Crop Yield Productivity Research Articles

Measured and modeled maize and soybean growth and water use on pipeline disturbed land

Quantifying the impacts of soil disturbance on crop yield after installing an underground pipeline is essential for implementing effective mitigation plans and land restoration guidelines in agricultural systems. The objective of this study was to model maize and soybean yields on disturbed (subsurface pipeline installation areas) and adjacent undisturbed (control) land areas. Field observations and simulation results depicted the negative impacts of soil disturbance on soil physical, chemical, and hydrological properties. Grain yield and aboveground biomass losses up to 27% were observed in the pipeline disturbed area, where the soil profile had 15% lower plant-available water retention capacity than the undisturbed soil. APSIM was parameterized using two years (2019 and 2020) of in-season crop growth observations from a soybean-maize rotation system. Simulation of yield production was further evaluated against independent data from 2017 to 2018. The overall model performance was assessed using the index of modeling efficiency (EF). The model was effective in simulating aboveground biomass (EF disturbed-undisturbed: 0.96–0.98), grain yield (EF disturbed-undisturbed: 0.91–0.96), and soil water content in the 0–450 mm soil layer (EF disturbed-undisturbed: 0.84–0.86) for the parameterization dataset. Furthermore, the EF values for simulations of the independent yield data were also satisfactory (EF of 0.98 and 0.97 for disturbed and undisturbed soils, respectively). Soil disturbance reduced water use efficiency (WUE) by 25% and 30% in soybean and maize, respectively. This study indicated the suitability of using APSIM for simulating crop growth and yield production in the highly disturbed pipeline installation area.

The Essentials of Deficit Irrigation for Crop and Water Productivity in Ethiopia: A Review

Improving irrigation water management and enhancing crop and water productivity (WP) are required to address future water scarcity in Ethiopia. Increasing WP through exposing the crop to a positive level of water stress the use of deficit irrigation (DI) is taken into consideration a promising strategy. To espouse deficit irrigation strategies, a shred of complete proof regarding DI for distinctive crops is required. The objective of this critical review is to collect adequate information about the indicators on the essential of DI to crop and water productivity. Just a study on the role of deficit irrigation indicates improve crop yield and water productivity. The end result confirmed that DI appreciably improved WP in comparison to complete irrigation. Despite better WP, the decreased yield became acquired in a number of the studied DI practices in comparison to complete irrigation. It was additionally observed that yield reduction can be low in comparison to the advantages won through diverting the saved water to irrigate extra arable land. The advantages of water-saving techniques which include alternate furrow and deficit irrigation want to be explored to make sure meals safety for the ever-growing populace in the context of declining availability of irrigation water. Consequently, reviewer concluded that deficit irrigation is doubtlessly important to enhance sustainable crop and water productiveness in Ethiopian agriculture.

The Essentials of Deficit Irrigation for Crop and Water Productivity in Ethiopia: A Review

Improving irrigation water management and enhancing crop and water productivity (WP) are required to address future water scarcity in Ethiopia. Increasing WP through exposing the crop to a positive level of water stress the use of deficit irrigation (DI) is taken into consideration a promising strategy. To espouse deficit irrigation strategies, a shred of complete proof regarding DI for distinctive crops is required. The objective of this critical review is to collect adequate information about the indicators on the essential of DI to crop and water productivity. Just a study on the role of deficit irrigation indicates improve crop yield and water productivity. The end result confirmed that DI appreciably improved WP in comparison to complete irrigation. Despite better WP, the decreased yield became acquired in a number of the studied DI practices in comparison to complete irrigation. It was additionally observed that yield reduction can be low in comparison to the advantages won through diverting the saved water to irrigate extra arable land. The advantages of water-saving techniques which include alternate furrow and deficit irrigation want to be explored to make sure meals safety for the ever-growing populace in the context of declining availability of irrigation water. Consequently, reviewer concluded that deficit irrigation is doubtlessly important to enhance sustainable crop and water productiveness in Ethiopian agriculture.

Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data

Irrigation plays an important role in crop yield production in arid and semi-arid regions. However, irrigation effects have not been well addressed in the application of crop models at a regional scale due to limited data availability, which constrains the reliability and accuracy of simulation results. Assimilating remote sensing information into crop models can provide a viable approach to reduce associated uncertainties. In this study, regional irrigation data for winter wheat (Triticum aestivum L.) grown on the Loess Plateau was used to calibrate and validate the ChinaAgrosys (China Agricultural System) crop model at site and regional scales. Remote sensing data was then assimilated into the ChinaAgrosys crop model under four assimilation schemes. Two remotely sensed assimilation state variables (i.e., LAI and NDVI) and two assimilation algorithms (i.e., PSO (Partical swarm optimization) and SCE-UA (Shuffled complex evolution)) were considered. During the winter wheat growing season on the Loess Plateau, 30.6% of the wheat production area was irrigated once, 6.7% was irrigated two times, 3.7% was irrigated three times, and the remaining wheat area was rainfed. The R2 values between maturity date, LAI, and yield simulated by the ChinaAgrosys crop model and observations at 21 agrometeorological stations on the Loess Plateau were greater than 0.73, 0.44, and 0.60, respectively, during 2010–2015. The accuracy and spatial heterogeneity of winter wheat yield estimation were effectively improved by assimilating remote sensing data into the ChinaAgrosys crop model based on regional irrigation data. Under the four assimilation schemes, the combination of PSO+NDVI produced the highest accuracy for yield estimation in Hongtong county (92.8%), followed by SCE-UA+NDVI (92.0%). Our results demonstrated the importance of accounting for the spatial heterogeneity of water availability when applying a crop model in arid and semi-arid regions. Additionally, our analysis regarding different assimilation state variables and algorithms indicated that both simulation accuracy and calculation efficiency should be considered when assimilating remote sensing data into a crop model for simulating crop growth at regional scales.

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints.

Uncovering Pathways Highly Correlated to NUE through a Combined Metabolomics and Transcriptomics Approach in Eggplant.

Nitrogen (N) fertilization is one of the main inputs to increase crop yield and food production. However, crops utilize only 30–40% of N applied; the remainder is leached into the soil, causing environmental and health damage. In this scenario, the improvement of nitrogen-use efficiency (NUE) will be an essential strategy for sustainable agriculture. Here, we compared two pairs of NUE-contrasting eggplant (Solanum melongena L.) genotypes, employing GC-MS and UPLC-qTOF-MS-based technologies to determine the differential profiles of primary and secondary metabolites in root and shoot tissues, under N starvation as well as at short- and long-term N-limiting resupply. Firstly, differences in the primary metabolism pathways of shoots related to alanine, aspartate and glutamate; starch, sucrose and glycine; serine and threonine; and in secondary metabolites biosynthesis were detected. An integrated analysis between differentially accumulated metabolites and expressed transcripts highlighted a key role of glycine accumulation and the related glyA transcript in the N-use-efficient genotypes to cope with N-limiting stress. Interestingly, a correlation between both sucrose synthase (SUS)- and fructokinase (scrK)-transcript abundances, as well as D-glucose and D-fructose accumulation, appeared useful to distinguish the N-use-efficient genotypes. Furthermore, increased levels of L-aspartate and L-asparagine in the N-use-efficient genotypes at short-term low-N exposure were detected. Granule-bound starch synthase (WAXY) and endoglucanase (E3.2.1.4) downregulation at long-term N stress was observed. Therefore, genes and metabolites related to these pathways could be exploited to improve NUE in eggplant.

Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.

Irrigation and phosphorous fertilization management to minimize rice grain arsenic content

This research sought to minimize inorganic arsenic levels in polished rice grain by using different irrigation and phosphorous fertilization practices while also maintaining crop yield and water productivity. Two experiments were conducted during seasons 2018–2019 and 2019–2020 using a split-plot design with three blocks, five irrigation treatments (main-plots) and two phosphorous levels (sub-plots). Irrigation treatments consisted of a traditional continuous flood (CF) control and four alternatives irrigation techniques with one or two drying events during the irrigation cycle. The phosphorous fertilization levels investigated were an unfertilized control (0 kg P2O5 ha−1) and the recommended fertilization level of 50 kg P2O5 ha−1. Soil pH and redox potentials were measured in each treatment. Strategically-timed, low severity drying events were effective at achieving aerobic soil conditions, resulting in Eh values over 50 mV. The alternative irrigation treatment with two drying events, implemented at panicle initiation and full flowering, was the most effective in reducing inorganic arsenic in grain without affecting grain yield or the amount of irrigation water applied. This irrigation technique could be considered as an alternative management to the traditional continuous flooded to reach minimal inorganic arsenic accumulation in grain in order to attend special quality standards or specific market requirements. Accumulated inorganic arsenic in grain was below international maximum levels in all analyzed samples, with an average value of 0.084 mg kg−1.

Rice Tariffication Law: Education and Views of Farmers in the Southern Philippines

The study was conducted to determine the views of farmers in the implementation of the rice tariffication policy. This qualitative study was conducted with 15 participants from the three barangays of the municipality of Norala South Cotabato, the Philippines using purposive sampling. On the awareness of farmers on rice tariffication law (RTL), it was found out that most of the farmers are aware of the implementation of RTL particularly through their access to social media such as radio and televisions, Facebook, and other online sources. On the views of farmers on RTL, it was found out that the majority of the farmers believe that their economic situations are being affected such as inflation, loss of profit, and failure of the harvest. On the issues encountered in the implementation of RTL, the following are found out: (1) continuous inflation rate in commodities and other basic needs, (2) high pricing fertilizer and pesticides, and (3) decrease in the yields of crop production.

Long-term rice-crayfish-turtle co-culture maintains high crop yields by improving soil health and increasing soil microbial community stability

Crop and aquatic animal co-culture systems have been used for over 1200 years and can very efficiently utilize nutrients and materials available in agroecology settings. These co-culture systems are sustainable forms of agriculture, and have been extensively practiced worldwide. Microorganisms play critical roles in promoting ecosystem nutrient transformations and material circulations. However, the long-term effects of this farming system on soil health and microbial community stability, in addition to their relationships with rice yields, remain unclear. Here, 7- and 12-year farming experiments with rice-crayfish-turtle (RCT) co-culture systems were conducted and compared against rice monoculture (RM) in a suburb of Shanghai, China. Increased years of RCT farming led to higher contents of soil organic carbon (SOC), total nitrogen (TN), and available nitrogen (Avail_N), but less total phosphorus (TP) when the same soil layers were compared against the RM treatment. Thus, RCT farming generally had positive effects on soil physiochemical properties across all soil layers. qPCR analyses indicated that RCT farming led to a higher abundance ratio of fungi to bacteria (F:B) in soils than in RM soils, suggesting an improvement in metabolic efficiency and sustainability of these agroecosystems. High-throughput sequencing demonstrated that 12-year RCT farming led to much higher soil microbial community diversity. Network analysis of 12-year RCT revealed more abundant microbial network modules and a larger proportion of negative correlations compared against 7-year RCT and RM networks. Overall, the community compositional analyses and network analyses indicated that RCT farming promoted soil health and increased microbial community stability. Stochastic processes dominated the bacterial and fungal community assembly, while random distributions were evident in the soil networks for 12-year RCT communities. Thus, all interactions between amplicon sequence variants (ASVs) were equivalent and these structures promoted network structure stability. Structural equation models (SEM) revealed that RCT farming had positive and indirect effects on rice yields by influencing soil properties or fungal diversity. Altogether, the improvement of soil health by RCT farming revealed the promotion of more suitable environments for soil microbial communities, which could contribute to nutrient cycling and further increase crop production yields. These results provide new insights into the co-associations and shifts in soil fertility quality with microbial community diversity and crop production, thereby helping to inform the ecological sustainability of RCT co-culture systems.

Simulation of wheat yield using CERES-Wheat under rainfed and supplemental irrigation conditions in a semi-arid environment

Wheat-fallow rotation is the major land-use system in the rainfed cropping system of Pakistan. Crop production in rainfed cropping systems is often jeopardized due to the scare and erratic seasonal patterns of rainfall. Climate change is further threatening the extent and productivity of rainfed agriculture in Pakistan. Climatic risk reduction strategies such as supplemental irrigation (SI) can assist in sustaining the productivity of rainfed agriculture. However, little has been done to investigate the potential of SI in sustaining the productivity of the rainfed cropping system of Pakistan despite the recent water resource developments in the rainfed regions of the country. For this purpose, a four-year (2010–2014) study was conducted to assess wheat yield and water productivity under rainfed and SI using a crop modeling approach. Calibrated CERES-Wheat was evaluated for its ability to simulate soil moisture dynamics, water productivity, canopy growth, in-season biomass, phenology, grain yield, and biomass at harvest based on soil water balance. Results showed a good to excellent performance of CERES-Wheat during evaluation. For example, combined values of soil moisture content between different layers, root zone soil moisture, seasonal crop evapotranspiration, in-season biomass growth, and canopy cover showed NRMSE values ranging from 13%–89%, 5–11%, 2–17%, 12–26%, and 13–22%, respectively. The NRMSE values of rainfall productivity of biomass and grain yield and water productivity of biomass and grain yield ranged from 18%, 16%, and 17%, 6%, respectively. The model was also applied to determine favorable management practices (appropriate planting dates from 15 October to 15 December at 15-day intervals and SI of 50 mm either at planting or 30 days after planting) as their determination under actual field conditions is laborious. Simulations for the best combination of planting date and SI suggested that higher crop yield and water productivity can be achieved with planting in November with irrigation applied 30 days after planting.

Effect of photovoltaics shading on the growth of chili pepper in controlled greenhouses

The integration of photovoltaic into a greenhouse has been implemented to maximize the energy output and crop production yield from the same land space. The effect of greenhouse external shading of opaque crystalline silicon photovoltaic (PV) panels at 13–26% of the roof area on the microclimate and growth of Chili pepper Capsicum annuum cv. (omega) was investigated. The PV panels were divided into two arrays (each of 4 PVs) and fixed separately on two external pillars of 4 m height in the Southeast and Northwest directions, respectively. Each array of 4 PVs could be tracked from the East-West direction to North-South. There were two greenhouses, one was used as control without shading and the other had the external shading. The results revealed that the external shading of PV slightly decreased air temperatures (1–2 °C) and light intensities (25–40%) but had no effect on the dew point temperatures and relative humidity as compared to the control (unshaded greenhouse). Furthermore, the yield and growth of Chili pepper in the shaded greenhouse was insignificantly higher than that in the unshaded greenhouse. Moreover, the simulation results revealed that the estimated electric energy of photovoltaic panels was 3705 kWh/year at a tilt angle of 25° facing South.

Optimization of urban resources efficiency in the domain of water–energy–food nexus through integrated modeling: a case study of Addis Ababa city

Abstract This study aims to understand the long-term (2020–2050) urban water–energy–food (WEF) resources access and sustainability in Addis Ababa city through a nexus modeling approach. Several feasible scenarios in line with improving WEF resources supply and access through conservation, system rehabilitation and technology input are explored. The water system scenarios include rehabilitation and conservation scenario, water supply enhancement scenario, technology input scenario and integrated water improvement scenario. The energy scenario includes energy conservation scenario and new renewable supply enhancement scenarios and integration of both scenarios as integrated energy scenario. The food system scenarios include crop yield productivity and irrigation water use efficiency scenarios of urban agricultural system. The integrated WEF nexus scenario is the integration of all scenarios under one nexus framework. The results are evaluated against baseline scenario. At a system level, the integrated water scenario result provides a water saving potential of 26 and 52% from the baseline scenario by 2030 and 2050, respectively, whereas the integrated energy use scenario saves energy by as much as 22 and 48%. For respective years, under the integrated WEF nexus scenario, the integrated water use scenario for low energy intensity reduces the energy use for urban water system by 23 and 72% from the baseline scenario. Similarly, urban food production have also shown enhancement. Urban food production system in Addis Ababa city is relatively small and does not significantly affect the food import from other parts of the country. Overall, the results WEF nexus modeling approach revealed the importance of exploring integrated nexus approach to sustainable urban water energy and food development and management as a first attempt at the urban scale.

Land Clearing, Preparation and Drone Monitoring using Red-Green-Blue (RGB) and Thermal Imagery for Smart Durian Orchard Management Project

Malaysia's agricultural activity is progressing toward the use of precision agriculture and Internet of Things (IoT) technology as the country moves forward with the adoption of Industrial Revolution 4.0. As a result, a novel approach based on spatial and thermal analysis is proposed and presented, along with a low-cost thermal and photogrammetric modelling workflow. As a result, the purpose of this project is to monitor durian orchards using drones equipped with thermal and red-green-blue (RGB) digital cameras. Thermal maps are presented to demonstrate the relationship between vegetation and soil temperature, and digital imagery from an early stage of preparation in the orchard is analysed for overall management system improvement. The findings indicate that the uneven ground surface of the orchard may have an effect on the irrigation system and crop yield production. Thermal imaging also reveals variations in soil temperature that are related to soil humidity. A well-managed orchard is essential for ensuring that the crop grows in a healthy state. The data acquisition and processing part of this workflow are being tested in Alor Gajah Smart Durian Orchard Management Project, Melaka, Malaysia.

Determination of optimum irrigation scheduling for sorghum in Benna-Tsemay Woreda, Southern Ethiopia

Scarcity of water is the most severe constraint for sustainable development of agriculture in arid and semi-arid areas. Hence, novel irrigation water application systems need to be developed so that high crop yield and water productivity per unit of land can be increased. Thus, the field experiment was conducted with the objective of determining the effect of different soil moisture depletion levels on yield and water use efficiency of sorghum crop in Benna-Tsemay woreda at Enchete kebele, Southern Ethiopia. The experiment was conducted for two consecutive years (2019-2020). It was arranged in RCBD with three replications and treatment was rated for five levels of available soil moisture depletion (ASMD), where T1 = 60%, T2 = 80%, T3 = 100%, T4 = 120%, and T5 = 140% of ASMD. Analysis of variance has shown that yield and water use efficiency of sorghum crop was significantly (P < 0.05) affected by irrigation scheduling. As observed in this study, the most economically attractive and environmentally accepted for small scale farmers with tolerable cost of production and higher net benefit was obtained by application of T3 (100% ASMD) under conventional furrow irrigation system. Therefore, for this particular sorghum crop (teshale variety), it could be concluded that increased water saving and water productivity through irrigation at 100% ASMD under conventional furrow irrigation system can solve the problem of water shortage and would ensure the opportunity of further irrigation development in the study area and similar agro-ecology. Int. J. Agril. Res. Innov. Tech. 11(2): 95-102, Dec 2021

A Review : Effect of Integrated Nutrient Management on Various Growth and Quality Parameters in Sorghum

Several studies have found that using chemical fertilisers on a regular basis has a negative impact on soil health and productivity by altering the physical, chemical, and biological aspects of the soil. Organic sources, on the other hand, provide all vital nutrients to plants, but they are insufficient to meet the nutritional needs of the country's crops. In this case, an optimum proportion of organic and inorganic sources should be combined to improve crop quality, crop yield, and soil productivity. This article reviews study that evaluate the effect of integrated nutrient management on crop yield, quality and soil productivity.

Effect of Imidacloprid on the Microbial Biodiversity of Soils of Selected Zones of Haryana, India

Imidacloprid is one of the most frequently used insecticide in Haryana having a pivotal role in pest management and improvement in crop yield production. This study was performed to investigate the consequences of Imidacloprid on bacterial, actinomycetes and fugal population in soils of selected zones of Haryana, India. The experiment was conducted for 28 days and the samples were taken after 1st, 7th, 14th, 21st and 28th day of Imidacloprid application at various concentrations like T1(1mg/kg), T2 (2mg/kg) and T3(10mg/kg) doses. The number of CFU (colony forming unit) of microbes were grown on appropriate media using serial dilution and spread plate method. The results obtained showed a negative effect of Imidacloprid at T1 and T2 doses in first two weeks of incubation but non-significant effect in last two weeks of incubation on soil microbial number. Imidacloprid stimulated number of heterotrophic bacteria and fungi at T3 with time but a negative effect of N2- fixing and phosphate solubilizing bacteria was observed at each dose and time point in each soil sample. Obtained results showed that these soil microbes were affected by concentration and treatment time of Imidacloprid significantly in soils of each selected zone of Haryana. The results provide a clue about the possible ecological effects of Imidacloprid.

A study on agriculture engineering implements using machine learning

India's primary source of income is agriculture. Farmers in India have differing perspectives on how to integrate technology into their farming operations. However, farmers lack the knowledge necessary to put technology into practice, even though crop yield productivity is improving. We performed a literature survey to get the suggestion of what types of agriculture engineering machinery had been employed to boost up crop production output. After some literature survey, this research work followed to implement machine learning techniques to provide better advice to farmers to use the agriculture engineering machinery to minimize manpower and boost crop productivity. According to this additional analysis, clustering is the most widely used machine learning technique to solve problems in agriculture fields. Regarding the execution time, k-means performed well for other clustering techniques for small and large datasets. In this research initiates three thousands of combined data in agri-machinery in various districts, this selective data should be assured the preferable results with the help of k-means clustering for an crops highest yield production. This research work fully engaged with k-means techniques to deeply analysis in the Agri machinery benefits for south region districts and it provides the strongly recommended machinery's knowledge to farmers to implement the farming works effectively. In this research that statistical ranges are predicted what type of machinery's gives good results for crop development in south TamilNadu farming land in the district wise. The goal of this study was to evaluate statistical ranges of agriculture types of machinery in south TamilNadu districts. This proposed effort aims to provide a better proposal for achieving a suitable level of crop output with the use of more agricultural technology.

Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods

The main aim of this paper is to develop the accurately map of soil chemical parameters were used for development of the agriculture, forestry, ecological planning, and crop yield production. Soil chemical properties analysis and forecast model was developed and validated with the Wavelet transform methods and multispectral satellite images. At the study area sites, satellite images and soil samples were collected during a similar time. Three most important soil chemical properties such as organic carbon, pH and EC were chosen to development of predication modeling based on the soil chemical information. This valuable information of parameters was analysed according to conventional methods. The observed data of soil was used for the predication modeling of soil chemical properties by MATLAB software. The identification of soil chemical properties was the subject of multi-spectral satellite images through algorithm of soil predication modeling. The real chemical characteristics of the soil are associated to wavelet transformation methods. Forecasting of soil chemical properties and this model can be given more accurate information related to soil nutrient parameters. Now a day’s machine learning programming is an easy to applied on the natural resources and agriculture studies. The chemical characteristics of the soil are compared with the different spectrum wavelengths of the MATLAB program. Therefore, four wavelets models like Daubechies, Symlet, Biorthogonal and Coiflet were selected to development of predication modelling, which wavelet model can be given more accurate information with best model of the soil chemical properties. Also, the coefficient of five key components and soil-chemical values were associated in the MATLAB software. In the semi-arid regions in, India, which components have been highly correlated with soil parameters in the predicated modeling. More detailed information of soil chemical characteristics was provided by four selected wavelet models developed based on the observed data and satellite data. Prediction of soil chemical values has been identified through low and high-frequency satellite images and artificial neural network model. In this study, the neural network wavelet model was used to predicted values related to soil chemical properties in the semi-arid region. The developed two models, like polynomial and ANN, have been validated and compared to the soil chemical properties data, which models can be fitted with the study of soil chemical properties. The results of the study area can be more beneficial for development of agriculture activities, climate change approaches, crop and soil suitability planning. From the results of models have been given a fast and quickly information of soil nutrient parameters without laboratory analysis. The results of predicated values can be more helpful to precision farming related activates and soil fertility mapping to provide the farmers and agriculture scientist.

Accessing Future Mixed Beans Yield in Zambia under a Changing Climate Scenario

Quantitative evaluation of the response of crop yield and crop water productivity to future climate change is important to prevent/mitigate the adverse effects of climate change. This study assessed the future impact of climate change on rain-fed mixed beans production in Zambia. Mixed beans yield data from 1976 to 2000, was used, aggregated by province and was detrended for non-climatic factors, then correlated with the climatic variables from WABAL function in MOSAICC programme. The significantly correlated variables were regressed to generate coefficients (xi) for each variable (ai) and the intercept (b) to build models for each province. Downscaled data from three GCMs of CanESM2, CNRM-CM5 and MPI-ESM-MR were used to forecast the 2011-2069 yields under RCP 4.5 and RCP 8.5. By comparing the historical and the future mean yields for each RCP, the change was computed and judged for significance. The general trend in the projected climate simulated future yields by the three GCMs under the two RCPs showed a decrease in yield in Agro-Ecological Region (AER) III, IIb and I, and an increase in AER IIa. The projected decrease was attributed to the temperature increase and rainy seasons tending to start later and end earlier than the historical average. This change calls for use of varieties that are more resilient to elevated mean temperatures, use of early maturing varieties, selection of suitable planting dates, the use of chemical fertilizers and the construction of irrigation and water conservancy facilities.