficiency gains and emission reductions in the planting production system,it was recommended that the local governments strengthen the comprehensive use of straw resources,optimize irrigation and fertilization techniques,and adjust the cropping structure,i.e.,increase the planting area of maize and soybeans and reduce the planting area of rice,and increase subsidies to protect the economic returns of planters.

Perspectives of Using L-Tryptophan for Improving Productivity of Agricultural Crops: A Review

Plant growth regulators are biologically active signaling molecules that regulate a number of plant physiological processes. Auxin(indole-3-acetic acid) is an important plant growth regulator and is synthesized within plant tissues through L-tryptophan(L-TRP)-dependent and-independent pathways. It has been found that plants respond to exogenously applied L-TRP due to insufficient endogenous auxin biosynthesis. The exogenous application of L-TRP is highly significant for normal plant growth and development.L-tryptophan is applied through foliar spray, seed priming, and soil application. Soil-applied L-TRP is either directly taken up by plants or metabolized to auxin by soil microbiota and then absorbed by plant roots. Similarly, foliar spray and seed priming with L-TRP stimulates auxin synthesis within plants and improves the growth and productivity of agricultural crops. Furthermore, L-TRP contains approximately 14% nitrogen(N) in its composition, which is released upon its metabolism within a plant or in the rhizosphere and plays a role in enhancing crop productivity. This review deals with assessing crop responses under the exogenous application of L-TRP in normal and stressed environments, mode of action of L-TRP, advantages of using L-TRP over other auxin precursors, and role of the simultaneous use of L-TRP and auxin-producing microbes in improving the productivity of agricultural crops. To the best of our knowledge, this is the first review reporting the importance of the use of L-TRP in agriculture.

Use of a Bioaugmented Organic Soil Amendment in Combination with Gypsum for Withania somnifera Growth on Sodic Soil

Limited availability of organic matter is a problem to sustain crop growth on sodic soil. Organic soil amendments are a costeffective source of nutrients to enhance crop growth. A field study was conducted to evaluate the effect of an organic soil amendment bioaugmented with plant growth-promoting fungi(SF_(OA) ) in combination with gypsum on soil properties and growth and yield attributes of Withania somnifera, one of the most valuable crops of the traditional medicinal system in the world, on a sodic soil at the Aurawan Research Farm of CSIR-National Botanical Research Institute, Lucknow, India. The SF_(OA) used was prepared by pre-enriching farm waste vermicompost with plant growth-promoting fungi before mixing with pressmud and Azadirachta indica seed cake. The application of SF_(OA) at 10 Mg ha^(-1)after gypsum(25.0 Mg ha^(-1)) treatment significantly(P < 0.05) increased root length(by 96%) and biomass(by 125%) of Withania plants compared to the control without SF_(OA) and gypsum. Similarly, the highest withanolide contents were observed in leaves and roots of Withania plants under 10 Mg ha^(-1)SF_(OA) and gypsum. Combined application of SF_(OA) and gypsum also improved physical, chemical and enzymatic properties of the soil, with the soil bulk density decreasing by 25%, water-holding capacity increasing by 121%, total organic C increasing by 90%, p H decreasing by 17% and alkaline phosphatase, β-glucosidase, dehydrogenase and cellulase activities increasing by 54%, 128%, 81% and 96%, respectively, compared to those of the control. These showed that application of the SF_(OA) tested in this study might reclaim sodic soil and further support Withania cultivation and results were better when the SF_(OA) was applied after gypsum treatment.

Non-invasive water status detection in grapevine(Vitis vinifera L.)by thermography

Grapevines are preferentially grown under mild to moderate water stress conditions to achieve the best compromise between wine quality and quantity.Water status detection for advanced irrigation scheduling is frequently done by predawn leaf water potential(ΨPD)or leaf stomata conductance(gL)measurements.However,these measurements are time and labor consuming.Therefore,the use of infrared thermography(IRT)opens up the possibility to study large population of leaves and to give an overview on the stomatal variation and their dynamics.In the present study IRT was used to identify water stress of potted grapevines.In order to define the sensitivity of IRT measurements to water stress,the IRT-based water status information were compared with simultaneously measuredΨPD and gL data.Correlations between IRT-based CWSI data on the one hand and gL andΨPD on the other showed the potential of IRT for water stress detection.However,the CWSI calculation procedure is laborious and the sensitivity of CWSI for water stress detection still needs to be improved.Therefore,further improvements are necessary in order to apply remote IRT-based systems for irrigation scheduling in the field.