Simulation of Crop Yields Grown under Agro-Photovoltaic Panels: A Case Study in Chonnam Province, South Korea

Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models in the decision support system for agricultural technology (DSSAT) 4.6 package. We reproduced yield data of paddy rice, barley, and soybean grown in AVP experimental fields in Bosung and Naju, Chonnam Province, South Korea, using CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system, developed using the crop models, was then applied to simulate the regional variations in crop yield according to solar radiation reduction scenarios. Simulated crop yields agreed with the corresponding measured crop yields with root mean squared errors of 0.29-ton ha−1 for paddy rice, 0.46-ton ha−1 for barley, and 0.31-ton ha−1 for soybean, showing no significant differences according to paired sample t-tests. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in crop yields due to the solar radiation reduction regimes. An additional advancement in the GCSM design could help prepare a sustainable adaption strategy and understand future food supply insecurity.

Developing decision support systems for crop yield forecasts

This chapter discusses existing yield forecasting systems in which the yield forecasts are driven by integration of different data sources, such as output of crop modeling, remote sensing and gridded climate datasets. It first provides overviews of the two predominant modeling approaches— crop simulation modeling and statistical modeling— to forecasting crop yield, with an emphasis on their respective use for operational crop yield forecasting systems. The chapter then briefly describes the accuracy and lead time of the existing yield forecasting models. Lastly, it provides a case study that integrates digital tools, field surveys, and crop modeling to provide on-time maize yield forecasts in small fields in Tanzania. The chapter concludes with a summary and future perspectives for research.

A glimpse on post-graduate thesis researches of Agronomy Department of IAAS and prioritized future research directions

To appraise the major research outputs of agronomic crops and cropping systems and to direct the future research priorities of Agronomy Department of post-graduate (PG) program of Institute of Agriculture and Animal Sciences (IAAS), a rigorous review was accomplished on about two decadal (2000-2018) student’s thesis research works. The review revealed that the agronomic researches at IAAS from 2000 to 2012 were concentrated mostly in on-station farm of Rampur, Chitwan and found their focus on 11 food grain crops with five major themes viz. varietal evaluation, crop management, soil nutrient and weeds management, and crop simulation modeling. With the shifting of IAAS PG program from Rampur to Kirtipur in 2013, the major agronomic researches were found to be concentrated in on-farm stations due to transitional movement of IAAS to Agriculture and Forestry University, Nepal. A total of 115 agronomic studies were conducted on various crops, of which 92 were on cereals, 8 on legumes, oilseed and minor cereals including potato. There were records of 10 studies on rice-wheat and 3 studies on maize-based systems. The huge gaps between the potential and farmers' field yield and between the potential and research station yields for rice, maize and wheat crops suggested a great scope to raise yields of cereals by improved agronomical researches on varieties evaluation, crop and nutrient management and weed management. Simulation modeling study predicted that the varieties of rice and maize adopted at present could sustain the yields only for recent few years and needed for introduction of new climate resilient varieties, then after. Innovative and new researches on eco-region suited on-farm trails with variety identification, improved crop husbandry and soil nutrient management, improved weed and water management and on agro-meteorology, conservation agriculture, climate change adaptation and crop simulation modeling are advised as future research frontiers to uplift the productivity and reduce yield gaps of major food crops and to strengthen the academics of post-graduate research in near future.

Forecasting phenology of mustard crop in North-western Himalayas

Field experiments were conducted during rabiseason of 2007-08 and 2008-09 to study the phenology, thermal indices and its subsequent effect on dry matter accumulation of mustard (Brassica juncea L.) varieties viz., RCC-4, Kranti and Varuna grown under varying environmental conditions of Himachal Pradesh. The early sown (10th October) crop varieties took maximum average growing degree days for flower initiation (492±1), 50% flower-ing (682±1), pod initiation (742±1), 90% pod formation (811±4) and maturity (1394±8) which decreased with subse-quent delay in sowing time and recorded lowest under late sown (9th November) crop. The accumulated helio-thermal units and photo-thermal units decreased from 9824 to 7467 oC day hour and 19074 to 15579 oC day hour, respectively. High heat-use efficiency was obtained under late sown condition on 30th October. The heat-use efficiency (HUE) was high at 90% pod formation stage as compared to other stages in all the varieties and sowing dates (except 9th November sowing). The early sown (10th October) crop had maximum calendar days and cumula-tive pan evaporation (158 days and 448.2 mm) followed by normal (20th and 30th October) (153 days and 434 mm) and late (9th November) (138 days and 403.1 mm) sown crop indicating higher water requirement under early sow-ing. The predictive regression models explained 83-85% variation in dry matter yield in three varieties of mustard. The agro climatic indices are important determinants for temperature, radiations and photoperiods behaviors of crop. The accurate predictions of crop phenology are useful inputs for crop simulation modeling and crop management, and used for climate change assessment and simulated adaptations in present scenarios.

In-Season Assessment of Rabi Crop Progression and Condition from Multi Source Data

Timely and accurate information on periodic crop progress and condition is essential to an agricultural country like India. The current study has been carried out with the objective of monitoring the timelines of the sowing of <i>rabi</i> crops viz. wheat and mustard, their progression and condition assessment using data from multiple sources viz. a set of indices derived from remote sensing, meteorological parameters and crop simulation modeling. The study area consists of six districts with significant wheat and mustard crops namely Patiala, Bhiwani, Agra, Bharatpur, Morena and Rohtas, and has been done for three years (2008&ndash;09, 2012&ndash;13 and 2013&ndash;14). The methodology consists of analysis of multi-temporal AWiFS and MODIS datasets of historical and current seasons for the period October to March covering <i>rabi</i> season. Crop simulation for wheat was carried out using DSSAT-CERES crop growth model for assessing crop growth. The results were discussed in terms of crop progression, start-of-the-season and crop condition. As a future scope of this study, thermal indices can be incorporated for further refinement and the same can be extended to larger areas.