Optimal Cropping Pattern Research Articles

Optimizing water-energy-food nexus index, CO2 emissions, and chemical pollutants under irrigation water salinity scenarios

The escalation of crises related to water quantity of quality, energy, food, and the environment has posed numerous challenges to sustainable development in the agricultural sector. This study seeks to establish an optimal and sustainable framework within the agricultural sector by meeting the objectives of the WEF (Water-Energy-Food) nexus index and environmental. For this, we individually optimized the objectives: maximizing the WEFN index (O1), minimizing CO2 emissions (O2), reducing chemical fertilizer consumption (O3), minimizing chemical pesticide usage (O4), and maximizing gross margin (O5) under various electrical conductivity (EC) scenarios (optimistic, moderate, and pessimistic) within the irrigation network of the Jiroft plain in Iran. In the second stage, we employed a Multi-Objective Programming (MOP) Model to attain these objectives using a weighted sum approach simultaneously. The results of the first stage revealed that under moderate and pessimistic EC irrigation water scenarios, significant alterations in the crop selection within the optimized models for O1 and O5 objectives occurred compared to the baseline scenario, which did not consider EC irrigation water. Specifically, in the pessimistic scenario with an EC irrigation water level of 2.25 ds/m, crops such as onions, tomatoes, and potatoes were replaced within the optimal cropping pattern by wheat, barley, and green-maize, leading to a notable decline in program efficiency. The results from the MOP optimization model indicated that in the pessimistic EC irrigation water scenario, the WEFN index, CO2 emissions, fertilizer consumption, pesticide consumption, and gross margin would change by approximately +12%, −48%, −28%, −8%, and −74%, respectively, compared to the baseline scenario. Consequently, an increase in EC irrigation water, despite improving environmental conditions, would substantially diminish economic profit. Thus, overlooking the impact of EC irrigation water in the WEF nexus analysis could result in misleading conclusions.

Assessing Groundwater Use Efficiency and Productivity across Punjab Agriculture: District and Farm Size Perspectives

While previous studies have focused on the technical aspects of groundwater extraction and optimal cropping patterns, there is a notable lack of research on the socio-economic factors affecting groundwater access and agricultural productivity, especially at a micro-level in Punjab, India. This study, therefore, investigates the water use efficiency (WUE) and economic water productivity (EWP) of paddy and wheat crops across diverse farm sizes and districts in Punjab, offering critical insights into agricultural water management. The study is based on primary data from Punjab, India, with Sangrur, Jalandhar, Pathankot, and Bathinda districts selected for groundwater profile analysis via multistage random sampling of 246 farmers. Notably, Punjab exhibits high EWP for wheat, surpassing the national average. However, disparities exist across districts, emphasizing the importance of localized interventions. Larger farms consistently demonstrate higher WUE and EWP, underlining the significance of scale in optimizing water inputs. Factors such as farm size, crop variety, and regional variations significantly influence WUE and EWP. Tailored approaches for marginal farmers and districts with lower efficiency are crucial for promoting sustainable agricultural practices. The findings underscore the need for targeted policy interventions to enhance water use efficiency and productivity in Punjab’s agriculture sector.

Providing optimal cropping patterns and water consumption according to monitored and forecasted drought conditions

Providing optimal cropping patterns and water consumption according to monitored and forecasted drought conditions

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Mitigating Nitrate Concentration Using an Optimal Cropping Pattern Developed by Linking a Soil and Water Assessment Tool with Evolutionary Optimization

The unplanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to simulate the nitrate concentrations for different crops at the river basin scale by applying the Nash–Sutcliffe model efficiency coefficient (NSE) as a measurement index. Then, a novel model was developed to optimize the cultivation pattern by applying different metaheuristic algorithms. Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was applied as a decision-making system to select the best optimization algorithm. The results demonstrated that using a SWAT in the optimization model structure is a robust method for the design of an optimal cultivation pattern. The Nash–Sutcliffe model efficiency coefficient (NSE) was 0.74, demonstrating the robust predictive skills of the water quality model. The decision-making system indicated that particle swarm optimization and shuffled complex evolution were the best evolutionary algorithms to optimize the cultivation pattern using the proposed method. The proposed method opens a new window regarding the optimization of cultivation patterns in agriculture and provides an environmental-based optimization to design cultivation patterns on the catchment scale.

OPTIMAL CROPPING SEQUENCE OF SMALL-SCALE FARMS IN THE RAIN-FED SECTOR OF GADARIF STATE- SUDAN(1)

The study aimed to examine the Optimal Cropping Sequence of the main crops grown under the rain-fed sector of Gadarif state - Sudan: such as sorghum, millet, sesame and groundnuts. The study used both primary and secondary data. Multistage random sampling technique was used to collect primary data on small-scale farms from three villages of the state by means of questionnaire as a sample farmer from 175 of Gadarif State, during 2017 -2018 season. Secondary data were collected from Central Bureau of Statistics and Federal Ministry of Agriculture. Linear programming technique was used to determine the optimal cropping sequence of small-scale farms (less than 210ha) in the rain-fed sector of Gadarif state. Spread sheet of the excel solver was used to run the analysis. The results revealed that the current cultural practices and crop sequence, in which sorghum do not come after sesame, was not the optimal one. Food-crops (sorghum and millet) did not enter the optimal cropping pattern under the current sequences and policy measures, with sesame dominated the total land. The crop sequence of groundnuts, sesame, sorghum and millet proved to be the optimal cropping pattern that improves farmer’s returns. Under such sequence, the four crops entered the optimum plan and farmers’ returns exceed of the current situation by 181.94%, but if this sequence is used with the recommended full package of technological improvement, then farmers’ net returns will increase three times than the current practices. Small-scale farmers should be encouraged to adopt the crop sequence in which sorghum come after sesame. They should also be encouraged to change their mind to become commercially oriented (by following the crop sequences with greatest returns according to local and international demand and expected prices. Even if the subsistence crop is excluded).

Temporal Trend Analysis of Annual, Maximum, and Seasonal Rainfall in Selected Weather Stations in Region IV-A, Philippines

Temporal variability analysis of rainfall is important for planning agricultural crop production activities, designing water resource systems, and conducting climatological research, particularly climate change analysis. This study aimed to analyze the temporal trends of annual (total and 1-d maximum) and seasonal (total) rainfall observed in Region IV-A. Historical rainfall data were collected from seven weather stations scattered all over the region. Each dataset was initially subjected to a series of homogeneity tests (standard normal homogeneity, Pettitt’s, Buishand’s range, and von Neumann ratio), followed by trend analysis using both linear regression analysis and non-parametric methods [Mann-Kendall (MK), modified MK (MMK), and Sen’s slope (SS) estimator]. All data series proved to be homogeneous except for the annual and seasonal (DJF) total rainfall in Sangley Point station. The results of the homogeneity test in Sangley Point in annual and DJF timescale suggest the consideration of the breakpoint on the years of 1984 and 1999, respectively, for trend analysis. Temporal trend based on linear regression showed a weak correlation between rainfall of various time scales and time in years, with only three stations showing a significant trend at a 5% significance level – namely, Los Baños (DJF, 2.86 mm/yr), Tayabas (DJF, 7.99mm/yr), and Sangley Point (JJA, 9.32 mm/yr). On the other hand, both MK and SS indicates a significant increase in the seasonal (DJF) total rainfall in both stations of Ambulong (1.04 mm/yr) and Los Baños (2.68 mm/yr). Overall, the results of trend analysis revealed an increasing trend in the cumulative rainfall across timescales in most stations in the region which has an implication on the timing of the optimum cropping patterns in agricultural areas and on the hydrology of the watersheds in the region. This study has generated new knowledge and information that could prove useful for agricultural production management and for proper planning, design, and management of water resource systems.

Improving Environmental Water Supply in Wetlands through Optimal Cropping Patterns

This study improves the environmental water supply in a wetland using a novel framework in which the environmental impacts due to irrigation supply and the economic losses for agriculture are minimized through the proposal of an optimal cropping pattern that changes the total cropping area and cultivated area of each crop. The ecological degradation functions for rivers and wetlands were developed using a fuzzy approach and data-driven model. The net farming revenue was considered as the economic index to maximize benefits. The root mean square error (RMSE) and the Nash–Sutcliffe model efficiency coefficient (NSE) were applied to evaluate ecological models. According to the results, the optimal cropping pattern simultaneously minimizes environmental impacts due to irrigation supply and maximizes farmers’ benefits. The optimal cropping pattern provides more than 50% of the ideal net revenue on the catchment scale, which means that ecological degradations due to reductions in inflow in rivers and wetlands, as well as farmers’ revenue losses, are minimized simultaneously. Furthermore, the results indicate that cropping patterns should be dynamic, which means that changing the cropping pattern annually based on the available water is essential to mitigating ecological impacts. This study demonstrates that the linking of cropping pattern optimization and environmental flow simulation in freshwater bodies should be considered in land-use policies due to the impact of cropping patterns on environmental degradation in wetland catchments.

A new interval meta-goal programming for sustainable planning of agricultural water-land use nexus

Meta-Goal Programming (MGP) is a simultaneous cognitive evaluation of the degree of achievements for original decision goals considered in a GP model. However, in most real-world situations, environmental coefficients and related parameters are not easily available. In such a situation, the decision-maker must consider various conflicting targets in a framework of uncertain aspiration levels at the same time. On the other side, Interval Programming (IP) is a method used to increase the range of available decision-maker preference structures in GP. In the perspective of solving the conflicts between agriculture and water use towards sustainability, this paper proposes an Interval Meta-Goal Programming Model (IMGPM) dealing with imprecision in data that covers interval coefficients, target intervals, and interval bounds of meta-goals. This novel methodology has been tested in a study area in Iran to validate its added value in solving conflicting uses of natural resources by economic sectors. This integration together with its application for sustainable optimal cropping patterns (agroecosystem planning) represents a novelty in the field of ecological modeling. The management solutions of our method in terms of land allocation are different from those in Sen and Pal (2013) model. In the case of Iran, many socio-ecological-economic strategies and policies should be necessary for improving the agricultural sector. More specifically, on the basis of rainfall amounts and spatial patterns, this approach can represent a decision-support system able to define strategies for additional water storage useful to support crop production. Furthermore, the availability of water together with the sustainable use of fertilizers can mitigate the risk of land degradation, guaranteeing people employment, food security, and economic profits. Although the present methodology seems to solve the problem of multi-goals decision-making, the inclusion of spatial relationships is able to introduce dependencies between the management of land use in adjacent areas, making the present approach nearer to real-world functioning.

Climate-smart management for increasing crop yield and reducing greenhouse gas emission in Beijing-Tianjin-Hebei region, China

Ensuring stable and high yields of crops and reducing greenhouse gas (GHG) emissions from farmland through climate-smart managements in China are of great significance for mitigating global warming. In this study, the effects of climate-smart managements on crop yields and GHG emissions during 1981–2020 in Beijing-Tianjin-Hebei region of China were explored based on the APSIM crop model. Four cropping patterns and four fertilization measures were set according to the local planting experiences and existing research results. The results showed that proper fertilization management and effective cropping pattern were conducive to the improvement of total crop yield and reduction of GHG emissions in Beijing-Tianjin-Hebei region of China. The total yield of winter wheatsummer maize (W-E) cropping pattern under F4 fertilization measure (400 kg·ha−1) was the highest, ranging from 26,088.82 kg·hm−2 to 31,430.62 kg·hm−2. Among the four fertilization measures, the GHG emission intensity of W-E cropping pattern was the highest, followed by winter wheatsummer maizeearly maize (10 days earlier) (W-M-ME10) and winter wheatsummer maizeearly maize (20 days earlier) (W-M-ME20) cropping patterns. However, the GHG emission intensity of summer maize (ME) cropping pattern was the lowest. As a whole, although the GHG emissions of ME cropping pattern were low, and the yields of summer maize were also low, which was difficult to ensure the local food security needs. As the traditional cropping pattern in Beijing-Tianjin-Hebei region, W-E cropping pattern could ensure the need for high yield, but it also caused excessive GHG emissions in the soil. Comprehensively estimating the environmental effects and crop total yields, the W-M-ME20 cropping pattern was considered an optimal cropping pattern in Beijing-Tianjin-Hebei region of China at present or in the future. Our results have significant ramifications for local agriculture to adapt to continuous global warming as well as for enhancing studies on climate change's effect.

Cropping pattern optimization considering water shadow price and virtual water flows: A case study of Yellow River Basin in China

Cropping pattern determines the agricultural water use requirement and efficiency, as well as economic benefits of crop production. Agricultural water resources include blue water (irrigation water) and green water (soil moisture from precipitation), which have different shadow prices. The virtual water (VW) flow embedded in the interregional food trade causes burden shifts of water resources pressures in space. However, these intertwined economic and social effects have been neglected in regional cropping structure management. Using the case of the Yellow River Basin in China, here, we propose a two-stage multi-objective cropping pattern optimization scheme to maximize crop economic output, while minimize blue water scarcities (the first stage), and considered the criteria of maximum economic benefits of the interprovincial crop-related VW flows based on the shadow prices of crop green and blue water use (the second stage). Results show considerable differences in shadow prices of crop water use by colures, crops and location. With the optimized cropping pattern, which appropriately expands the planting scale of vegetables with higher water shadow price and comparative advantage and reduces the crop planting with lower water shadow price and intensive blue water consumption (e.g., soybean and wheat), blue water scarcity can be alleviated by ∼20%, combined with a ∼5% increase in crop economic output and up to ∼3% (800 million USD) higher benefits of VW flows. The premise to achieve above goals is to improve crop water resources utilization efficiency and break down the barriers of dietary preferences and trade policy.

Evaluation of water balance in the Jaro Irrigation area, Tabalong Regency, South Kalimantan Province

In the Tabalong Regency, South Kalimantan Province, many irrigation networks have not worked optimally. The Jaro Irrigation Area is one of these irrigation networks, the majority of available fields are planted with Paddy. Evaluation of irrigation water to irrigate paddy fields is very important for optimal growth and development of paddy. The objective of this research is to evaluate the water balance, the potential area for paddy field area development can be obtained by applying the optimal crop pattern. Analysis of the rainfall data used in this study with statistical analysis in the form of validation and correction of rainfall data. Rainfall data was obtained from the Tropical Rainfall Measurement Mission (TRMM) and the Meteorology, Climatology, and Geophysics Agency (BMKG) Jaro Station in the period 2013-2019, the data using statistical analysis obtained a correlation coefficient and regression equation. The regression equation is used to obtain the corrected rainfall value which will be used in the hydrological analysis. Water requirement analysis with several cropping pattern scenarios. Calculation of evapotranspiration using the Modified Penman method. The F.J Mock method is used to develop the discharge value. Dependable discharge, 80%, is used to estimate water availability. Furthermore, an evaluation of the water balance is carried out for each scenario, and the result of information on surplus or deficit conditions can be obtained each month. Analysis of water demand discharge based on planting scenarios is divided into three. First Scenario with a high-yield paddy - a high-yield paddy according to the existing conditions at the research location. The second scenario is with a high-yield paddy - high-yield paddy - beans and the third scenario is with a high-yield paddy - paddy. According to the results of the water balance evaluation for the three scenarios, the potential area can be reached up to 900 ha from 850 ha with the chosen crop pattern in scenario number 2.

Framing a novel holistic energy subsystem structure for water-energy-food nexus based on existing literature (basic concepts)

It is interesting to note that the country of Iran is essential in terms of energy production and consumption, and the economy of Iran is mainly dependent on energy revenues. Therefore, thermal and hydropower plants consume water to produce various energy carriers. Considering that Iran is suffering from water stress, the nexus of water and energy becomes very important. This paper frames a comprehensive structure for Iran's energy subsystem within the Water, Energy, and Food (WEF) nexus system. The energy subsystem's supply and demand side in the proposed framework are formulated using data and physic-based equations. The presented framework addresses most interactions between WEF subsystems in a dynamic and adaptive setting. It is shown that through analysis of binding interactions between WEF, different management scenarios can boost the flexibility of the supply and demand side of the energy subsystem. In addition, by incorporating this framework, the water subsystem will manage the allocated and consumed water on the supply side and arrive at the most desirable outcome for the water sector. Also, the optimal cropping pattern could be evaluated based on energy consumption.

The impact of water conservation policies on the reallocation of agricultural water-land resources

The effects of policies on farmers' profit have a key role in their adoption by the farmers and their operationalization at the basin level. The present research explored the effects of water resources conservation policies on optimal cropping pattern and farmers' profit in the Zayanderud basin located Iran using a combined hydrological-economic model composed of WEAP and multi-objective (MOP) models for the assessment of the effects of the A2 climate change scenario with or without the conservation policies. The results showed that climate change will reduce crop yields by 0.5–4%, will decrease water availability by 12–19%, and will increase crop water requirements by 0–4%. It was also found that the adoption of the policy of changing irrigation technology along with the policy of limiting groundwater use will alleviate the effects of climate change on the farmers' profit, will enhance their planned return per unit area by 5%, and will increase the economic efficiency of water use from 5,283 to 6,002 IRR/m3. But, the policy of increasing water price cannot improve the farmers' profit at the basin level and the economic efficiency of water use although it can reduce water use. So, proving the improvement of profit and livelihood of the farmers by applying combined water resources conservation policies can play a significant role in motivating farmers to accept these policies, operationalizing optimal water resources management, and resolving conflicts over water use in this basin.

OPTIMIZATION OF PLANTING PATTERNS ON PADDY PLOTS IN PURI VILLAGE, PURI DISTRICT, MOJOKERTO REGENCY, EAST JAVA USING A LINEAR PROGRAM

The research site is located in one of the hamlets in Puri village, which has an agricultural land area of approximately 25 hectares. Because of its fertile soil conditions, the land is very suitable for agriculture. However, the agricultural productivity in Puri village has decreased due to changing weather conditions. Puri village, Puri district, Mojokerto regency, has a flat landscape. The duration of the dry season is slightly longer, affecting the availability of water and causing a change in the initial cropping period. As a result, the cropping pattern in the village is not appropriate, leading to a decrease in agricultural productivity. To solve these problems, an optimal cropping pattern needs to be planned to increase agricultural productivity in the village. In order to plan the optimal cropping pattern, the first thing that needs to be done is to calculate the availability of existing water. Then, the need for irrigation water should be calculated. Using the two sets of calculation data, the optimal cropping pattern can be planned with linear program. The analysis is revealed that Puri village has an optimal cropping pattern with a Paddy-Corn-Corn cropping model and an initial planting period in December period 2.

Multiobjective Intuitionistic Fuzzy Optimization Approach in Optimal Irrigation Planning and Operation of Reservoir

A Multiobjective Intuitionistic Fuzzy Linear Programming model is developed to get an optimal cropping pattern in the command area of the Ukai-Kakrapar Irrigation project in Gujarat, India. Four conflicting objectives, namely, maximization of net irrigation benefits (NIB), maximization of employment (EG), minimization of cultivation cost (CC), and maximization of revenue generation on account of industrial and municipal supplies (MI) were optimized for their crisp linear programming (LP) solutions. Intuitionistic Fuzzy Optimization Multi-objective fuzzy linear programming (IFO MOFLP), IFO MOFLP with hesitation index, and two-phase IFO MOFLP models were developed from their LP solutions. The TPIFO MOFLP model has been found to perform better than other variant of IFO MOFLP models and the MOFLP model (Average operator Case-I) developed by Mirajkar and Patel in J Water Resour Plan Manag 142(11):1–16, 2016 for the same command area with reference to the uncertainty parameters like degree of acceptance (α), rejection (β), and hesitation index (π) for 75% dependable inflow condition. The recommended TPIFO MOFLP model also gives higher irrigation intensity (112.19%) than Average Operator Case-I (104.60%) for the whole command area. The implementation of TPIFO MOFLP model in the command area is likely to give the values of NIB, EG, CC, and MI as Rs 10,836.19 million, 34,980.4 thousand workdays, Rs 5,672.23 million, and Rs 2,314.03 million, respectively, with α = 0.68, β = 0.19, and π = 0.13. The suggested model with new parameters like α, β, and, π, would assist the decision makers to apply the same on real world problems with greater certainty.

Optimizing agricultural cropping patterns under irrigation water use restrictions due to environmental flow requirements and climate change

This study proposes a reservoir operation optimization framework to maximize the regional agricultural profit under the constraints of downstream environmental flow requirements and climate change. Three climate change models—CanESM2, MIROC5, and NorESM1-M—and the soil and water assessment tool (SWAT) were used to simulate the reservoir inflow in future periods under uncertainty. Minimum and ideal environmental flow regimes were embedded in the structure of the reservoir operation model to optimize the environmental flow needs and water supply and assess their tradeoffs. Cropping pattern optimization was used to maximize farmer profit. Particle swarm optimization was applied in the optimization processes. The method was applied to a case study in the Tajan River basin, Iran, with the results showing the environmental flow regime considerably reduces irrigation supply and has significant impacts on farmer profits. The results showed that cropping pattern optimization was not an effective strategy to mitigate the economic impacts of climate change under environmental flow constraints, but this assessment may not be generalized to other areas. Uncertainties related to the climate change models are a notable weakness of the approach and should be considered in future studies.

Using the geographica information systems and remote sensing to select the optimal cropping pattern in the new lands in Fayoum Government.

Using the geographica information systems and remote sensing to select the optimal cropping pattern in the new lands in Fayoum Government.

Optimization of Planting Patterns in South Sulawesi

South Sulawesi is one of the nine provinces in Indonesia that has the highest contribution to the national income and its leading sector in agricultural crops including rice. The planning of planting patterns is highly dependent on the size of the intensity of rainfall in a particular area. Therefore, these conditions can have an impact on the level of suitability of crops to be planted. The purpose of this study has been to analyze the water balance and identify the optimal planting pattern. On the basis of rainfall data obtained from 4 rainfall stations, which are Mangkoso station, Manuba station, Mareppang station, and Ralla station, during the period of 25 years (1996 – 2020), the analysis to determine the average semi-monthly rainfall can be carried out. The results show that the maximum value of irrigation water requirements for rice-rice-palawija planting pattern obtained for simulation 1 has occurred on 1 September by 1.66 m3/s, while the minimum value has occurred on 1 January, 2 May, and 2 December by 0.23 m3/s. As a conclusion, the maximum value in simulation 3 has occurred on 2 October by 1.65 m3/s, while the minimum value has occurred on 1 February and 1 June by 0.23 m3/s. The optimization results show that the most optimal cropping pattern is planting 100% of rice in the first and second planting seasons with an area of 717 Ha and 71% planted with palawija in the third planting season of 509.68 Ha.

Optimal cropping pattern based on short-term streamflow forecasts to improve agricultural economic benefits and crop productivity under uncertainty conditions

ABSTRACT Accelerated urbanization has led to diminished land for agricultural activities. Riverine ecosystems play an important role in allocating fertile lands to support agricultural activities. A substantial component of the uncertainty in agricultural productivity comes from seasonal variations linked to inter-annual climate fluctuations. Therefore, understanding the complicated phenomena of streamflow in a riverine environment is important for agricultural and water resources decision making. The present work focuses on forecasting monthly to seasonal streamflow using persistence flow, historical analogues, and artificial neural network approaches. Based on these forecasts, decisions on cropping patterns were made by developing an optimization framework using the constrained linear programming and inexact multiobjective fuzzy linear programming approaches. The proposed fuzzy programming approach was found to be beneficial in producing fair and stable solutions under uncertainty. The findings reveal that integrating forecasting and optimization knowledge could aid in precisely evaluating ecosystem services and meeting rising food demand.