Crop Modeling

Sugarcane (Saccharum officinarum L.) is one of the most important crops in Brazil and its growth and development can be simulated through process-based models. The current study evaluated a model based on the decision support system for the transfer of Agrotechnology DSSAT/CANEGRO to simulate the sugarcane crop productivity in the western region of São Paulo. The DSSAT/CANEGRO model was calibrated using published yield parameters from a selection of five Brazilian sugarcane cultivars, while sugarcane yield data (tons of stems per hectare) from commercial land were used as benchmark data. Other modeling inputs were derived from the primary regional cultivar. The root mean square error (RMSE), Willmott agreement index (d), and mean absolute error (MAE) were used as performance metrics. The DSSAT/CANEGRO model resulted in a good RMSE performance. The productivity estimates were better for the cultivars SP791010 and RB835486, with RMSE equal to 2.27 and 4.48 Mg ha-1 , respectively. The ...

The objective of this research was to develop a prognosis support system that provides users with predictions of the dates of the beginning and end of flowering stage and the honey potential yield of sunflower (Helianthus annuus L.) crops. It is emphasized that this prognosis is a "poor-structured" problem and it can be solved most advantageously by a tool of "decision support system" type. The prognosis method of the flowering stage is based on "thermal time" (TT) measured in "growing degree-days". The model of calculation of the daily biologically active temperature for TT summations uses a "base temperature" of 6.7 ?C and a correction of the average daily temperature. Three TT thresholds are defined: (1) sowing emergence, (2) emergence beginning of flowering, and (3) beginning end of flowering. These TT thresholds and the honey potential yield the prognosis parameters are specific to hybrids, maturity class of hybrids (four classe...

This study was mainly aimed at determining the irrigation water requirement of wheat, cropping
pattern in the area and effect of CRIWAR software as a modeling technique. Kura Local
Government area of Kano State of Nigeria was used as the study area, where the size of the farm
used (irrigable area) was 3 hectares.
In order to use the software, the General Data File on the irrigated area, a Meteorological Data
File and a Cropping Pattern File were input into the CRIWAR software. The Meteorological
data of Kano state was collected from Nigeria Meteorological Agency (NiMeT).
CRIWAR calculated the irrigation water requirements, created tables and graphs of reference
evapotranspiration, crop irrigation water requirements per 10-day period or month, cropping
intensity, cropping pattern and effective precipitation. All tables and graphs can be imported
into commonly used word-processing software. The CRIWAR strategy component was applied in
a three hectare irrigated perimeter to estimate the irrigation water requirement and FAO
Modified Penman Method was used throughout the modeling.
It was found that the length of crop development stages for wheat planting period was 200 days
were initial stage (40days), development (60days), mid-season (60 days) and late season (40
days). From the research, it was observed that according to CRIWAR the wheat should be
planted and harvested in 8 months from November to June.
It was found that the total water requirement for wheat irrigation in Kura local government area
was determined to be 520.67 mm/cropped area.

Weekly rainfall analysis of Kandhamal district during the period of 1965 to 2010 were taken for analysis purpose, the analysis is very much important for crop planning and analyzing the probability of occurrence of dry and wet periods. This will act as bench mark for crop planning as well as sustainable agricultural management of Kandhamal district which comes under the agro-climatic zone of East-coast hill region. Markov chain model has been utilized to derive the probability of dry or wet weeks and also forward and backward accumulation of rain water suitable for crop production. This analysis can be helpful to find out different cropping system including intercropping and sequence cropping suitable during that period.

In many less developed countries, there is a need to improve productivity and profitability of agricultural systems to improve rural livelihoods, particularly in areas where monocultural production has dominated the historical land use. Generally, there are a number of physical, chemical and biological soil constraints in these systems that prevent the successful production of alternative crops, often compounded by limited access to water. Rather than conduct time-consuming and expensive field trials, modelling techniques can provide a relatively inexpensive, first assessment of yield potential of cropping options under different water/nutrient regimes which can be used to inform and refine further research. However, in less developed regions, crop modelling activities are often constrained by limited institutional and technical capacity and inadequate or incomplete input datasets. In this case, complex models that require significant technical capacity and comprehensive datasets ma...

Models are built on the assumption that any given process and relation can be expressed in a formal mathematical statement(s) or equations, and they all can be interconnected to run iteratively. Development of crop models started first in 1940s, but very few in the scientific community believed that the complex bio-physical and morphological processes of plant growth could be described mathematically, except possibly under controlled environments. In the early 1980s for the first time models were used as Decision Support System (DSS) tool. Soon after enhanced understanding of agro-ecosystem processes and synthesization of this to predict outcomes helped apply models to control and manage outcomes. The first product that came out of this effort was DSSAT model followed by APSIM model. Soon CGIAR centres started to develop and work with models. Development in modeling science and natural progression of scientific research went hand-in-hand. Since then models have been used in crop management, yield gap analysis, abiotic stress impact studies, potential yield, plant disease forecasting, inter-cropping and livestock-crop interaction studies. Soon models were used to predict climate change impact on food security and socioeconomic issues at regional and global scale. Since 2010 modeling work expanded to multi-model inter-comparison, climate change and climatic variability impact assessment, quantifying uncertainties in models for further development, genomics studies, crop improvement programs, regional and global food security issues, GxExM and GxExMxS interactions, conservation agriculture (CA), agro-forestry and tree-crop-livestock interactions and tuber crops, and also as DSS tool for crop management under current and future climates.

Crops exhibit known responses in their growth and development to local weather and climate that bear large impacts on crop productivity. Since recent decades, crop simulation models have been widely put to use in assessing the impacts of a changing climate variables (temperature, rainfall solar radiation etc) on crop production, while also targeting appropriate adaptation and mitigation responses. The objective of this study was to evaluate the Cropping System Model (CSM)-CERES-Wheat for its ability to simulate growth and development responses of three different maturity categories of wheat cultivars. Included cultivars were Hollandi, Meda walabu and Sofumer which are widely cultivated in major wheat belts in Ethiopia. The CSM-CERES-Wheat simulation model was calibrated and validated against the experimental data. The performance evaluation of the CSM-CERES-Wheat model revealed that the model was able to simulate phenology (days to anthesis and maturity) and grain yield accurately, with root mean square of error ranging between 1.3 to 3.8 and 2.4 to 5.5 days for anthesis and maturity, respectively and, 5.2 and 6.5 qtha-1 for grain yield. The index of agreement (D-statistics) between observed and simulated results were also in the higher range (D>0.87). These results are promising in making the best of crop simulation modeling in wheat technology development and promotion.

Crop models are highly useful for simulating crop and soil processes in response to variations in climate and crop management. However, well estimated crop genetic coefficients are required. So the purpose of this study is to calibrate and evaluate the performance of CERES-wheat model and to simulate the climate change impacts on phonological stages and grain yield of bread wheat (Tay and Senkegna varieties) in the study area. Observed climate data from National Meteorological Agency of Ethiopia from 1983 to 2015 and future climate from Climate Research Programme's Fifth Coupled Model Intercomparison Project (CMIP5) database across 20 Global Circulation Models for Representative Concentration Pathway (RCP4.5 and 8.5) emission scenarios in the time horizon of early-term (2010-2039), mid-century (2040-2069) and end-century (2070-2100) were used. Crop and soil data were obtained from Adet Agricultural Research Center. Decision Support System for Agro-technology Transfer (DSSAT) crop model was employed. There was strong agreement between the simulated and observed values with R 2 being 96, 79 and 79% for days to anthesis, grain yield and days to maturity, respectively for Tay wheat variety while 75% for days to anthesis, 92% for grain yield, and 75% for days to maturity of Senkegna bread wheat variety. On the other hand, during model validation, the goodness of fits (R 2) was 86% for anthesis day, 70% for grain yield and 96% for physiological maturity days of Tay wheat variety. Similarly for Senkegna bread wheat variety, R 2 was 89, 82 and 75% for anthesis day, grain yield, and physiological maturity days, respectively. The yield of both bread wheat varieties showed increase except in 2080s under RCP4.5 relative to the baseline. However, days to flowering and to maturity showed decreased in each time slice under both RCPs.

Uno de los principales factores que afecta la calidad de la manzana es el punto de maduración en la cual es cosechada. Por esta razón, se desarrolla un modelo de optimización que incorpora este factor, para apoyar la planificación de las faenas agrícolas en huertos de manzanas. El modelo busca minimizar los costos de mano de obra, de uso de maquinaria y de pérdida de calidad de la fruta, de manera de atender la demanda de las plantas de embalaje, considerando la capacidad de procesamiento de éstas, la producción en los huertos y las ventanas de tiempo para realizar la cosecha. Al aplicar este modelo a tres huertos de manzanas de la Región del Maule, Chile, se obtuvo una importante disminución en los costos de mano de obra y en la pérdida de ingresos por cosechar fruta de baja calidad.

Weekly rainfall analysis of Kandhamal district during the period of 1965 to 2010 were taken for analysis
purpose, the analysis is very much important for crop planning and analyzing the probability of occurrence of
dry and wet periods. This will act as bench mark for crop planning as well as sustainable agricultural
management of Kandhamal district which comes under the agro-climatic zone of East-coast hill region. Markov
chain model has been utilized to derive the probability of dry or wet weeks and also forward and backward
accumulation of rain water suitable for crop production. This analysis can be helpful to find out different
cropping system including intercropping and sequence cropping suitable during that period.

Predicting attainable yield under water-limiting conditions is an important goal in arid, semi-arid and drought-prone environments. To address this task, FAO has developed a model, AquaCrop, which simulates attainable yields of the major herbaceous crops in response to water. Compared to other models, AquaCrop has a significantly smaller number of parameters and attempts to strike a balance between simplicity, accuracy,

Crop models may potentially explore alternative ways to improve agroecosystem resilience in arid regions of Middle East and North Africa. Mapping the outputs behavior as a function of the inputs and quantifying the uncertainty contribution of inputs to the variability of outputs are crucial for understanding and applying complex mathematical models to a new environment. Objectives of present research are (i) to calibrate and evaluate the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model using detailed experimental datasets on maize production in arid sandy soils (Entisol) and (ii) to determine the model's sensitivity to soil, genotype and crop management inputs under the currently explored conditions (low fertility and water holding capacity) based on multivariate analysis and variance decomposition methods. The goodness-of-fit statistics between observed and simulated data indicated that the calibrated model reasonably well simulates maize phenology, growth and yield, evapotranspiration, soil water content, grain N concentration, and postharvest soil NO 3-N in eight year site field experiments. A global sensitivity analysis using the co-inertia method was carried out to link 14 output variables and 25 soil and genotype input parameters. Maize growth and yield variables were strongly correlated with soil hydrological and fertility input parameters such as soil water upper limit (SDUL) and soil organic carbon (SOC), whereas simulation of maize phenology was largely determined by phenological genotype-specific cultivar input parameters. A strong association was also observed between the output variables of yield and soil fertility. The effect of carbon (C) related soil input parameters of initial SOC and stable SOC and crop management factors of maize residue retention and compost application under no-till system on the long-term (10 years) simulation of yield and SOC dynamics was further explored using Sobolʹ method. Simulated grain yield, water productivity, active SOC, and cumulative soil CO 2 efflux were most sensitive to initial stable SOC and compost application. Maize residue retention significantly affected the simulation of cumulative N mineralization, SOC % in 0.2 m depth, and cumulative soil CO 2 efflux through interactions effect, i.e. total-order sensitivity index (S Ti) > 0.05, with other inputs. Compost application increased grain yield by 13 %, SOC stock by 5%, and cumulative soil CO 2 efflux by 95 % compared with no application. However, compost application with maize residue retained significantly reduced cumulative soil CO 2 efflux by 12 % compared with compost application with maize residue removed. Therefore, the application of compost with maize residue retained under no-till system is a plausible crop management option for agronomically improved and environmentally sound maize production in arid sandy soils.

New farming systems and management options are needed in South Asia as the intensive rice-wheat production system is set to become increasingly unsustainable under climate change. In the current study, six cropping systems options/treatments varying in tillage, crop establishment method, residue management, crop sequence and fertilizer and water management were evaluated using a cropping systems model under current (1980-2009) and future (2030 and 2050) climate scenarios in the state of Bihar, India. The treatments were current farmers' practice (CP), best fertilizer and water management practices, zero till-age (ZT) with no crop residue retention, ZT with partial crop residue retention (ZTPR), future conservation agriculture-based rice-wheat intensive cropping system (FCS-1) and future conservation agriculture-based maize-wheat intensive cropping system (FCS-2). The results indicate that climate change is likely to reduce rice-wheat system productivity under CP by 4% across Bihar. All the crop management options studied increased yield, water productivity and net returns over that of the CP under the current and future climate scenarios. However, the ZTPR treatment gave significantly higher relative yield, lower annual yield variability and a higher benefit-cost-ratio than the other treatments across cropping system components and climate periods. Although all the new cropping system treatments had a positive yield implication under the current climate (compared to CP), they did not contribute to adaptation under the future climate except FCS-2 in wheat. It is concluded that adaptation to future climate must integrate both cropping system innovations, and genetic improvements in stress tolerance.

Ce devoir s’inscrit dans le cadre d’une meilleure assimilation du cours de logiciels et simulations des impacts des changements climatiques sur les cultures. Pour cela, des données
de précipitations, de températures maximale et minimale journalières de l’Agence nationale de
la météorologie du Bénin et de 10 modèles de l’expérience CORDEX en leurs parties historiques et futures (1986-2005 et 2021-2040) ont été utilisées. Les simulations des rendements
ont été réalisées à l’aide du modèle de culture AQUACROP version 6.1. Les différents résultats montrent une hausse en moyenne des températures de 1◦C pour le scénario RCP4.5 et de
1:5◦C pour le RCP8.5 pour tous les modèles. De plus, une baisse mensuelle moyenne de 15
mm des précipitations est prédite pour la saison d’intérêt (grande saison des pluies) à Bohicon
à l’horizon 2040. Les rendements de maïs diminueront dans la commune. Ces diminutions sont
estimées en moyenne à 27 % et 2 % respectivement dans les scenarii RCP4.5 et RCP8.5 pour
le modèle CCma puis de 7 % et 3 % pour le modèle MOHC. Cependant, le modèle CNRM
prévoit une augmentation des rendements en moyenne de 28% dans un futur proche à Bohicon
pour le scénario RCP4.5.

Climate change induced crop failure is becoming a more frequent phenomenon with large uncertainty as to the degree and character of future impacts. An overwhelming number of these impacts are  projected to be within the tropical belt; an already highly capital constrained region, where issues related to health, agriculture, and infrastructure are still among the many perennial problems. With such limited capital, the opportunity cost of inefficient investments is massive. Thus, it is necessary to address the uncertainty of climate change and pinpoint which adaptation strategies are optimal for the region. This study focuses on maize and bean growing smallholders in the region of Chiquimula, Guatemala. A region which, over the past two decades, has proven to be among the world's most vulnerable; as the frequency and intensity of drought has increased, the region has proven itself unable to provide sufficient disaster relief nor food aid.
   
While climate impact and adaptation studies are not in a short supply, the vast majority of these studies focus on a global or national scale and fail to address the farm level. The following research specifically assesses the uncertainty of climate change at the farm level. By modeling the economic impact of projected climate change and analyzing optimal adaptation strategies given economic constraints, this thesis will suggest that genotype mixes augmented with various degrees of fertilization and irrigation are the optimal strategies for smallholders in Chiquimula, Guatemala to counter the effects of climate change. This study will draw on historical weather data and down-scaled General Circulation Model projections to simulate the meteorological effects of climate change in the region. The projections will be considered given the IPCC A2 scenario, as well as split between El Ni\~{n}o and La Ni\~{n}a years.  This data will then be used to simulate the effects on maize and bean yields using DSSAT to determine the adaptation impact and principle constraints. Next, a linear programming model will be employed to determine optimal irrigation, fertilizer, genotype, and intercropping combinations given the region's economic constraints.

Pesticides are the chemicals used to control the crop harms by killing the pests causing various diseases in crop plants, thereby playing vital role in yield and crop production. The ingredients of these chemical pesticides are not only lethal to target arthropods but also for human beings. Therefore, the aim of present study was to find out the perception about the impacts of various pesticide usage on the health of cotton growing farmers in rural areas of District Muzaffargarh, Pakistan. Three villages were selected purposively and 130 farmers were approached to perform a questionnaire via face to face interview. Our findings revealed that a major proportion of the respondents (56.9%) were agreed that pesticides cause illness in cotton growing farmers. The results showed the perceptions of respondents that pesticides were responsible to cause various diseases to a great extent in farmers, like increase in headache (48.5%), fatigue (40.0%), insomnia (45.4%), dizziness (37.7%), hand...

42. Krishnan P, B. Ramakrishnan, K. S. Rao and R N Dash (2009) Simulation studies to characterize the impact of climate change on crop production and to identify strategies for adaptation and mitigation.  Book chapter In S.N. Singh (ed.), Climate Change and Crops, Environmental Science and Engineering  DOI 10.1007/978-3-540-88246-6 2, C _ Springer-Verlag Berlin Heidelberg 2009  , p39-63

Climate change and associated uncertainties have serious direct and indirect consequences for crop production and food security in agriculture-based developing regions. Long-term climate data analysis can identify climate risks and anticipate new ones for planning appropriate adaptation and mitigation options. The aim of this study was to identify near-term (2030) and mid-term (2050) climate risks and/or opportunities in the state of Bihar, one of India's most populous and poorest states, using weather data for 30 years (1980–2009) as a baseline. Rainfall, maximum and minimum temperatures, and evapotranspiration will all increase in the near-and mid-term periods relative to the baseline period, with the magnitude of the change varying with time, season and location within the state. Bihar's major climate risks for crop production will be heat stress due to increasing minimum temperatures in the rabi (winter) season and high minimum and maximum temperatures in the spring season; and intense rainfall and longer dry spells in the kharif (monsoon) season. The increase in annual and seasonal rainfall amounts, and extended crop growing period in the kharif season generally provide opportunities; but increasing temperature across the state will have considerable negative consequences on (staple) crops by affecting crop phenology, physiology and plant-water relations. The study helps develop site-specific adaptation and mitigation options that minimize the negative effects of climate change while maximizing the opportunities.

Abstract: Crop growth and productivity are determined by a large number of weather, soil and management variables, which vary significantly across space. Remote Sensing (RS) data, acquired repetitively over agricultural land help in identification and mapping of crops and also in assessing crop vigour. As RS data and techniques have improved, the initial efforts that directly related RS-derived vegetation indices (VI) to crop yield have been replaced by approaches that involve retrieved biophysical quantities from RS data.

The  real  wheat  delivering  nations  on  the  planet  are  India, China, USA, France, Russia, Canada  and  Australia.  Worldwide  interest  for  wheat  is  developing  at  1  percent  for  every  year.  Yield  development  and  profitability  are  controlled  by countless,  for example, hereditary capability of harvest  cultivar, soil, climate and administration factors, which  differ  essentially  crosswise  over  time  and  space. Early  expectation  of  harvest  yield  is  vital  for  arranging  and  taking  different  strategy  choices.  Numerous  nations utilize  the  ordinary  methods  of  information  accumulation for edit observing and yield estimation  in  light  of  ground  –  based  visits  and  reports.  These  techniques are subjective, expensive and  tedious. Exact  models have been  created utilizing climate  information  which  is  additionally  connected  with  various  issues. With  the  starting  of  satellites,  satellite  information  are  being  utilized  for  edit  checking  and  yield  expectation.  Most  investigations  have  uncovered  a  solid  connection between remotely detected NDVI and harvest yield. An  exertion  has  been  made  for  near  appraisal  of  these  procedures  the  subtle  elements  of  which  are  talked  about  in  the  paper.  The  present  paper  features  on  utilization  of  Remote  Sensing  (RS)  and  Geographical  Information System  (GIS) advancements  for  the wheat  land  estimation  for  Jalandhar  district,  Punjab,  India.  Wheat real estate estimation  is a standout amongst  the  most  essential  parameter,  if  zone  has  a  solid  between  yearly fluctuations while yield remains generally steady.  Single  date,  cloud  free  LISS-III  computerized  information  harmonizing  with  blossoming  phase  of  wheat  trim  was  utilized  for  land  estimation.  The  regulatory  limit  of  the  examination  region  (Jalandhar  District)  is overlaid  on  the picture  to  remove all pixels  having  a  place  with  think  about  locale.  ERDAS  IMAGINE  9.1  picture  examination  programming  and  Arc GIS 9.2 programming were utilized for information  preparing  and  investigation.  The  refined  preparing  insights  produced  utilizing  the  multi-groups  information  are  utilized  for  directed  order  utilizing  most extreme probability.

A study aimed at generating wheat yield maps of farmer’s fields by using remote sensing (RS) inputs was undertaken during the rabi season of 1998-99 in six villages of Alipur Block of Delhi State. RS derived leaf area index (LAI) were linked to wheat simulation model WTGROWS by adopting a strategy christened “Modified Corrective Approach”. This essentially uses an empirical relation of grain yield and LAI, which was derived from WTGROWS simulation model by running model for a combination of input resources, management practices and soil types occurring in the area. This biometric relationship was applied to all the wheat fields of the study area for which the LAI was derived from single acquisition of IRS LISS-III data (Jan 27, 99). The LAI–NDVI relation adopted was logarithmic in nature(R2=0.83) and was based on ground measurements of LAI in farmer’s fields in the same area. A comparison of predicted grain yield by the modified corrective approach and actual observed yield for the 22 farmer’s fields showed high correlation coefficient of 0.8 and a root mean square error (RMSE) of 597 kg ha-1 which was 17% of the observed mean yield. Thus linking of RS information and crop simulation model provides an alternative for mapping and forecasting crop yield under highly variable cropping environment of Indian farms, which is a pre-requisite for implementing Precision Crop Management (PCM).

Numerous modelling efforts have focused on maize as it is an important cereal crop for both human consumption and livestock production. Crop simulation and multi-linear regressions models can be used to quantify the likely potential impacts of climate change on maize growth and yield.

Potato is the most popular root crop and plays a crucial role in human nutrition worldwide. However, farmers in Sri Lanka face many challenges throughout at different stages of potato production. This study intends to provide context-specific actionable information using the Packages of Practice (PoP) model through an ICT-based mobile platform. To investigate farmers’ challenges, a field survey was conducted in major potato growing Districts in Sri Lanka. Primary data were obtained using a pre-tested structured questionnaire, formal and informal discussions from 100 potato farmers selected by purposive sampling technique. Low farmgate prices (37%), pest and disease attacks (32%) and high cost of farm inputs (20%) were the major challenges identified in the study area. These challenges were mainly due to the non-adoption of proper agronomic practices, lack of complete information at the right time, scarcity and low-quality agro-technology, etc. Further, identified decision support factors required for each stage in the farming life cycle for better cultivation. The PoP model was designed concerning the farmer context using Design Science Research methodology. Then the designed PoP was verified and validated by the experts in the domain. The ontological crop knowledge base was used to represent the PoP. Finally, knowledge in the PoP model is fed through a mobile application and then according to user requests, information is acquired from the knowledge base. The designed PoP will be modified based on the updated information/knowledge and user satisfaction/feedback. The proposed information flow will be continued cyclically. A mobile-based information system was taken as an effective tool to speed up the process of knowledge sharing among farmers by providing real-time information, promised to overcome common challenges faced by the potato farmers and achieve long-term sustainability in production.

AVAILABLE AT
[https://www.researchgate.net/publication/353622276_A_toxicity_study_of_microbial_extracts_and_synthetic_insecticides_against_Helicoverpa_armigera_and_natural_enemies_from_Peshawar_Pakistan]
[https://www.researchgate.net/profile/Abdul-Rehman-53/publication/320880198_FECUNDITY_LONGEVITY_AND_DEVELOPMENT_TIME_OF_PARASITOID_ACEROPHAGUS_PAPAYAE_ON_PAPAYA_MEALYBUG_PARACOCCUS_MARGINATUS/links/5a00b2074585159634beeb47/FECUNDITY-LONGEVITY-AND-DEVELOPMENT-TIME-OF-PARASITOID-ACEROPHAGUS-PAPAYAE-ON-PAPAYA-MEALYBUG-PARACOCCUS-MARGINATUS.pdf#page=37]

The experiment was conducted for the comparison of microbial extracts and synthetic insecticides viz Alternaria sp., Pythium sp., Bifenthrin (Brigade 10 EC Insecticide) and Flubendiamide (Belt 480 SC Insecticide), respectively against the Helicoverpa armigera. The experiment was laid out in RCB Design with 3 replications each. Results revealed that the synthetic insecticide, Bifenthrin was more effective, followed by Flubendiamide, Alternaria sp., while the least effective was Pythium sp. Results of Bifenthrin revealed highly significant among the tested treatments with larval population of 0.18 larvae plant-1 , fruit damage (76.58 kg ha-1), yield (3746.19 kg ha-1) and population density of biological control agents (0.39 insects plant-1). While results of Flubendiamide for larval population revealed 0.55 larvae plant-1 , followed by fruit damage 104.6 kg ha-1 , yield 2881.49 kg ha-1 and population density of biological control agents revealed 0.81 insects plant-1. However, Alternaria sp. recorded larval population of 0.94 larvae plant-1 , followed by fruit damage 131.38 kg ha-1 , yield 2432.76 kg ha-1 and population density of biological control agents 2.26 insects plant-1. Whereas, Pythium sp. recorded larval population of 1.29 larvae plant-1 , followed by fruit damage 18.63 kg ha-1 , yield 1974.97 kg ha-1 and population density of biological control agents 2.96 insects plant-1. However, the least effective were the control plots which recorded larval population 1.73 larvae plant-1 , fruit damage 20.25 kg ha-1 , yield 1614.14 kg ha-1 and population density of biological control agents 3.52 insects plant-1 .

The market for high-quality organic bread wheat (Triticum aestivum L.) is increasing in New England, USA, providing opportunities for organic dairy farmers to grow this alternative cash crop. Our objective was to determine the sustainability of eight 3-year crop sequences compared with a perennial forage baseline in long-term (25-year), well-managed, medium-sized organic dairy farm simulations. Systems included wheat (spring or winter) preceded by maize (Zea mays L.) silage, a 1-year-old perennial forage grass, or soybean as well as maize silage followed by maize silage or soybean. Farm net return was highest for the entirely grass-based system (US$742.15 cow–1). Higher winter wheat yields for soybean–wheat–grass resulted in 7% more income from feed sales ($1027) than spring wheat. Soybean followed by wheat reduced soil nutrient accumulation by 0.8 kg ha–1 year–1 for phosphorus runoff and leachate losses and for potassium accumulation (–17%); there was also a 4% reduction in water f...

Today’s Scenario is more or less like this.

1. Agricultural lands are used for industrial or mining or for land filling of Solid Wastes or for dwelling thus causing diminishing agricultural lands available.

2. Labour is becoming scarce and expensive discouraging farmers to grow.

3. Wild Animals, Rats and untimely rains, hurricanes, cyclones etc are damaging agricultural fields and thus reducing total available yields.

4. Pests and insects are becoming more and more pesticide resistant and reducing the yields.

5. Seasonal fluctuations in market prices sometimes lead to heavy losses to the farmers leading to suicides in many developing and under-delveloped countries where agriculture is the main occupation.

6. Younger generations are attracted to more remunerative occupations.

7. Over generations inherited lands got divided and became smaller with lesser cultivable land after volumes of the boundaries are increased.

In this context a need arises to solve all these problems.
A better proposition is to enable a farmer to use one seed which can yield one crop above the ground and simultaneously another crop under the ground like POMATO which was designed to produce Tomatoes above the ground and Potatoes under the ground; thereby farmer will be having a chance to have a better price atleast to one of them.

In this article efforts are made
1. to compile the available technologies
2. to better understand the protocols involved  in simpler terms
3. to use improvised versions of the methodologies to augment sustained world food production in the coming Years.

: Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and
nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to
anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERESMaize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha−1 (APSIM-Maize) and 0.84 t ha−1 (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m−2, soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.

Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R2) of 0.57 and mean absolute error (MAE) of 310 kg ha−1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.