Efficiency Of Agricultural Production Research Articles

Productivity model and experiment of field crop spraying by plant protection unmanned aircraft.

Traditional agricultural production requires numerous human and material resources; however, agricultural production efficiency is low. The successful development of plant protection unmanned aerial vehicles (UAVs) has changed the operation mode of traditional agricultural production, saving human, material, and financial resources and significantly improving production efficiency. To summarize the process of improving the productivity of plant protection UAVs, this study established a productivity calculation model of UAVs based on the time composition of the UAV agricultural plant protection process, including spraying, turning, replenishment, and transfer times. The time required for the unmanned aircraft application process was counted through years of tracking the application process of eight different plant protection unmanned aircraft. Plot lengths of 100, 300, 500, 700, 1,000, 1,500, 2,000, 2,500, 3,000, and 3,500 m were established to calculate the theoretical productivity. The results showed that the productivity of different types of plant protection UAVs increased with an increase in plot length in the range of 100 to 1,500 m; however, when the plot length reached a certain value, the productivity growth rate slowed down or even decreased slightly. Simultaneously, based on the working area per 10,000 mu, the recommended plot length and the number of configured models for different models were recommended. If the plant protection UAV was distinguished by electric and oil power, the time utilization rate of electric plant protection UAVs was 72.7%, and the labor productivity was 56.4 mu/person·h. In contrast, the time utilization rate of the heavy load oil-powered plant protection unmanned aircraft was 86%, and the labor productivity was 63.5 mu/person ·h. This study can support plant protection UAV enterprises to optimize equipment efficiency, provide evaluation methods for the operation efficiency assessment of plant protection UAVs, provide a reference for the selection of plant protection UAVs, and provide a basis for field planning.

Resilience or efficiency? Strategic options for sustainable development of agricultural systems in ecologically fragile areas of China

Sustainable development of agricultural systems is crucial for ensuring global food security, and resilience and efficiency are important topics for sustainable development of agricultural systems. Therefore, it is important to study the coupling and coordination relationship between agricultural system resilience and agricultural production efficiency, and explore the sustainable development model of agricultural system accordingly. Using statistical data from 2000 to 2020, we constructed a system of indicators for sustainable development of agricultural systems, and assessed the coordination status and interaction of agricultural system resilience and agricultural production efficiency in 49 county-level administrative units in the Loess Hilly Region Gansu Section (LHRGS) of China with the help of the coupling coordination degree (CCD) model, bivariate Moran's I index, and geographically weighted regression (GWR) model. The results show as follows: (1) The level of resilience of the LHRGS agricultural system and the level of agricultural production efficiency shows a continuous upward trend and a robust growth state. (2) High-value areas of the LHRGS agricultural system resilience are concentrated in counties with better resource endowment, and high-value areas of agricultural production efficiency are distributed in regions with stronger innovation capacity, and there is a moderate level of coupling relationship between them. (3) The sustainable development capacity of the agricultural system is gradually improving, but there is a certain degree of heterogeneity between agricultural system resilience and agricultural production efficiency. (4) Agricultural input factors have significant effects on the sustainable development of the agricultural systems, and significant spatial differences are found in the effects of different agricultural input factors on the sustainable development of agricultural systems. Based on the findings of this study, policy recommendations for the sustainable development of agricultural systems in ecologically fragile areas are proposed.

An Automated Irrigation and Monitoring System Using IoT: A Survey

The Internet of Things (IoT) is revolutionizing the agricultural business and assisting farmers to overcome the enormous obstacles they confront. Agriculture is a vast area that needs to be studied. About 80% of India’s surface water is used for agriculture. It can be used for a plethora of applications. Proper irrigation is still a challenge in most agriculture practices. Farming is critical to the development of a nation like Ethiopia. Agriculture-related issues have consistently stymied the country's development. The difficulties that the Internet of Things (IoT) may handle, include regulating cattle, sustainability, and plants and soil. The only solution to this challenging problem is contemporary agriculture and technology for sustainable water use by updating traditional agricultural techniques. By solving issues in farming, having to cut IoT applications boosts agricultural production's efficiency, quality, durability, and cost-effectiveness. This reduces the risk of over or under-watering, resulting in healthier plants and higher yields. Today's enormous and tiny farms may apply IoT to monitor the location that may monitor crop development, identify insects, and assess water content as well as their smart linked cultivators and irrigation. Moreover, the system also reduces the need for manual monitoring and intervention, saving time and reducing labour costs. This project improves the watering operation while monitoring soil parameters including water content, temperatures, and conductivity. Node-MCU is applied to make judgements. If there is a deviation from the impact on succeeding, the user is warned by text message. This guarantees the whole system's health and it enables the system to use an appropriate quantity of water which avoids over/under irrigation.

Long-term responses in different karst agricultural production systems to farm management and climate change: A comparative prefecture-scale study in Southwest China

Rapid shifts have occurred in the agricultural production systems of the karst regions in Southwest China to meet increasing food demands. The responses of agricultural production vary under different degree of rocky desertification. However, the interactive effects of related driving factors on the food production sustainability of local crops are unknown. In this study, we quantified the impacts of seven key indicators—population, cropland area, multiple cropping index, irrigation area, fertilizer use, temperature and precipitation—on agricultural production in three rocky desertification grade regions from 1960 to 2011. We also classified these indicators into three categories: population, farm management and climate change. The results showed that farm management and population were the main drivers of increased food crop production, contributing 79.7% and 17%, respectively. In contrast, climate change had a limited influence on agricultural production. With increases in crop yield, the influences of some indicators were enhanced, and the negative impact on the environment was also increased; therefore, the sustainability of agricultural production systems could be adversely affected in the future. The agricultural production systems in mild rocky desertification areas (Zunyi) could meet the current food demands, while the areas with moderate and severe rocky desertification (Bijie and Qianxinan, respectively) are faced with substantial pressure. Although continuous improvements in agricultural production efficiency due to advances in modern science, technology and management have partly mitigated environmental pressure, rocky desertification poses a challenge to the sustainable development of agriculture in Southwest China. These findings provide a reference for adjusting farm management practices and climate change countermeasures in regions with different degrees of rocky desertification to achieve agricultural production sustainability in the future.

Statistical analysis and modeling of factors of resource potential of agricultural development in the region

In modern realities, the issue of effective use of the existing resource potential of the regions is acute, the solution of which directly affects the effectiveness of the functioning of industrial complexes of both individual territories and the country as a whole. Agriculture consists of a system of interconnected industries that produce various types of agricultural products, ensuring the food security of the state and the quality of life of the population. The purpose of the work was to analyze the resource potential of regional agriculture and identify a set of relevant factors (based on processing the flow of statistical data) on the example of the Orenburg region. The economic and statistical apparatus using the Statistica software package was used as a tool for carrying out correlation and regression analysis and building models. The article analyzes various interpretations of the concept of "potential". The components of the resource potential are considered and the factors characterizing them are ordered. A comparison of a number of indicators of agricultural production (the volume of agricultural production in millions of rubles, the size of the acreage of crops, the volume of gross harvest of grain and potatoes, the number of cattle and pigs) by regions of the Volga Federal District is given. The leading subjects of the district have been identified, among them the Orenburg Region, on the materials of which methods of correlation and regression analysis have been implemented and a set of models has been built. As an example, an econometric model of the impact on the volume of agricultural products of a number of factors (characterizing the resource potential of the region) is given, including the area of agricultural land, the average annual number of agricultural workers, the introduction of organic and mineral fertilizers per hectare of crops, the cost of fixed assets by type of economic activity, the volume of investments in fixed capital of agriculture farms. The analysis of the variables included in the model is carried out. Taking into account the identified factors, a set of measures aimed at improving the efficiency of agricultural production in the region is formulated.

Soybean Leaf Disease Detection

Abstract: With the continuous integration of computer technology into agricultural production, it also reduces personnel costs while improving agricultural production efficiency and quality. Crop disease control is an important part of agricultural production, and the use of computer vision technology to quickly and accurately identify crop diseases is an important means of ensuring a good harvest of agricultural products and promoting agricultural modernization. In this paper, a recognition method based on deep learning is proposed based on soybean brown spot. The method is divided into image pre- treatment and disease identification. Based on traditional threshold segmentation, the pre-processing process first uses the HSI colour space to filter the information of the normal area of the leaf, adopts OTSU to set the threshold to segment the original image under the Lab colour space, and then merges the segmented images. The final spot segmentation image is obtained. Compared with the renderings of several other commonly used methods of segmentation, this method can better separate the lesions from the leaves. In terms of disease identification, in order to adapt to the working conditions of large samples of farmland operations, a convolutional neural network (CNN) of continuous convolutional layers was constructed with the help of Caffe to extract more advanced features of the image. In the selection of activation functions, this paper selects the Max out unit with stronger fitting ability, and in order to reduce the parameters in the network and prevent the network from overfitting, the sparse Max out unit is used, which effectively improves the performance of the Max out convolutional neural network. The experimental results show that the algorithm is superior to the algorithm based on ordinary convolutional neural network in identifying large sample crop diseases.

The impact of science and technology services on agricultural income of rural household: An investigation based on the three northeastern provinces of China

Science and technology services (STS) are requirement for sustainable agricultural development and are hypothesized to be crucial for raising rural households' income. However, evidence regarding the impact of STS on rural household income has primarily been limited to qualitative research, with limited quantitative research conducted to date, resulting in a lack of clear consensus. This study uses survey data from rural households in poor rural counties in the three northeastern provinces of China to examine the mechanism behind the impact, heterogeneity, and asymmetry of STS on rural households' agricultural income. Our results show that STS has income-promotion effect, which is enhanced with income level. All subdimensions of STS examined have promotional effects, including technical training, industrial demonstration, information services, and organizational drive; moreover, technical training and information services have the greatest and least impacts, respectively. These effects are more pronounced among rural households with young, male and highly educated householder, poor, have received STS for longer periods or adopted green production techniques. These effects are attained through improved agricultural production efficiency, capabilities to adopt new technologies, endogenous motivation, and lowered costs. The findings have important implications for scholars and policymakers in terms of enhancing effectiveness of STS for disadvantaged groups.

Spatial-Temporal Pattern of Agricultural Total Factor Productivity Change (Tfpch) in China and Its Implications for Agricultural Sustainable Development

With increasing tension between humans and land, and arising pressure on food security in China, the improvement of total factor productivity is important to realize agricultural modernization and promote rural revitalization strategy. In this study, we applied the DEA-Malmquist index method to measure the growth of China’s agricultural total factor productivity and its decomposition indexes at the prefecture-level city scale from 2011 to 2020. We found the average annual growth rate of agricultural total factor productivity was 4.5% during this period, with technical change being the driving factor and technical efficiency change being the suppressing factor. There is an initial decrease and then an increase in the Dagum Gini coefficient. The cold and hot spot areas of agricultural Tfpch were clearly formed. During the decade, the gravity center of agricultural Tfpch has migrated from the northeast to the southwest in general. Based on the characteristics of agricultural Tfpch, China is classified into four zones. In the future, the Chinese government should balance the government and the market mechanism, improve the agricultural science and technology innovation system and technology adoption promotion system, and implement classified policies to improve agriculture production efficiency.

The Effect of Internet Information Dependence and Land Transfer on Agricultural Production Technical Efficiency of Households

In this essay, the author studies the relationship between internet information dependence, land transfer, and agricultural production technical efficiency based on the CFPS database, including observations from households from 25 provinces in China. The stochastic frontier approach is adopted to predict the technical efficiency of each household, and then OLS, group regression, and regression under interaction terms are applied, respectively. As a result, leasing land from others and internet information dependence are proven to have a positive impact on technical efficiency, while only in the case that the house owner does not depend on the internet to acquire information, the effect of leasing land from others is significant. Though the interaction term between internet dependence and land transfer is not significantly negative, internet dependence does have a substitution effect on land transfer in terms of improving technical efficiency. The result sheds light on developing countries striving to increase agricultural production efficiency and thus presents possible suggestions for peasants to gain better economic status.

The Impact of Digital Transformation of Circulation Industry on The Circulation Efficiency of Agricultural Products

This paper aims to study the impact of the digital transformation of the circulation industry on the efficiency of agricultural product circulation. Through literature review and empirical analysis, this paper finds that digital transformation can promote the improvement of the circulation efficiency of agricultural products, mainly in the aspects of informatization, logistics and intelligence. Digital transformation can improve the information transparency of agricultural products, reduce circulation costs, increase circulation speed, and shorten the length of the supply chain. Digital transformation can also improve the transportation efficiency of agricultural products through the application of logistics technology, and further improve the circulation efficiency. Digital transformation can also improve the quality and safety of agricultural products and increase consumers' trust in agricultural products through the application of intelligent technologies. This paper proposes the impact mechanism of digital transformation on the circulation efficiency of agricultural products, and puts forward policy recommendations to promote the application of digital transformation in the circulation of agricultural products.

Implementation of Efficiency Tracking and Evaluation Algorithm for Agricultural Product Circulation Based on Radio Frequency Data Tags and Android Platform

The article believes that the circulation efficiency of agricultural products, especially the efficiency of the circulation system of agricultural products, is the ratio of circulation output to circulation cost in the process of circulation of agricultural products. The circulation efficiency of agricultural products cannot be measured by a single indicator. The article argues that the PCA-DEA combination evaluation model can be constructed by using the market integration degree, market concentration degree, technical efficiency, consumer satisfaction, circulation spread, transaction costs, and using the principal component weight constraint cone to objectively assign DEA values. At the same time, it is necessary to develop a circulation mode of agricultural products led by leading enterprises, promote the intensive processing of agricultural products, and improve the circulation efficiency.

Management of material resources in the context of ensuring the sustainability of the agroindustrial complex

Substantiated are approaches to improving the processes of managing material resources, focused on the formation of an integrated management system in the context of increasing the efficiency of agricultural production. The provisions of the scientific and theoretical (the target model of material resources management in agriculture, taking into account the interrelation of the subjects of the national economy, the evolution of the national economy and the conditions for making managerial decisions in terms of material resources; the general scheme of ordinal factors of state regulation of direct and indirect influence on the effectiveness of the use of material resources in agriculture) and methodological (improvement blocks the current practice of managing material resources in the context of ensuring the financial and investment stability of the agroindustrial complex) character.

An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture

In modern agriculture and environmental protection, effective identification of crop diseases and pests is very important for intelligent management systems and mobile computing application. However, the existing identification mainly relies on machine learning and deep learning networks to carry out coarse-grained classification of large-scale parameters and complex structure fitting, which lacks the ability in identifying fine-grained features and inherent correlation to mine pests. To solve existing problems, a fine-grained pest identification method based on a graph pyramid attention, convolutional neural network (GPA-Net) is proposed to promote agricultural production efficiency. Firstly, the CSP backbone network is constructed to obtain rich feature maps. Then, a cross-stage trilinear attention module is constructed to extract the abundant fine-grained features of discrimination portions of pest objects as much as possible. Moreover, a multilevel pyramid structure is designed to learn multiscale spatial features and graphic relations to enhance the ability to recognize pests and diseases. Finally, comparative experiments executed on the cassava leaf, AI Challenger, and IP102 pest datasets demonstrates that the proposed GPA-Net achieves better performance than existing models, with accuracy up to 99.0%, 97.0%, and 56.9%, respectively, which is more conducive to distinguish crop pests and diseases in applications for practical smart agriculture and environmental protection.

Environmental efficiency in greenhouse tomato production using soilless farming technology

The environmental efficiency of agricultural production and the relationship between management type and monetary value of achieving environmental efficiency are important parameters to assess the competitiveness of farms. The objectives of the study were to (i) estimate the environmental efficiency of greenhouse tomato farms, (ii) explore the determinants of the environmental efficiency level of greenhouse tomato farms, and (iii) calculate the monetary value of providing less greenhouse gas emission (GHG) and effective waste management in greenhouse tomato farms. The research data were obtained from 42 professional and 55 semi-professional greenhouse farms producing tomatoes using soilless farming technology in Turkey by using questionnaires. The environmental impact of tomato production was assessed using Life Cycle Assessment (LCA). The environmental efficiency was estimated by using Data Envelopment Analysis (DEA), and the Tobit model was used to elicit the determinants of environmental efficiency. The results showed that 1.72 kg CO2-equivalents of GHG were emitted for 1 kg fresh tomatoes from the cradle to the farm gate, and 1753.1 tons of organic waste were generated. The average environmental efficiency score of the greenhouse farms was 0.86, while the score was higher for professional farms and glass greenhouses. The results also revealed that the monetary value of GHG reduction and composting of all organic wastes in the greenhouse tomato farms were 1037.14 and 5700 US dollars, respectively. The values were higher for the professional greenhouse farms and glass greenhouses. The variables of education level of farmers, the size of glass greenhouses, net income, waste recycling investment, environmental knowledge level of farmers, and presence of green production practices positively affected the environmental efficiency. The findings suggest that awareness, extension, and training activities should be organized to reduce the farm-level negative impact of environmental inefficiency. Shifting from a static cost impact paradigm to a dynamic environmental management paradigm in greenhouse tomato farms may improve environmental efficiency, especially in semi-professional farms, and increase the competitive power of greenhouse farms.

Effect of Spatial Characteristics of Farmland Plots on Transfer Patterns in China: A Supply and Demand Perspective

(1) Background: The tense relationship between man and land makes transferring farmland rights in the market critical for improving agricultural production efficiency and promoting large-scale agricultural management. (2) Methods: This study considers the impact of the spatial characteristics of farmland plots on the economies of scale of farmers in terms of farmland use and heterogeneity. The effect of plots’ area and location on the directional flow of plots in the farmland transfer market from the perspective of matching supply and demand is also investigated. An empirical test is conducted on farmer actions and plot characteristics data based on surveys from 2015 and 2018 in the Chinese provinces of Heilongjiang, Henan, Zhejiang, and Sichuan. (3) Results: The plots’ area and location affect economies of scale for different potential transfer plots. This leads to large plots and adjacent plots in the market transferring to large-scale households, while scattered small plots mainly transfer to ordinary households. (4) Conclusions: The fixed spatial characteristics of the plots determine the scattered circulation of farmland in the transfer market, hindering the centralized utilization of farmland and restricting efficiency in farmland transfer market allocation. The findings from the context of China are similar to what has been found elsewhere. This suggests the need for a unified trading platform for farmland transfer and strengthening the mutual transformation of land and agricultural machinery.

The Influence of Agricultural Production Mechanization on Grain Production Capacity and Efficiency

As an important production factor of grain production, agricultural machinery can effectively provide a theoretical basis for agricultural modernization development strategies by exploring its impact on grain production capacity and efficiency. This research starts from the two aspects of grain production capacity and grain production efficiency, takes rice, wheat, and corn as the research objects, and uses the C–D production function and Tobit model as the basis, respectively, to establish two impact models of production capacity and production efficiency. At the same time, according to the different emphases of the two models, this research designs different variable systems and finally uses the data from 2017 to 2021 for empirical analysis. The research results show that the influence coefficients of machinery service income and machinery power resource input on the total grain production capacity are 0.0976 and 0.0437, respectively, with a significant positive impact. At the same time, for rice crops, wheat crops, and corn crops, the amount of mechanization cost per mu has a significant positive impact on the yield capacity of crops, with impact coefficients of 0.0311, 0.0827, and 0.0233, respectively. The supply level of agricultural machinery services and the utilization rate of agricultural machinery services per mu have a significant positive impact on grain production efficiency. The impact coefficients of the supply level of agricultural machinery services per mu are 0.0192, 0.0587, and 0.0241, respectively. The impact coefficients of the agricultural machinery service utilization rate are 0.0059, 0.0148, and 0.0607, respectively, with a significant positive impact. It can be seen that agricultural production mechanization can effectively promote the improvement in grain production capacity and efficiency and promote the process of agricultural modernization. At present, most of the research on industrial mechanization services is biased toward the choice of agricultural mechanization services by farmers. However, this research has carried out the impact mechanism analysis from the perspective of time and space and the perspective of crops, rationalizing the impact mechanism of agricultural production capacity and agricultural production efficiency under agricultural mechanization.

Land-Use Efficiency of Some Agricultural Livelihood Models on Low-Lying Land: A Study in Ha Tinh, Vietnam

Economic efficiency is a concept showing a concentration of development in depth, reflecting the exploitation of financial resources and the cost of resources in the production process. The study was conducted in Ha Tinh province, Vietnam, to evaluate the economic efficiency of agricultural production in low-lying areas. The research results of the topic are based on data collected from 80 agricultural households of 3 livelihood models (specialising fish, rice–fish, and rice–fish–duck). The results show that the specialising fish model gives the most extensive production value and requires the highest initial investment costs, followed by the rice–fish–duck model and the rice–fish model. However, considering the production efficiency indicators, the rice–fish–duck model has the highest economic efficiency, then the specialised fish model and the rice–fish model. The study also pointed out some limitations of livelihood models and farmer households, such as land size, technical system, and seed source. Therefore, if these shortcomings are solved, the economic efficiency of farmers will be improved.

Resilience theory and coerced resilience in agriculture

There is uncertainty regarding the resilience of modern intensive agricultural systems, given that they have been developed recently in history (post-WWII). Historically, agricultural sciences have focused on scales from the molecular and plant to plot and field. Innovation in this space has led to increased efficiency in agricultural production at local scales, but it remains a challenge to scale up such knowledge to explain dynamics in soil, water, nutrients, and biodiversity at larger spatial and temporal scales, or to account for complex interactions and feedbacks. We present agricultural systems as coerced regimes, and then discuss three underutilized resilience concepts relevant for investigations of agricultural systems as multi-scaled complex adaptive systems (CASs): scales, uncertainty, and regime shifts. Framing agricultural systems as coerced regimes situates them according to their degree of self-organization, the impact on resilience, and the possibility of alternative regimes. Collectively, these resilience concepts offer a path forward for evaluating the resilience of modern agriculture systems and expanding agroecological principles to scales beyond that of the field or farm because they explicitly account for agricultural systems as multi-scaled complex adaptive systems. This knowledge base has immediate management implications from the farm to continental scale, as climate and land use change force challenging decisions regarding the ability of modern agriculture to persist as is, adapt, or transform in order to avoid undesirable regime shifts.

A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management.

The persistent impact of drought stress on the resilience of summer maize.

Crop resilience refers to the adaptive ability of crops to resist drought at a certain level. Currently, most of the research focuses on the changes in root or photosynthesis traits of crops after drought and rehydration. Still, the persistence effect (drought period (T2) - rehydration period (T3) - harvest period (T4)) of drought stress on crops and quantitative estimation of resilience is still unclear. Field experiments were conducted in this study to determine the persistence effects on above-ground and below-ground growth indicators of summer maize at different levels and durations of drought. Next, an evaluation method for integrated resilience of summer maize was proposed, and a quantitative assessment of integrated resilience was made by Principal Component Analysis (PCA) and resilience index calculation. The results showed that the resilience of summer maize decreased with increasing drought levels, which persisted until harvest. Although summer maize resilience was strong after rewatering under light drought (DR1), declined after sustained rewatering. At the same time, production had decreased. However, a specific drought duration could improve the resilience of summer maize under light drought conditions. In particular, leaf biomass and root growth in the 30-50cm layer could be enhanced under long duration light drought (LDR1), thus improving summer maize resilience and yield. Thus, under water shortage conditions, a certain level and duration drought could improve the resilience and yield of summer maize, which would persist until harvest. Clarifying the persistent effects on the growth indicators of summer maize and quantitatively evaluating the resilience of summer maize could improve agricultural food production and water use efficiency.